Liberal Arts Political Science Political Theory Political Theory Study Guides
In the text below you will find study guides from Political Theory. The study guides cover topics such as Machiavelli, Locke, Hobbe's, Plato, Socrates, Aristotle, various topics pertaining to political science research methods, and much much more. The study guides will help you with any Political Theory college course.
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Survey of Political Theory Study Guides
1) According to Machiavelli, why is it necessary to read old books? How should we read them?
I think it is necessary, according to Machiavelli, to read old books because in those old books we can see some truth about the world. It is also necessary to learn the art of war “by mind.” Reading old books will allow you to learn the actions of great men and their causes and learn of their victories and losses. However, we must read them carefully since not everything in those books is true. Some things are misleading or downright false and thus we must read them fastidiously. When reading old books (histories) you must “read between the lines” to see how leaders really acted because books “sugar coat” a leaders qualities.
2) What does Locke mean by "prerogative" and why is it important? How would Machiavelli respond to Locke's argument about the need for prerogative?
Prerogative, according to Locke, is the power to act according to discretion, for the public good, without the prescription of the law, and sometimes even against it. Locke believes the lawmaking power is not always in being, and is usually too numerous, and so too slow, for the dispatch requisite to execution. Due to this it is impossible to foresee and to provide for all accidents and necessities that may concern the public. Also, it is impossible to make all laws that will do no harm. Because of this it is important for the executive power to have latitude to do things of choice that the law may not prescribe. Prerogative is the power to provide for the public good and is important because in unforeseeable cases or certain occurrences certain existing laws can not remedy the situation safely. Locke also believes it is important because it allows for the good of the people. Prerogative establishes government on its true foundation. I think Machiavelli would agree with Locke’s idea of prerogative. However, I do not think Machiavelli would agree with the application. Machiavelli would argue that the power of prerogative should be reserved to the executive for the use of prevailing over non-supporters of the government.
3) What lesson does Machiavelli mean to convey through his discussion of Cesare Borgia?
Machiavelli uses Cesare Borgia as an example of a virtuous prince. Borgia’s competence, as displayed by his efforts to secure his state quickly after he was put in power, exemplifies virtue. His placement of Messer Remiro de Orco, an extremely cruel and hated man, to restore peace and obedience in Romagna showed his ability to successfully use cruelty and force. Also, his slaying of Remiro de Orco showed his ability to avoid hatred, cleverly satisfy the people, and ultimately demonstrate his immense power. Even though Borgia eventually fell since he acquired his state through fortune, his ability to seize opportunities and vary his conduct accordingly proves as exemplary virtue nonetheless. The lesson Machiavelli is trying to convey is that a great prince rules by satisfying and terrifying his people. Cesare Borgia’s example should be noted and imitated because he showed the most you could do in ruling through others’ arms.
4) What is Hobbes's argument against the "fool" who says in his heart that there is no justice? How might Machiavelli reply to Hobbes?
Hobbes argument against the “fool” (who claims that covenant-keeping may turn out to be an irrational action) is three-fold. First, Hobbes says that if someone were to perform an act that was obviously not in their self-interest, but through luck that action lead to a good result, the performer is not to be considered wise or to have acted with reason. Hobbes does not believe this is reliable enough to dictate a course of action. Hobbes believes that because of their infrequency anyone who relies on them is illogical. Secondly, Hobbes presents a situation where an actor has broken a covenant and gotten away unharmed. Hobbes believes that word will spread of such acts and that others will refuse to enter into covenants with him, for fear that he will not fulfill his end of the bargain. The result being this actor’s inability to join forces with anyone will leave him isolated and undefended. The final part of the argument Hobbes indicates that if one were to get away with breaking their covenant with a positive reward and not have their reputation tarnished that in the end their actions will eventually have negative effects. Machiavelli would reply to Hobbes by saying a prince should do whatever is in the prince’s self-interest to preserve his power over the state. Whatever a prince does to preserve his rule and the security of his state can be considered just.
5) According to Hobbes, what is justice and where does it originate? Why must it originate in this way?
According to Hobbes, justice is the keeping of covenants. The origin of justice essentially comes from the third law of nature, which, in itself, is deduced from the first two laws of nature. The third law of nature states that it is not enough simply to make contracts, but that people are also obligated to keep the contracts they make. From this, injustice is defined as not performing your valid covenants. Justice is humanly constructed via the compliance to contracts and is not present in the state of nature. Justice, like all of Hobbes nineteen laws of nature, must originate in this way because it seeks to liberate man from the perilous grasp of the state of nature, escape war, and ultimately survive.
6) How does Locke justify his claim that we have a natural right to acquire as much private property as we can?
Locke originally stated the earth and everything on it belongs to all people and that all have the same right to make use of whatever they find and can use. The only exception to this rule is that each of us has an exclusive right to her/his own body and its actions. Applying these actions to natural objects by mixing our labor with them, Locke argued, provides a clear means for appropriating them as an extension of our own personal property. Since our bodies and their movements are our own, whenever we use our own effort to improve the natural world—the resulting products belong to us as well. This gives us a natural right to acquire as much private property as we can because we make useless property into useful property. This acquiring of private property through the use of labor improves the land. For example, a plowed field is worth more than a barren field. Locke argues that as long as there is enough land people have a natural right through the use of their labor to acquire as much as they can for the purpose of improvement.
Q1. How might Machiavelli respond to the judgment that he is a teacher of evil?
A1. Some of the advice to rulers found in The Prince—most famously, the defense of cruelty toward subjects—has led to criticism that Machiavelli is a “teacher of evil.” Moreover, the explicit separation of politics from ethics and metaphysics seems to indicate that there is no role for any kind of virtue in Machiavelli’s state—which can also lead critics to further believe that Machiavelli is a “teacher of evil.”
Machiavelli might respond to the judgment that he is a “teacher of evil” by stating that he never advocates cruelty or other vices for their own sake. He advocates them only in the interests of safeguarding the state, which, in his view, is a kind of ultimate good in its own right. Nor does he advocate that virtue should be shunned for its own sake. Indeed, Machiavelli states several times that when it is in the interests of the state, a prince must strive to act virtuously. But virtue should never take precedence over the state. Thus, generosity, which might be greatly admired by others, actually is detrimental to the future prosperity of the state. It is for this reason alone that a prince should avoid it—not because he must strive to be wicked or evil.
Machiavelli may also respond that instead of being a “teacher of evil” he is rather a founder of a new way of thinking—a “modern” political science that advocates the suspension of commonplace ethics in matters of politics. Moral values have no place in the sorts of decisions that political leaders must make, and it is a category error of the gravest sort to think otherwise. Machiavelli strives to determine effectual truth and lays out guidelines that best enable political leaders, such as princes, to determine effectual truth and ultimately have themselves and their states succeed—which, in itself, is the ultimate goal. Machiavelli believes that theory should serve practice—not practice should serve theory. To conclude, Machiavelli may state that he endeavors to give the most accurate and realistic account of human nature and the world as it is—whether they may be good or evil, and thus best prepare princes to be able to act accordingly and successfully.
Q2. What do the examples of Scipio and Hannibal tell us about writers prior to Machiavelli?
A2. The examples of Scipio and Hannibal tell us that the writers prior to Machiavelli distorted effectual truth. In terms of Hannibal, writers often admire his great actions, such as his ability to lead a large army of diverse men without any dissension, yet they condemn the principal cause of it—which was his inhuman cruelty. According to Machiavelli, without inhuman cruelty Hannibal’s other virtues would not have been enough to successfully lead such diverse men into battle. Hannibal’s cruelty enabled him to be feared, prevent dissension within his army, and thus be militarily successful. However, Machiavelli argues that writers are inconsiderate of the positive effect of Hannibal’s cruelty and distort and mitigate its impact via condemnation. In terms of Scipio, writers incorrectly attribute his glory to his excessive mercy. According to Machiavelli, Scipio’s excessive mercy was actually detrimental to his fame. For example, Scipio’s excessive mercy, such as allowing his soldiers more license than is fitting for military discipline, resulted in his armies in Spain to rebel against him. Also, his excessive mercy resulted in Senator Fabius Maximus calling him the corruptor of the Roman military. Such mercy proved malevolent to Scipio’s glory and perhaps would have sullied his fame if not hidden and made for his glory. Mercy was greatly detrimental to Scipio, but writers prior to Machiavelli make like it actually was the reason why he was successful.
Q3. What, according to Hobbes, is justice? What is its origin? What makes it obligatory?
A3. According to Hobbes, justice is the keeping of covenants. The origin of justice essentially comes from the third law of nature, which, in itself, is deduced from the first two laws of nature. The third law of nature states that it is not enough simply to make contracts, but that people are also obligated to keep the contracts they make. From this, injustice is defined as not performing your valid covenants. Justice is humanly constructed via the compliance to contracts and is not present in the state of nature. Justice, like all of Hobbes nineteen laws of nature, seeks to liberate man from the perilous grasp of the state of nature, escape war, and ultimately survive.
Q4. Explain, with examples from the text, Machiavelli's conception of virtue.
A4. Machiavelli’s conception of virtue is a range of personal qualities that the prince will find it necessary to acquire in order to “maintain his state” and to “achieve great things,” the two standard markers of power for him. Machiavelli expects princes of the highest virtue to be capable, as the situation requires, of behaving in a completely evil fashion. For the circumstances of political rule are such that moral viciousness can never be excluded from the realm of possible actions in which the prince may have to engage. Machiavelli's sense of what it is to be a person of virtue can thus be summarized by his recommendation that the prince above all else must acquire a “flexible disposition.” That ruler is best suited for office, on Machiavelli's account, who is capable of varying her/his conduct from good to evil and back again “as fortune and circumstances dictate”
He argues that a prince should always try to appear virtuous, but that acting virtuously for virtue’s sake can prove detrimental to the principality. A prince should not necessarily avoid vices such as cruelty or dishonesty if employing them will benefit the state. Cruelty and other vices should not be pursued for their own sake, just as virtue should not be pursued for its own sake: virtues and vices should be conceived as means to an end. Every action the prince takes must be considered in light of its effect on the state, not in terms of its intrinsic moral value.
Machiavelli uses Cesare Borgia as an example of a virtuous prince. Borgia’s great prowess, as displayed by his efforts to secure his state quickly after he was put in power, exemplifies virtue. His placement of Messer Remiro de Orco, an extremely cruel and hated man, to restore peace and obedience in Romagna showed his ability to successfully use cruelty and force. Also, his slaying of Remiro de Orco showed his ability to avoid hatred, cleverly satisfy the people, and ultimately demonstrate his immense power. Even though Borgia eventually fell since he acquired his state through fortune, his ability to seize opportunities and vary his conduct accordingly proves as exemplary virtue nonetheless.
Q5. According to Hobbes, what is the difference between a "right of nature" and a "law of nature?"
A5. According to Hobbes, there is a significant difference between a “right of nature” and a “law of nature.” Essentially the difference between them lies in Hobbes’s explanations of each. On the one hand, Hobbes’s “right of nature” is the liberty of an individual to do whatever they need to do to stay alive. People are free to defend themselves and do whatever seems to preserve their life. On the other hand, Hobbes’s “law of nature” is a limitation from reason that restrains the rights of nature to be able to have peace and coexist. The laws of nature affirm human self-preservation, condemn acts destructive to human life, and state that human beings must strive for peace. To conclude, the essential difference between a “right of nature” and a “law of nature” is that a “law of nature” places limits on the “right of nature” in order for humanity to escape the horrors of the State of Nature.
Q6. Compare Locke's account of the State of Nature with Hobbes's.
A6. Locke’s account of the State of Nature and Hobbes’s account of the State of Nature have both numerous similarities and differences.
In terms of similarities, Locke explains the State of Nature as a state of equality in which no one
has power over another, and all are free to do as they please. Hobbes’s account of the State of Nature is similar since he also asserts that all men by nature are equal. Also, both Hobbes and Locke and assert that individuals have a duty to preserve himself/herself.
However, some differences between Locke’s and Hobbes’s account of the state of nature also exist. For instance, Locke asserts that the liberty of people to do as they please does not equal license to abuse others, and that universal natural law, which can be executed by any individual, exist in the state of nature. Also, Locke states that if people are safe, they have a duty preserve others. This is quite different from Hobbes who argues that the State of Nature has no natural laws and is a “war of all against all,” in which human beings constantly seek to destroy each other in an incessant pursuit for power. While in latter chapters Hobbes stresses that people in the state of nature do see the need to cooperate, they are still selfish and do not necessarily attempt to save other people. To Hobbes, life in the state of nature is essentially “nasty, brutish and short.”
Initially upon reading Locke, one might believe that he holds more of an optimistic view of the State of Nature. However, his view on the State of Nature can be just as bleak as Hobbes’s. Locke asserts that the State of Nature is full of inconveniences, is seldom anything but miserable chaos, and has no known law, no fair judge, and no reliable execution of the law. The State of Nature only really lasts for a few minutes until anyone disagrees and then, like Hobbes, the State of Nature is just a State of War. Both Locke and Hobbes have unique views on the State of Nature, but, despite some differences, they are remarkably similar.
Q1. Compare and contrast Plato’s use of the army in the Republic with Machiavelli’s account of the proper role of an army in a principality.
A1. Plato’s use of the army in the Republic and Machiavelli’s account of the proper role of an army in a principality have both similarities and differences. In the following, I will first define both Plato’s and Machiavelli’s use/role of an army and then will compare and contrast their similarities and differences.
Plato’s use of the army in the Republic essentially comes from the specific roles of two of the three classes that comprise a just society and make up the actual army—which are guardians and auxiliaries. The guardians, who are chosen from the ranks of the auxiliaries and have gold in their soul (according to the Myth of Metals), are responsible for ruling the city. They govern the city, have no families, and belong to/manage a system of eugenics (selective breeding among guardians). The auxiliaries, who are warriors and have silver in their soul, are responsible for defending the city from invaders and for keeping peace within the city. The auxiliaries must enforce the convictions of the guardians and ensure the obedience of the third class of society, which has a soul of bronze/iron and is known as the producers (craftsmen, farmers, or anyone else that is not a ruler or warrior).
Machiavelli’s account of the proper role of an army in a principality is to aid a prince in acquiring, securing, and defending a principality. In terms of acquiring a principality, a prince can use an army (either his own army, which is most preferable since it is from virtue; or an army of another, which is not as preferable since it is through fortune) to acquire a principality or additional principalities. In terms of securing a principality, an army should provide security for the principality of a prince. A good army, according to Machiavelli, is one of the two essential components of a strong state (the other is good laws). Since force is an inseparable part of governing a principality, good laws cannot exist without a good army, which provides such force. A good army indicates the presence of good laws, aids a prince in effectively governing, and thus provides security for the princes’ principality. In terms of defending a principality, a proper army will give a prince the means and ability to defend against foreign enemies and make good allies (since, according to Machiavelli, a strong army always leads to good allies).
In comparison and contrast, Plato’s use of the army in the Republic is to basically rule, govern, and control the people of the city. Machiavelli’s account of the proper role of an army is to serve as a tool for a prince in acquiring, securing, and defending his principality. Similarly speaking, both armies defend their city/principality from foreign invaders and keep peace within the city/principality. Also, Plato’s army in the Republic is comprised of native people who live in the city, which, according to Machiavelli, would be most a most effective, loyal, and desirable army for a prince. In terms of differences, Plato’s army rules and governs the city/state as somewhat of an oligarchy; and does so with immense power, even to the point of selectively breeding the guardian population; whereas Machiavelli’s army is ruled/controlled by the prince and only has powers in which the prince gives it. While Plato does mention political rule by a “philosopher-king,” there actually can be more than one, and they are essentially the guardians. Also, Machiavelli’s army can be used to acquire additional principalities, whereas Plato’s army in the Republic is not used for such purposes. Plato’s army can be used to in order to incite internal dissension (among classes, etc.) in other states by planting conspiracies, but it can only do this to safeguard/preserve the republic and not take over other states.
Q2. How would Machiavelli evaluate Locke’s account of the responsibilities and powers of the Executive in a political society?
A2. Machiavelli would evaluate Locke’s account of the responsibilities and powers of the Executive in a political society as very much similar to the responsibilities and powers of a Machiavellian prince. The powers described in Locke’s Executive, such as the power to enforce laws, punish offenders, and protect/defend the property and liberties of the people, demonstrate the significant authority of the Executive. From seeing such significant Executive powers, as well as the ability of the Executive to continuously enforce/possess such powers (it is always active), Machiavelli would state that Locke picked up where he left off, making his powerful executive (prince) compatible with a mixed government by balancing it with legislative powers. Machiavelli would notice that even within a mixed/liberal government, with its circumscribed ability to act, Locke’s Executive still remains powerful since he endows it with “prerogative,” the ability to act without law—even against the law—when the public good demands it. This executive prerogative, very much like the main goal of a Machiavellian prince, purports to preserve the state. Thus, Machiavelli would classify Locke’s executive as a Machiavellian executive—slightly mitigated, liberalized, and transformed, but still recognizable and with responsibilities and powers that are very much significant, intact, and present.
Q3. Contrast Locke and Plato on the role of property in a good political community. Why do they differ?
A3. Locke’s role of property in a good political community is to develop and create value out of the empty wasteland that is the world (given by God). People in a good political community have the right to property because they have the prior right to self-preservation, which is derived from the duty to preserve mankind. People develop the land through labor which then the land becomes their property (since they put their own property, labor, into it). With the advent of money, property can increase the economic well-being of the community as a whole since people have the incentive to develop their property (such as land) to its fullest ability and acquire things. Property, along with a form of currency, produces the most out of the land, eliminates potential spoilage, and results in trade/barter. The more people develop their land the more money they will receive (i.e., trade food for money), and the higher development of the land makes the world a more valuable place. Property holds a very beneficial role in a good politcal community, such as resulting in people coming together to barter, resulting in people being rational and industrious, and ultimately creating value out of the world.
On the contrary, Plato’s role of property in a good political community is not as positive, beneficial, or impacting as Locke’s. For example, the guardians, who are the rulers of the city, receive no wages and are not allowed to even have property. If rulers are permitted to acquire private property, they will inevitably abuse their power and begin to rule for their own gain, rather than the good of the entire city. Thus, property, as well as money, is very much a root of evil. This view that the possession of property will yield detrimental effects to the city is essentially why Plato’s role of property in a political community differs from Locke’s. Locke essentially believes money is a great human invention, which should be used to acquire property, whereas Plato believes both money and property can be a cause of evil, and, in the case of guardians, should be avoided. Producers can have property, but the guardians; who rule, govern and control the producers and the city; do not. Thus, Locke’s political community is very much defined by liberal free trade of property whereas Plato’s political community (not necessarily what he advocates, just the one he, through Socrates, describes) is communistic in nature.
Q4. Machiavelli criticizes other writers for concerning themselves with “imagined principalities.” What is the basis of his criticism? How would Plato respond?
A4. The basis of Machiavelli’s criticism is that such “imagined principalities” do not exist nor have ever been seen or known to exist in truth, particularly effectual truth, and do not exist in the real world. These “imagined principalities” are built upon an idealized notion of how men should live rather than how men actually live. Machiavelli states that truth strays far from the expectations of imagined ideals, and men never live every part of their life in accordance with ethics. There is so much difference between the way people should act and the way they do act that any man who tries to do what he should will ruin himself. Thus, as Machiavelli states, “it is necessary to a prince, if he wants to maintain himself [and his state], to learn to be able not to be good, and to use this and not use it according to necessity.” Such “imagined principalities” do not comply with this reality (having to be able not to be good), and thus are not reality, not useful, and are mere imagination.
Plato would respond by stating that the “imagined principality” in the Republic, created by Socrates and discussed with the other characters, was imagined in order to discover what justice is and does for an individual. The “imagined principality” or city in the Republic is a city in speech—not in an actual physical form, and was put in place in order to find out what justice (and injustice) does in the city, as well as to see how important (and/or beneficial) justice is for the city. By discovering what justice does for the city, it then can be observed what justice does for the individual. The city is a way of answering what justice is to us, whether or not it does good for an individual, and whether justice should be taken seriously or be regarded as a burden. Thus, Plato would state that Machiavelli’s critique is somewhat irrelevant and/or incompatible in regards to the city in the Republic since it overlooks the actual point of the city, which was not to form an actual everlasting state, but rather to discover if a just life is good for an individual.
Plato would somewhat agree with Machiavelli on the basis that an “imagined principality” like the one in the Republic; with its state-led eugenics (among guardians), absence of families (among guardians), and total obedience of its citizens to the state; would be rather difficult, if even possible at all, to exist in the real world. Socrates, as well as other characters in the Republic, actually states that such a city would be nearly impossible to institute and preserve.
Nonetheless, Plato would conclude that Machiavelli’s critique of “imagined principalities” is somewhat besides the point/irrelevant in regards to the “imagined principality” in the Republic since the purpose of creating such an “imagined principality” was to discover if a just life/justice is good for an individual, and not, like the intent of Machiavelli’s advice in The Prince, to create an actual most everlasting state.
Q5. Compare what Machiavelli and Socrates say about the status of a wise human being in political society.
A5. In comparison of what Machiavelli and Socrates says about the status of a wise human being in political society, being wise is a most beneficial quality that ultimately leads a person to high status, ranking, ability, and power. Machiavelli states that a wise human being can positively influence/contribute to a political society, such as a wise minister/adviser to a prince, and can also be successfully at the top of political society, such as a wise prince/ruler. Socrates states that a wise human being ultimately rules and manages society. The wise human beings are the guardians in the city and rule, control and govern the people, such as the producers (commoners). The guardians also belong to/manage a system of eugenics (selective breeding among guardians). Thus, for both Machiavelli and Socrates, being wise essentially translates into a person having high status in political society.
Ancient Political Theory Study Guides
1. In Aristophanes’ Clouds, what is Socrates’ relationship to rhetoric? To the “Unjust Speech”?
Socrates relationship to rhetoric is that of someone explaining the old and new education. He attempts to make evident how justice should be rooted in nature. He shows the danger of the new education, rhetoric. However, he also shows the flaw of old education. This new education is rooted in popular speech which claims to be wise and has the freedom to enjoy great pleasure. He is able to teach how the weak (unjust) speech can defeat the stronger (just) speech. In the Clouds Socrates is known as a sophist and is meant to represent all that is wrong with sophistry. The unjust speech is an immoral, immodest, and indulgent form of speech rooted in the new education. It defeats just speech through persuasion and rhetoric. This is accomplished through the utter frustration of just speech. Through the actions of Pheidippides, who was taught the unjust speech by Socrates, the danger of this new education is exposed. The danger of new education is an immoral, non-virtuous way of life that is not aimed towards the good or betterment of people. The old education rooted in just speech is that of moderation, courage, shame, respect of ones parents, tradition, wisdom, and honor and will lead to a happy life. However, the old education is not without flaw. The flaw or weakness is that the old education cannot defend how marriage, family, moderation, and justice have grounding in nature. Furthermore, just speech realizes that it will always lose if what is just is not popular. In the end, what is virtuous or to the betterment of people is not always what is popular and popularity is the drawing attribute of unjust speech.
2. Based on your reading of the Clouds, discuss Aristophanes’ most serious critisms of Socrates or Socratic philosophy.
Aristophanes’ most serious criticisms of Socrates are three-fold. The first is that Socrates is ignorant of fundamental requirements of political life. This becomes evident with his attack on the foundations of the family; prohibition of incest and father-beating. Socrates is dependent on the city for his students and his food, but speaks as if he has no dependence. Second, Socrates is portrayed as a man ignorant of the human soul. Socrates overestimates the ability of men to think rationally. Socrates also forgets the power of human love, particularly that of wife and children. Because of these misjudgments, Socrates never grasps men’s need for gods. Finally, Socrates is ignorant of the truth about gods. He believes the clouds to be an imitation of all things that exists and that they are patrons of all those who are talkers. As imitators of all things and lovers of speech, the clouds point to nature which implies correct speech. However, the clouds are proponents of all speech whether true or false.
3. According to Socrates’ presentation in the Gorgias as a whole, what is rhetoric? What can it accomplish? What are its limits?
Rhetoric is persuasive speech made to a public audience without a base in knowledge. Rhetoric can accomplish the answering of any question asked. The limitation of rhetoric is that it is only powerful among non-experts. However, Socrates aims to develop the concept of a noble rhetoric. This form of rhetoric can be developed to help justice. This would be rhetoric that is not based on flattery, but based on art. The difference is that rhetoric based on flattery is aimed at pleasure while rhetoric based on an art is aimed at the good. The limitation of this type of rhetoric is that it is likely to be unpopular because it aims for the good for people rather than pleasure for people. This type of rhetoric promotes virtue leading to a healthy body and soul. This is the essence of a good life and for the better in the long term.
4. How does Socrates answer Callicles’ long speech criticizing the rule of law and advocating that the strong rule in order to gratify their own desires?
Socrates states that if the wise were to rule they would not take too much because they would have no use for it; they would be moderate. He argues that harm can come to a person who indulges in every appetite. The body is a tomb, which is a reference to the soul, is to imply that to live through the body is to live through external things which is not living. Socrates then argues with his analogy of the leaky jar that the moderate person is always full and the immoderate person is a slave to achieving fulfillment which is never realized.
5. According to Socrates’ conversation with Callicles, what is the difference between pleasure and the good?
The good facilitates a strong and healthy soul while pleasure facilitates a sickly and weak soul. In these two concepts lies the two types of rhetoric; rhetoric based on experience and/or skill and rhetoric based on art. Rhetoric based on experience/skill is aimed at pleasure. This is an appeal to popular sentiment. Rhetoric based on art is aimed at the good. This is an aim at what is best regardless of popularity.
6. What does Socrates mean when he says that he practices the “political art?” What exactly would such an art accomplish? When and where does Socrates seem to practice it?
When Socrates says he practices the political art he means that he tries to make people better. He accomplishes this by instituting an extensive examination of what people care about then he exposes why they don’t understand what it is they care about or how they contradict themselves and then send them off to think about it. This would accomplish a political body that reflects on his/her work, does not act randomly, and governs in an orderly manner. In essence, this art would make political people better. Socrates practices this art everywhere. An example can be recounted in The Apology when Socrates’ actions as a gadfly are explained.
7. What are Socrates’ chief criticisms of Athenian democracy? Does he envision or imply that there could be a better form of democratic rule? Explain.
Socrates chief criticisms of Athenian democracy include the fact that successful politicians always imitate the powerful in society. In becoming like the powerful they also become as corrupt as the powerful. In this case they are no longer leading, but rather they are following. Another criticism is the use of rhetoric. Rhetoric is an immoral practice which has the power to persuade without knowledge. This threatens political communities because the powerful are without knowledge. Socrates explains that a noble rhetoric could be employed. This noble rhetoric would be based on the political art which makes souls better. This would be a way of talking to people for the purpose of making them and society better. You cannot pretend to value the majority thought of the powerful without eventually adopting these values. In doing so, you lose yourself in a radical way. They way to overcome this is through noble rhetoric. In this way you appeal to the people without gratifying the people.
1. What arguments does Aristotle offer in favor of kingship?
In cases where there is an outstanding group or individual, aristocracy or kingship might be preferable; an idealized form of monarchic government in which the king is an exceptional individual who governs with everyone's best interests in mind. Aristotle acknowledges that finding such an outstanding leader is difficult, but prizes the possibility nonetheless. In those rare cases in which one individual clearly outstrips the rest, it may be just to grant that individual absolute kingship. A king is more adaptable than laws to particular circumstances. In Aristotle's opinion, then, a sovereign law should confer benefits according to each person's contribution to the city, and deliberative and judicial assemblies that are made up of all citizens should rule in cases where the law is ambiguous. However, the question remains how we should determine who makes the best contribution to the city. If the goal of the city is to ensure the good life for its citizens, it is far from clear how we could fix an objective standard to determine who contributes most to this goal. Aristotle's solution is that, since all citizens take part in deliberative and judicial office, all citizens contribute equally. This solution is trumped in the case of outstanding individuals who clearly make a far more significant contribution than their peers. In Aristotle's opinion, it would be unjust to place such an individual on an equal level as his peers, since he is making an unequal contribution. Though Aristotle is reluctant to endorse kingship for a number of reasons, he ultimately concludes that in some cases it may be the best solution.
2. In Book III, Aristotle asks whether it is better to be ruled by law or by the best individual or individuals. How does he resolve this question?
Aristotle's suggestion that a citizen is someone who shares in the deliberative or judicial offices of a city may seem odd to the modern reader, as very few people in the twentieth century would count as citizens by this definition. In the polis, on the other hand, involvement in the affairs of the city defined one's identity to a large extent. Though there were certain leaders concerned exclusively with the government of the city, all citizens were required to contribute in some way. Assemblies of citizens made decisions in bodies whose modern equivalents are law courts and city councils, and these assemblies would rotate membership so that every citizen served a specific term. The only aspect of this system that remains in modern times is jury duty. Aristotle will ultimately argue that just government works best when the masses are allowed to participate.
3. In Book IV, what does Aristotle mean by a “polity?” On what ground does he recommend it?
Aristotle considers constitutional government, in which the masses are granted citizenship and govern with everyone's interest in mind, one of the best forms of government. It combines elements of oligarchy and democracy, finding a compromise between the demands of both the rich and the poor. Politeia, or constitutional government, is a mixture of oligarchy and democracy that confers benefits both on the masses and on the wealthy, but it does not discriminate on the basis of merit. In a healthy constitutional government, it is essential that everyone in the city be content with the constitution. Here constitutional government is portrayed as a middle ground, giving favor to both rich and poor, between the corrupt alternatives of democracy and oligarchy.
4. In the Gorgias, Socrates says that if someone does injustice, it is better for that person to suffer the penalty than to escape. Why does Socrates make this argument? What reasoning supports his claim?
Socrates also points out that one who receives punishment for a wrong "suffers justly" by paying the just penalty. This fact in turn prompts him to avow that one who is justly punished suffers the good and is thereby liberated from the high evil of the soul. One who inflicts wrong and receives proper punishment therefore liberates his soul from evil in a way that another who inflicts wrong and escapes punishment cannot. Consequently, it is worse to commit a wrongful act and escape punishment than to commit wrong and be punished. The topic gains even more significance in that it comes as the first hint of some overarching sense of right and wrong within the dialogue. Put differently, by discussing justice of the soul as the highest state of human existence, Plato effectively arrives at a notion of abstract and hierarchical virtue through the vehicle of more particular considerations. The pattern thus takes shape for a consideration of virtue in general by way of an inquiry into particular instances of right and wrong, good and bad, as well as the resulting implications. This model applies to Gorgias as well as to Plato's entire extended philosophical project.
5. What is the purpose of the myth at the end of the Gorgias? Does it have structure? To what sort of people does it appeal? Does it appeal to more than one kind of person?
This final section focuses even more intensely on integrating into an organic whole the various aspects of proper living defined earlier in the text. New emphasis is placed on the happiness, courage and piety that automatically accompany justice and temperance, for example. Value also is added to the worth of the soul, an aspect of humans that already attains great importance as the seat of justice and temperance. Moreover, justice and temperance are themselves the two most important components of a good life. The account serves to highlight the fundamental importance of virtue to human existence. So important is this composite art of proper living, in fact, that the degree to which one attains it determines the nature of that being's existence throughout eternity. In light of this virtually unimaginable significance, it should come as no surprise that virtue—the good life—is the overarching theme of this particular dialogue, as well as a major strain of Plato's life-long philosophical investigation. By telling this myth, Socrates is attempting to do all that he can for his fellow man. This is the last resort to make them see that living a just life aimed at a pure soul is the best life.
6. In the Apology, Socrates describes his conversations with others in different ways. For example, he says that he spoke differently to others in trying to refute the Delphic Oracle than he did when he compared himself to Achilles and called himself a gadfly and spoke to his fellow Athenians. Do Socrates’ conversations in the Gorgias fit either of the models of conversations outlined in the Apology? If so, explain how. If not, say why not.
The conversations are different in the sense of tone. In the Apology, Socrates is forceful. He is more focused on exposing people’s flaws and then showing the people that they don’t even know why they are flawed. In Gorgias, he is more subtle. He is attempting to convince a person that living with a pure soul is the best life by using reason. These conversations are the same, in the sense that Socrates is trying to help people to see the benefits of knowing thy self and living a just life. However, in the Gorgias he has learned he can only help so much… he can only force feed this way of life so much… the people need to realize this as the best way of life for themselves.
7. What is the importance of music in Aristotle’s best regime?
In terms of education, Aristotle recommends a program of reading and writing, drawing, physical training, and music. This education should be directed toward the end of achieving a life of good quality, and should encourage life skills, moral goodness, and cultivation of the mind. Determining the value of music is trickier, but Aristotle suggests that it helps promote the proper use of leisure. In doing so, he distinguishes between work, play and relaxation, and leisure. Play and relaxation are forms of relief from hard work. Leisure is more than just relief; it is the medium in which happiness and a life of good quality can be pursued. If leisure consisted simply in play and relaxation, then a life of good quality—the end goal for which man strives—would be nothing more than play and relaxation. While music is not useful and does not promote courage, it helps man make use of his leisure. Similarly, the practical tools of reading, writing, and drawing can have application beyond their usefulness, and they can also widen man's knowledge and teach him to appreciate form and beauty. Aristotle returns to the question of music's place in education. He offers three possible arguments for the use of music: (1) amusement and relaxation; (2) improvement of moral character; and (3) cultivation of the mind. Aristotle suggests that one learns a deeper and subtler appreciation of music by understanding what goes into its performance. However, education in music should not be taken beyond the point of learning an appreciation of rhythm and harmony: if students dedicate themselves to being skilled performers, they will be studying only to please others. For that reason, Aristotle suggests that students not learn the flute or harp, or, for that matter, any instrument requiring a great deal of skill. Aristotle believes that music can serve moral purposes because it can, quite literally, "represent" states of character just as paintings can represent trees and houses. By representing a virtuous character, music can serve as a very powerful tool for moral instruction.
8. According to Aristotle, what is the difference between a city and a household? Between a city and a regime?
CITY - Aristotle's claim is not that cities must exist to serve the ends of individuals. Rather, he claims that individuals are to a large extent defined by the cities they live in and that man can be fully human (i.e. fully rational) only by participating in the city. The city is a complete whole and each individual is a mere part. The city is thus more important than the individual. The different kinds of associations that exist are founded on different kinds of relationships.
HOUSEHOLD - The basic unit of association is the household, the next is the village, and the ultimate association is the city, toward which end humans, seeking to attain the highest quality of life, naturally move. Aristotle concludes, "man is by nature a political animal." Only as part of a city can people fully realize their nature; separated from the city, they are worse than animals. This securing of food, shelter, and other necessities is called natural acquisition because it is an indispensable part of the management of a household.
REGIME - Aristotle's solution is to require, first of all, that the governing body include all citizens and that they govern in the common interest; and second, that the laws be well constituted and directed toward the general good. The compromise between the life of political action and the life of speculative philosophy is one of the central tensions of the Politics. Aristotle's remarks that all citizens should know one another and that the population be "surveyable" reinforce the intimate nature of the polis and the fact that the political life is necessarily social. The contemplative life, on the other hand, requires a great deal of solitude. Though citizens must engage in political life in order to govern the city, Aristotle ultimately concludes that political life is merely a means to the end of philosophical speculation, as it helps maintain the conditions that make the speculative life possible.
Research Methods In Political Science Study Guides
Lecture 1
Scientific Inquiry
I. The Nature of Scientific Inquiry
The Nature of Scientific Inquiry
• “Chaos is nature’s reality, order is man’s dream.”
• Curiosity and necessity motivate human inquiry.
n Nature of what science is all about
n Human innate desire to learn about their world driven by necessity or curiosity.
n End Point – understand world in which we live or improve it.
The Nature of Scientific Inquiry
• How do we know?
???? A question of method.
???? The primary interest of the course.
• How should we use what we know? (Application off scientific knowledge to use what we know)
???? A question of ethics or preference.
???? Addressed in part two.
NON-SCIENTIFIC INQUIRY
How Do We Learn (about the world)?
• Myth – create a shared set of beliefs (ex. G. Washington chopping down the cherry tree)
• Tradition – Shared knowledge developed from experience. Body of knowledge accepted by the community is not always correct (ex. Slavery)
• Authority – Accept knowledge from others (ex. Professor)
• Rationality – Use own logical reasoning to deduce truths about the world.
• Intuition – A gut feeling. Initial hunches may be incorrect.
• Personal experience
Problems with Non-Scientific Knowledge (with previous observations in learning about the world)
• Inaccurate observations
• Overgeneralization
• Selective observations
• Illogical reasoning
• Subjective or Normatively Based – Our own values may bias our inquires.
SCIENTIFIC INQUIRY
The Essence of Scientific Inquiry
1) • Causality
???? Future circumstances are caused
by present conditions.
- Use scientific knowledge for prediction and understanding.
2) • Probabilistic Reasoning
???? The effect occurs more often when the causes occur than when they do not.
Characteristics of Scientific Knowledge
• Explicit – All rules for defining and examining are predetermined before the process begins.
• Systematic – Each item of evidence is linked by reason or observation by all other items of evidence.
• Controlled – Observed in as rigorous manner as possible
• Empirical – Seeks to explain how the world works as opposed to how it should work.
• Objective – Our own values do not bias our inquires.
Outputs of Social Scientific Knowledge
• Generalizable – Explain a broad class of phenomenon. A general explanation that can account for all similar types of cases.
• Predictive – Explains what has happened and what to expect in the future.
• Provisional – All scientific research is provisionally accepted until it is either disproved or improved by other researchers.
• (THEORY BUILDING IS…) Iterative – No single piece of research is accepted as the final word. Truth is accepted only after a body of evidence develops over time.
Scientific Research Methods
• (Definition of Scientific knowledge) -A process of testing theories and hypotheses by applying rules of analysis to the observation and interpretation of reality.
Scientific Inquiry
II. The Methodology of Social Scientific Research
STEP 1: Defining the Research Question
• All research starts with a question.
• Research questions should be narrowly defined. (easier to answer in a satisfactory manner)
• Often we address part of a much larger question.
• Many times we re-visit questions examined by prior research.
STEP 2: Developing a Theory
• Theories are proposed answers to our research questions.
• Theories are logical arguments that explain empirical relationships.
• Theories are not fixed, but evolve (over time).
• Theories seek to have explanatory and predictive power.
Types of Theories
•Normative theory seeks to explain how the world should work.
• Empirical theory seeks to explain how the world does work.
•Normative theory can inform our research, but the research must be empirical.
• The division between normative and empirical theorists is a point of contention.
Establishing Causality
• Theories explain how the independent variable causes the dependent variable to change.
• A bivariate theory posits a single iv affects the dv.
• A multivariate theory posits multiple ivs all partially affecting the dv.
Deducing Hypotheses (what we expect to find)
• Theories are not tested directly
• Instead we test hypotheses deduced from our theory.
• Hypotheses captures the essence of our causal argument in a single sentence.
Operationalization (From theory building to analysis)
• Operationalization is the process of moving from theory to analysis
???? Data collection (existing or collect)
???? Development of measures for variables in our theory (measurement – turn raw data into indicators that closely capture the theoretical concept of interest. (*Measurement is a crucial step in this process)
???? Application of either quantitative or qualitative methods
- Quantitative – thorough accounting of 1 or 2 cases but no generalibility.
- Qualitative – No depth, but generalibility.
Inference and Generalization
• The conclusions that we draw directly from our analysis are called inferences.
• Generalization assesses the larger implications of our research. (less context specific, but more general)
Scientific Inquiry
III. Limitations
Limitations
• Human beings – may defy systemic theorizing (idiosyncratic)
• Measurement – Some things we are interested in empirically validating may be difficult to measure.
• Observable – May not have access to all possible observations or actual decision making. (ex. Cannot enter the Oval Office)
• Small numbers of observations (n) – Difficult to study a small number of cases and develop and test theories that are generalizable.
• Subjectivity – In studying things you care about objectivity may be difficult to maintain in research.
Black Box Theories
Input
Input Output
Input
* The box represents an educated guess on output (but we do not know what is in the box).
EXAMPLE:
Public Opinion
Legislative Priority Veto or sign Bill
Partisan Control
of Congress
Lecture 2:
Overview of Political Science
Overview of Political Science
I. The Development of Political
Science
Goals of Political Science
• The systematic and empirical study of political phenomena.
???? This has not always been the case.
???? Differences of opinion exist about what political scientists should accomplish.
1) The introduction of scientific methodology is a harmful illusion that trivializes basic truths about politics.
2) Critical Theory Approach – Political science is inseparable from society in general so we cannot simply look at political science so we have to take a broader look at what political science is.
3) (Dr. Damore’s belief) – You can study politics scientifically. Political science is about the systematic and empirical study of political phenomena.
The Evolution of Political Science
• As old as humankind (Some people argue: Plato’s Republic is the 1st political science book.)
• Towards a more empirical political
Science (Alexis de Tocqueville: observing democracy 100 years after it was established in the U.S. à Book “Democracy In America” à His research lacked a research method; it was rich in description, but no attention to political behavior.)
• The professionalization of political
Science (Modern era of political science 1) University of Chicago – In the 1920s and 1940s focused on organized empirical research program à emphasis on barrowing from psychology and sociology to study political phenomena. First movement towards quantification. à After WWII, behavioral revolution – University of Michigan application of scientific methods on the study of politics. Increased acceptance of quantitative methods. The discipline was maturing with the emerging of sub-fields, professional societies, and the creation of refereed journals. Entry of deductive and mathematical models (from economics) à More recently, neo-institutionalism, capture the interdependence between institutional structures and how they affect and are affected by political actors. Important to remember, the traditional normative approach still survives.
• A hybrid discipline – The discipline has evolved by barrowing theories and methods from other disciplines such as psychology, sociology, anthropology, economics, philosophy, and journalism. Look to other disciplines for guidance.
Overview of Political Science
I. The Development of Political
Science
II. Organization of the Discipline
Organization of the Discipline
• American Politics
• Comparative Politics
• International Relations
• Political Methodology
• Political Theory
- Each sub-field:
- 1) draw on different methodological and theoretical traditions.
- 2) subject to its own methodological problems and difficulties.
- 3) has its own journals and professional organizations.
- Because of the attention to sub-fields à criticized for lacking integration as opposed to finding commonalities among sub-fields à this affects other disciplines to one degree or another.
American Politics (two areas of research)
• 1) Institutions – Focusing on the major institutions of the American governing process. (ex. Presidency, courts, legislatures, congress, state/federal relations.)
• 2) Political behavior – motivation and consequences of individual political activity. (ex. Voting behavior, campaigns and elections, public opinions, etc.)
• Public policy / Public Law – (These are unique to the other two) Public Law – focused on thick description and textual interpretation. Public Policy – brings together both the study of political behavior and political institutions. à Early institution studies ignored institutional changes and development and how political behavior is constrained by its institutions. à Neo-Institutionalists paradyme – tries to bridge the gap between instutions and political behavior; institutions themselves are a function of individual preferences. Paradyme tries to tease out how institutions are affecting individuals while at the same time individuals are shaping institutions to achieve their own goals.
Comparative Politics
• Focus on many of the same concerns as American Politics (only difference is that it takes a comparative perspective and tends to look at multiple countries.
• The sub-field is divided between area studies and comparative work (areas studies – a single country qualitative / comparative work – region or multiple countries quanitative)
International Relations (shapes by two concerns)
• 1) War and the decision making and institutions that support it
• 2) Political economy
• Presently much of the work in this area examines the impact that domestic politics exerts on international relations
Political Methodology – goal is to improve our ability to study politics scientifically.
• Philosophy of science – trying to determine what make science science and how we separate scientific knowledge from non-scientific knowledge.
• Improving research methods
• Statistics – Developing new statistical procedures to allow us to more accurately test our theoretical arguments.
Political Theory
• Political theory is becoming divorced from the rest of the discipline (it is more normative while political science itself moves more towards being empirical.)
• Political theory is normative in its orientation
???? The Ancients (the Greeks)
???? Modern (post-Machiavelli)
???? Liberal political thought (post-
Enlightenment) (ex. Locke, Hobbes)
???? Contemporary political thought (more recent)
Lecture 3
Theory Building I:
Formulating the Research Question
Formulating the Research
Question
I. Role of the Research Questions
Role of the Research Question
• All research begins with a question.
???? The range of questions that can be addressed by political scientists is limitless.
???? Many of the questions of interest to political scientists overlap with other disciplines.
Role of the Research Question (driven by two goals)
• All questions are asked for the same purposes:
???? 1) Help us better understand the world in which we live.
???? 2) Help us better anticipate or control future events. (using knowledge in our research to improve)
Sources for Research Questions
1. Personal observations.
2. Prior research. – Assures continuity à insures incremental advancement.
3. Personal interests and experiences. – Be careful of subjectivity.
4. Normative theory. – Actual research must be empirical (how the world works rather than how it should work).
5. Methodological preferences. – (NOT IDEAL) The research question should drive the method not the other way around.
Evaluating Research Questions (Not all questions are of equal value)
1. Contribution to knowledge.
2. Relevance to politics.
3. Potential for originality.
4. Feasibility. (Very important – can I handle the question in a manageable manner?)
5. Ethical issues.
** Not every question will address all of these criteria, but it would be wise for a researcher to consider them from the outset.
Formulating the Research Question
I. Role of the Research Questions
II. Defining and Focusing the
Research Question
Selecting a Topic
• A researcher may begin with a basic topic:
???? International conflict.
???? Campaign finance.
???? Economic development.
• Very quickly, the researcher will need to focus their topic in order to develop a manageable research question.
** A general topic may motivate our research, but we must come up with a research question that is manageable.
Focusing the Research
Question
• As the question is being focused, we think about potential explanations for what we observe. (Theorizing – what is the potential answer)
• This leads to a working hypothesis, which lays the ground work for theory.
???? At this point, we are thinking at the level of concepts (can vague or open to interpretation)
???? We also want to think about the unit of analysis.
** Must be open to other points-of-view to avoid bias.
CONCEPTS – Term that can be used to describe objects, phenomena, or ideas.
UNIT OF ANALYSIS – What it is we are actually going to be studying.
EXAMPLE OF THE PROCESS:
Focusing a Research
Question: Example
• General question: Why do some people participate in politics?
Focusing a Research
Question: Example
• General question: Why do some people participate in politics?
• Focusing the question:
???? What do I mean by political participation?
???? What factors differentiate participants from nonparticipants?
Focusing a Research
Question: Example
• General question: Why do some people participate in politics?
• Focusing the question:
???? What do I mean by political participation?
???? What factors differentiate participants from non-participants?
• Focused question: What affect do demographic characteristics have on voter turnout?
Book: Essentials of Political Research – Alan D. Monroe
Why is it important to know about research methodology?
1) To better understand past research results
2) You can better conduct original research of your own.
Definitions:
Science – an attempt to identify and test empirical generalizations.
Empirical – facts or the real world – that which can be known through the experiences of our senses (seen, touched, heard, smelled).
Normative – it reflects our judgments about what should be. Scientific methods cannot deal with non-empirical questions.
Objective – the results must not be dependant on any particular researcher’s biases. Intersubjective Testability – A finding cannot be accepted unless it can be replicated by others.
Generalization – Make a statement about an entire class of objects, not just individual cases, though the observation must be of individuals.
· The main purpose of science is to explain and predict.
· The generalizations made in social sciences are almost never absolute.
· Analytical statements refer to prepositions whose validity is completely dependant on a set of assumptions or definitions rather than on empirical observation.
Methods of reformulating normative questions into empirical questions: (pg. 6)
1) Change the frame of reference
2) Ask empirical questions about the assumptions behind normative judgments.
· Empirical research can never answer a normative question.
· Scientific research begins with the question the researcher intends to answer.
Topics to consider when formulating a research question:
1) Clarity
2) Testability
3) Theoretical Significance
4) Practical Relevance
5) Originality
Stages in the research process:
1) Formulate research questions
2) Formulate hypotheses
3) Research Design
4) Data Collection
5) Data Analysis
6) Draw Conclusions
Lecture 4
Theory Building II: Conducting the
Literature Review
Conducting the Literature Review
I. The Role and Purpose of the Literature Review (Review existing body of knowledge)
- Political Inquiry – Has been around since the Ancient Greeks.
- Empirical Political Research – Roots begin with Machiavelli.
- Over 15,000 political scientists in the U.S. today.
The Role of the Literature Review
• Thorough knowledge of prior research is a key to theory building.
• What is a body of knowledge? (Developed slowly over a long period of time)
• Incremental contributions and iterative theory building. (Contribution is likely to be to an existing body of knowledge).
- Research is collective, not individual.
- Value of Research – Ability to build upon and extend existing literature incrementally adding to collective knowledge.
Purposes of the Literature Review (Read Wide and Knowledgeably)
• What research questions relevant to our topic have been addressed by others?
• How have other researchers gone about addressing these questions?
• What conclusions have these researchers found?
- Answering these questions will help…
o General understanding of past research relevant to our topic.
o Avoid problems down the road.
Purposes of the Literature Review (To get a firm understanding of research relevant to our topic)
• Uncover variables that we had not considered.
• Existence of relevant data.
• Measurement issues
• Rival or alternative explanations.
- Benefit from technology – Online Databases.
Conducting the Literature Review
I. The Role and Purpose of the Literature Review
II. Conducting and Organizing the Literature Review
Conducting the Literature Review
• Not exhaustive, but comprehensive
???? The more work that is done up front the easier the rest of the process goes.
???? The goal is to have a firm understanding of the accumulated knowledge in a given area.
???? The role of technology.
Conducting the Literature Review
• Structuring the search
???? Start broadly and move to more specific sources.
???? Efficiency hints:
• Start with most pertinent and most recent.
• Stayed focused. (Do not read every word, scan for things relevant to our research)
• “Borrowing” from others.
American Political Science Association: A Volume à The State of the Discipline summarizes all literature that has been done over the last decade.
Organizing the Literature Review
• Key research questions in the literature.
• Chronology.
• Competing explanations or conclusions. (Different camps of thought)
• Different methodologies. (Quantitative and Qualitative)
- Do not submit summaries of books (annotated bibliographies)
- Make as brief as possible
- Present crisp overview of literature relevant to your area of research.
Lecture 5
Theory Building III: The Logic of Theory Building
The Logic of Theory Building
I. What is a Theory? (Plausible explanation that will answer the research question.)
What is a Theory?
• The research question and theory building
???? As we develop and refine our research question, we begin the process of theory building.
???? This initial reasoning helps us to reduce the complexity of social life and puts us in a position to begin scientific inquiry.
What is a Theory?
• Purposes of theories
???? Theories create possible explanations for observed events.
???? Theories help to gain understanding of reality in order to better control it or adapt to it.
???? Theories provide direction for how to determine if our understanding of events is correct
???? Theories aid interpretation of events or data.
- Without theory we will not be able to determine if the data we observe is correct.
- Without theory, data are meaningless because theory provides context for why the data we observe exists in the first place.
What is A Theory?
• Theory defined
???? Theories are logically related propositions that represent what we think occurs (e.g., intellectual tools).
???? Theories are the proposed answers to our research questions.
???? Theories are neither true nor false in any absolute sense, but are evaluated in terms of their usefulness.
???? Theories are not found, but rather they are crafted.
- Theories go beyond description to address the WHY question.
- Theories begin with a thorough knowledge of what we want to explain (purpose of a literature review)
- Theory building can be thought of as a conundrum.
The Logic of Theory Building
I. What is a Theory?
II. Inductive Reasoning
Inductive Reasoning
PROCESS
Generalization
(assumption)
Induction
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Evidence
(many specific facts)
EXAMPLE
All Republicans are conservative
|
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Therefore
|
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All the Republicans in Nevada are conservative
- Induction – the purpose of generalizing from what we observe to what we have not/cannot observe (moving from the specific to the general)
- Inductive reasoning is considered weaker.
- Empirically Grounded – with inductive theory building observation of the data precedes our attempt to explain it.
- The main weakness – observe data in a specific context in trying to make a general claim devoid of that context.
- Goal – if we accurately tap into general assumptions it should apply to all.
The Logic of Theory Building
I. What is a Theory?
II. Inductive Reasoning
III. Deductive Reasoning
Deductive Reasoning
PROCESS
Generalization
(assumption)
|
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Deduction
|
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Evidence
(predictions about many specific facts)
EXAMPLE
The Republican Party attracts only conservatives
|
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Therefore
|
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All the Republicans in Nevada are conservative
- Considered higher powered theory.
- Works opposite of induction.
- Reasoning from general to specific.
- If general assumption is on the mark should help to explain the specific.
The Logic of Theory Building
I. What is a Theory?
II. Inductive Reasoning
III. Deductive Reasoning
IV. The Confluence of Inductive and Deductive Reasoning
The Confluence of Induction and Deduction
1. Use induction to translate observations into general assumptions.
2. Then use deduction to develop predictions.
3. Test these predictions with new data.
4. Revise assumptions based upon analysis.
5. Repeat the process to refine and clarify the theory.
- Most theories involve an interaction between induction and deduction.
The Wheel of Science
Lecture 6:
Theory Building IV: The Building
Blocks of Theory
The Building Blocks of Theory
I. Concepts
Concepts
• What is a concept?
???? Concepts are the initial building block of theories.
???? Theories are composed of sets of concepts that are related by logical propositions.
???? Concepts are merely a word or symbol that represents some idea.
Concepts
• Making concepts useful
?? 1 - The concept must refer to a phenomena that is potentially observable (empirical referents – something we can observe that is indicative of the concept – concept itself is not observable ex. democracy).
?? 2 - Concepts must be precisely defined.
?? 3 - Concepts must have theoretical import.
The Building Blocks of Theory
I. Concepts
II. Variables
Variables (Similar to Concepts)
• From concepts to variables
???? Concepts are mental constructs that need to be fleshed out.
?? Once they have been defined and we have associated specific properties with them, they become variables.
- Variables are logical groupings of attributes.
Variables
• Variables and attributes
???? Attributes are characteristics or qualities that describe an object.
???? Variables are logical groupings of attributes.
???? Attributes are the categories that make up a variable and represent our concepts.
Concepts vs. Variables vs. Attributes
Concepts
Education
Age
Sex
Social Class
Occupation
Race/Ethnicity
Variables
Education
Age
Gender
Social Class
Occupation
Race/Ethnicity
Attributes
Years of Education
Young, old
Male, female
Upper, lower, middle
Types of jobs
African American,
Asian, Latino
Variables
• Variables and attributes
???? Variables must have more than one attribute. (VARIABLES MUST VARY)
???? Description versus explanation.
Variables
• Independent and dependent variables
???? The independent variable is the factor that theory argues causes change in the relationship of interest
???? The dependent variable is the effect (e.g., the behavior, decision, outcome of interest) we are trying to explain.
???? Bivariate versus multivariate theories.
- Bivariate – (Simplest, rare, trivial) Single independent variable on a dependent variable.
- Multivariate – (Common in social science) More than one independent variable all affecting the dependent variable.
The Building Blocks of Theory
I. Concepts
II. Variables
III. Assumptions
Assumptions
• The role of assumptions
???? Assumptions are the glue that hold our theory together.
???? Because of their importance to theory building, assumptions need to be logically derived.
Assumptions
• Where do assumptions come from?
???? With deduction, assumptions come first.
???? With induction, assumptions are made after observation.
???? To aid us in developing reasonable assumptions, we may turn to paradigms.
The Building Blocks of Theory
I. Concepts
II. Variables
III. Assumptions
IV. Paradigms
Paradigms
• Role of paradigms
???? Paradigms are fundamental models used to organize our observations and reasoning.
???? Paradigms can be difficult to recognize because they are often implicit.
???? Paradigms provide researchers with sets of assumptions from which they can build their theory.
Paradigms
• Paradigms and scientific development
???? Thomas Kuhn (The Structure of Scientific Revolutions). (1st to focus on paradigms)
???? In the social sciences, paradigms may gain or lose popularity but they are seldom discarded.
???? Instead, social sciences paradigms represent a variety of views, each of which offers insights the others lack while ignoring aspects of social life that the others reveal.
Examples of Paradigms in
Political Science
• Realism – Used primarily in international politics. Internationally we are in a state of anarchy à competitive.
• Rational choice – Barrows from economics à decision making as a function of cost benefit analysis. (Cross disciplinary)
• Neo-institutionalism – Institutions are the rules of the game à How humans construct institutions and then are affected by them in decisions.
• Political psychology – Study of political behavior mostly through the work of Walter Litman. Politics is a very low interest activity à low cognitive effort to involve ones self.
Lecture 7
Theory Building V: Causality and Parsimony
Causality and Parsimony
I. Causality
Causality
• The centrality of causality
???? Central to any theory is demonstrating causality between the independent and dependent variables.
???? Social scientific theories focus on those independent variables or factors that have a strong and systematic effect on the dependent variable.
???? Probabilistic versus deterministic reasoning.
- Probabilistic (mostly thought of in these terms) – when the independent variable is present it is probable in observing change in the dependent variable.
- Deterministic – In every case where the independent variable is present that there should be the outcome we expect to see in the dependent variable (If one case doesn’t work the whole theory is thrown out).
Causality
• The concept of correlation
???? The strength and direction of causal relationships.
???? Direction can be either positive or negative.
???? The strength of a relationship can be subjective.
???? Collectively, we think of relationships between variables in terms of correlations.
- Correlation in itself does not guarantee causality.
Understanding Correlation
Positive – move in the same direction.
Negative – move in different directions.
Criteria for Causality
1. The variables are empirically correlated (a covariational relationship).
2. The cause precedes the effect.
3. A causal process that accounts for process of interest can be logically derived.
4. The correlation between the variables cannot be explained by a third variable (a spurious relationship).
- They are interrelated – Correlation (empirical phenomena that we can observe) – Causality (only comes from theory).
Problems in Establishing Causality
• Exogenous causal relationships
???? Causation runs from the independent variables to the dependent variable (independent affects dependent)
???? Example: The impact of inequality of wealth on revolution
???? Example: The impact of age and education on voter turnout
Problems in Establishing Causality
• Endogenous causal relationships
???? Causation runs back and forth between the independent and dependent variables. (Variables affect each other).
???? Example: The relationship between public opinion and policy outcomes.
???? Example: The relationship between campaign fundraising and election outcomes.
- Misspecified Theory – when you ignore endogenous relationships.
Causality
• Control variables
???? A second class of independent variables a theory needs to account for are control variables.
???? To demonstrate causality, we need to account for any other factors that may affect the relationship of interest.
???? Past research and identification of control variables.
Causality and Parsimony
I. Causality
II. Parsimony
Parsimony
• Importance
???? The development of general theories places a premium on parsimony.
???? Parsimony is defined as “extreme or excessive economy or frugality.”
???? Parsimonious explanations are simple, precise, and elegant.
Parsimony
• Occam’s razor
???? Occam's razor is a logical principle attributed to the mediaeval philosopher William of Occam.
???? It states that one should not increase, beyond what is necessary, the number of entities required to explain anything.
Causality and Parsimony
I. Causality
II. Parsimony
III. Characteristics of Useful Theories
Characteristics of Useful Theories
1. Testable – Needs to be able to be assessed with data.
2. Logically Sound – Consistent, cause precedes effect, etc.
3. Communicable – Can others understand your theory?
4. General – Predictative accuracy.
5. Parsimonious – Simple enough to be explained and applied.
- Useful Theories Provide: Clarity, simplification, and precision.
Building Generalizable Theories
Reading: Essentials of Political Research Chapter 2 – Alan D. Monroe
Theories, Hypotheses, and Operational Definitions:
- Science starts and ends with theories.
- Theories – A set of empirical generalizations about a topic.
- A theory consists of very general statements about how some phenomenon, such as voting decisions, economic developments, or outbreaks of war, occurs.
- But theories are too general to test directly because they make statements about the relationship between abstract concepts that are complex and not directly observable.
- To actually investigate the empirical applicability of a theory, it must be brought down to more specific terms.
- This is done by testing hypotheses.
- Hypotheses – An empirical statement derived from a theory.
- If a general theory is correct, then the more specific hypotheses derived from it ought to be true.
- If the hypothesis is confirmed by empirical observation, then our confidence in the general theory is increased.
- However, if a hypothesis is not confirmed, we must question the validity of the theory from which it was derived.
- Hypotheses are those answers to our research questions that seem to be the most promising on the basis of theory and past research.
- Hypotheses are statements about variables.
- Variable – an empirical property that can take on two or more different values.
- Each variable in a hypothesis must have an operational definition.
- Operational Definition – A set of directions as to how the variable is to be observed and measured.
AN OVERVIEW OF LEVELS OF RESEARCH: (p. 18)
LEVEL
THEORY: Concept 1 is related to Concept 2.
HYPOTHESES: Variable 1 is related to Variable 2.
OPERATIONAL: Operational Definition 1 is related to Operational Definition 2.
TYPES OF HYPOTHESES:
- Univariate Hypothesis – The hypothesis makes a statement about only one property or variable.
- Multivariate Hypothesis – Makes a statement about how two or more variables are related.
- Most scientific hypotheses are multivariate as well as directional.
- Directional – Specifies not just that the variables are related to one another but also what the direction of the relationship is.
- Positive/Direct Relationship – As one variable raises the other tends to rise (or as one falls the other falls).
- Negative/Inverse Relationship – As one variable rises the other tends to fall.
- Nominal Relationships – The hypothesis does predict direction, but one or both of the variables are such that they cannot be described in quantitative terms.
THEORETICAL ROLE:
- Theoretical Role – The presumed causal relationship between the variables are specified.
- Independent Variables – Those presumed in the theory underlying the hypothesis to be the cause.
- Dependent Variable – Are the effects or consequences.
- Often the nature of the relationship lies in the timing between variables.
- We usually presume that the social factors are independent variables and the behaviors are the dependent variables.
- Ultimately the decision as to which are the independent and which are the dependent variables is based on our theoretical understanding of the phenomena in question.
- Control Variable – Are additional variables that might affect the relationship between the independent and dependent variables.
UNIT OF ANALYSIS:
- Unit of Analysis – the objects that the hypothesis describes.
- Ecological Fallacy – Erroneously drawing conclusions about individuals from data on groups.
- The best way to avoid the problem is to draw conclusions only about the units of analysis for which the data were actually collected.
OPERATIONAL DEFINITIONS:
- Testing hypotheses requires precise operational definitions specifying just how each variable will be measured.
- Aggregates – Population groups.
- All variables in a hypothesis must be operationalized for the same unit of analysis.
- Two requirements in constructing the operational definition:
o 1 – What we want, and
o 2 – Where (or how) we will get it.
- If the unit of analysis is the individual then the source usually must be a survey.
- Standardized – Measured in a way that makes comparison of the different cases meaningful.
Reading: Reading Journal Articles:
- Journal articles provide very detailed and comprehensive perspectives of small pieces of much larger puzzles.
- The first thing to figure out is what the larger puzzle is.
- Often times the title, the introduction, or the conclusion can help.
- Journal articles organization – 1) Identifying the research question 2) the literature review 3) specification of theory 4) research design 5) analysis 6) discussion and conclusion.
Reading: Doing a Literature Review:
- A literature review summarizes and evaluates a body of writings about a specific topic.
- 2 Key elements of a literature review: 1) concisely summarize the findings of prior research 2) reach a conclusion about how accurate and complete that knowledge is.
Lecture 8 Operationalization
Operationalization
I. Operationalization
Overview
Operationalization Overview
* More abstract to more concrete.
Operationalization
I. Operationalization Overview
II. From Concepts to Indicators
From Concepts to Indicators
• Concepts and conceptualization revisited
???? The process of conceptualization lays the ground work for measurement.
???? Central to empirical research is the contention that any concept can be measured (through indicators).
- Concepts – description of mental images.
- use best words to describe concepts to properly convey meaning to others.
- Measurements – careful observation of the real world for the purpose of describing events and objects in terms of attributes composing of variables.
From Concepts to Indicators
• Concepts as constructs
???? Scientists seek to measure three things:
• Direct observables (something we can observe directly ex. Color)
• Indirect observables (things we indirectly observe based upon information taken from another source ex. Survey Data)
• Constructs (theoretical creation based upon observation but we cannot observe directly ex. Measure Intelligence à IQ Test)
???? Because concepts are not real in any tangible sense, but rather they are descriptions of mental images, can they be measured?
From Concepts to Indicators
• Indicators
???? Conceptualization gives definitive meaning to a concept by specifying one or more indicators of what we have in mind.
???? An indicator is a sign of the presence or absence of the concept we are studying.
???? Thus, concepts are not measured, but rather we measure indicators of these concepts.
???? In many cases, there may be multiple indicators that might be used as proxies for concepts.
- Ex. Inflation à is an indication of the state of the economy.
- Ex. Multiple Indicators – Inflation, unemployment rate, and GDP à all indicators of the state of the economy.
From Concepts to Indicators
• Bringing it together
???? Conceptual definitions give definitive meaning to the terms we are using,
???? This lays the ground work for the operational definition, which define the procedures that will result in empirical observations of these concepts.
???? In so doing, we precisely specify our variables and the indicators that we will analyze.
Operationalization
I. Operationalization Overview
II. From Concepts to Indicators
III. Formulation of Hypotheses
What is a Hypothesis?
• A statement of the expected probabilistic relationship between our independent and dependent variables.
• Hypotheses capture the essence of the causal relationship posited by our theory. (but do not explain the “why” part, the Theory does that)
• A separate hypothesis is needed for each independent variable.
Characteristics of a Good Hypotheses
• Empirical statements. (not normative)
• Focus on general as opposed to specific relationships.
• Based on logical reasoning. (Explanation as to why we expect the independent variable to effect the dependent variable)
• Stated in as specific terms as possible. (Directional – positive or negative)
• Testable. (Must be evidence or data in the real world that we can collect to test if hypotheses is correct or not)
Causal Diagram Example: Impact of Spending on Student Performance
Causal Diagram Example: Capitol Punishment Policy in the States
*Nothing is set in stone in the research process.
Lecture 9
Measurement (the process by which we develop rules for defining what the indicators of our variables are)
I. Introduction
Operationalization Overview
Introduction
• Importance
???? The process by which the rules and procedures for defining our indicators for our theoretical concepts are defined.
???? For every variable in our theory we need to develop an empirical indicator.
???? Without valid and reliable measurement, empirical validation is impossible.
Introduction
• The nebulous nature of scientific concepts
???? For many concepts that are commonly used, we do not give much thought to how they are measured.
???? Measurement is of even greater import when working with abstract and complicated concepts.
???? This problem plagues both the hard and soft sciences.
???? The language of measurement provide the operational choices used to develop empirical indicators .
- Measurement is uncertain even for things we take for granted: (Ex. Weights and measures à Europe uses metrics)
- Pope Gregory 13th à Gregorian Calendar
Measurement
I. Introduction
II. The Language of Measurement (Provides the operational choices to develop valid and reliable indicators)
The Language of Measurement
• Range of variation
???? What is the potential range in values that a concept can have?
???? To what extent am I willing to combine attributes into gross categories?
???? What is the appropriate range that is needed to capture the variation I think occurs in the world?
- Ex. Income
The Language of Measurement
• Defining categories
???? Conceptual and operational definitions specify our variables and their attributes.
- Ex. Measuring political affiliation:
???? The attributes composing a variable should be exhaustive. (Republican, Democrat, or Independent)
???? The attributes composing a variable should be mutually exclusive. (Only one Category – Ex. Republican)
The Language of Measurement
• Precision
???? Precision focuses on the amount of information about a concept.
???? Higher levels of measurement possible provides more information about the concept of interest.
???? The level of measurement also has implications for the use of statistics.
Levels of Measurement
• Nominal – variables that are mutually exclusive and exhaustive (e.g., categories).
• Ordinal – variables that can be logically ranked. (There is order, but not a precise difference Ex. Age in terms of decades)
• Interval – indicates ranking and specifies exact difference between categories. (THIS IS IDEAL – actual distance separating the attributes Ex. Age in terms of years)
The Language of Measurement
• Reliability
???? A measure is reliable to the extent that it gives the same result if the measurement is repeated. (Primary concern = Consistency)
???? There are numerous sources of unreliability in social science data. (Ex. Attitudinal measurement – different interpretations cause problems)
???? The best way to avoid unreliable measures is to develop precise conceptual and operational definitions prior to the fact.
Testing for Reliability
• Test-retest – repeating the measurement a second time. (Should get the same results)
• Multiple coders – different individuals measure the same concepts and then inter-coded reliability checks are used to assess reliability.
The Language of Measurement
• Validity (Primary concern = accuracy)
???? How accurately does a measure captures the theoretical concept of interest?
???? Validity is a bigger concern than reliability.
???? Testing for validity is more difficult than testing for reliability.
Testing for Validity
• Face validity – on its face does the measure seem to be valid? (Most common but less rigorous – Self-evident)
• Construct validity – does the measure perform how we expect it to in relation to other concepts?
• Discriminant validity - how does the measure differ from indicators of other concepts it is unrelated to? (Ex. Trust of people in general vs. trust of government officials à should be different)
• Pragmatic validity – how well does the indicator perform compared to another measure that we know is valid? (Ex. More appealing candidates get a bigger share of the vote)
* THEORY DRIVES MEASUREMENT NOT THE OTHER WAY AROUND!
Measurement Error
• No measurement is 100% accurate.
• Measurement error stems from two considerations:
???? Random error is a function of reliability. (Defies data randomly)
???? Systematic error is a function of validity. (More problematic – Defies all data)
• Our goal is to eliminate systematic error and minimize random error.
Measurement
I. Introduction
II. The Language of
Measurement
III. Measurement Examples
Nominal Data Coding Example: Gender
Male = 1
Female = 0
*Dichotomist or Dummy variables.
Nominal Data Coding Example: Religious Affiliation
Catholic = 1
Jewish = 2
Protestant = 3
Muslim = 4
Ordinal Data Coding Example: Attitudes (Uses categories à not precise)
*This is called a Likert scale à captures rates of attitudes.
Ordinal Data Coding Example: Education
Ordinal Data Coding Example: Partisan Identification
Simple Interval Data Coding (Ranking and precise differences)
Examples: Age and Education
• Age measured in years
• Education measured in years
Complex Interval Data Coding
Examples: Media Exposure
A scale taken from the following NES question:
1. # of days per week respondent watches television news
(0-7)
2. # of days per week respondent reads a newspaper (0-7)
3. How closely respondent followed campaign through television news (1-5)
4. How closely respondent followed campaign through newspaper (1-5)
The latter two variables are transformed into eight point scales to make them equivalent to the other two components of the index by subtracting one and multiplying by 7/4. The measure is then divided by 2.8 to create a scale ranging from 0 to 10.
Measurement
I. Introduction
II. The Language of
Measurement
III. Measurement Examples
IV. Indexes, Scales, and Typologies
Indexes, Scales, and Typologies
• Used for concepts whose meanings are complex and varied, or when multiple indicators exist for a concept. (May need more complex measures à scales & typologies)
• Typically, scales and indexes are used in quantitative analyses.
• Typologies are used in both quantitative and qualitative research.
Indexes versus Scales
• Terms are used imprecisely and interchangeably.
• Both are interval measures and both are composite measures of indicators.
DIFFERECNE BETWEEN INDEXES AND SCALES:
• Indexes simply aggregate values from multiple indicators together.
• Scales do this as well as weight the individual indicators to reflect the degree to which each indicator taps the variable.
Index Construction Logic (Activism on some degree)
Scale Construction Logic (Different degrees of political activism)
Typologies
• Summarize the intersection of two variables to create categories or groupings.
• Typologies can be used as independent variables, but they do not work as dependent variables.
Typology Example
• Assessing the ideological tone of newspaper coverage of domestic and foreign policy.
Book: Monroe Pages 83-90:
Levels of Measurement:
- The term level of measurement refers to the classification or units that result when a variable has been operationally defined.
- There are three levels of measurement: nominal, ordinal, and interval data.
Nominal Variables:
- The “lowest level of measurement,” that is the least precise, is the nominal level.
- A nominal variable simply places each case into one of several unordered categories.
- Nominal variables contain information on “what kind” not “how much.”
Ordinal Variables:
- Ordinal variables rank cases in relation to each other.
- This can take two forms: 1) Rank order 2) Ordered categories.
- Rank order puts the cases in exact order according to some characteristic. Rank order is not much used in analysis for research purposes.
- Ordered categories are not put into categories (like nominal variables) the categories have inherent order.
- Unlike nominal variables, ordinal variables, whether rank order or ordered categories, may be described in quantitative terms.
- In determining whether a set of categories may be considered as ordinal, it is important to remember that all categories must fit a pattern of high to low (or low to high) on the variable.
Interval Variables:
- This is the highest level of measurement.
- An interval variable provides an exact number of whatever is being measured.
- There is also a similar mevel of measurement called a ratio scale.
Examples of levels of measurement: (p. 86).
Rules for using levels of measurement: (Examples p. 89)
1) A variable may always be treated as a lower level of measurement. (Always can go down, but never up)
2) A dichotomy may be treated as any level of measurement. * A dichotomy is a variable that has two and only two possible values or categories (Ex. Male or female). In multivariate analyses a dummy variable is sometimes created using each category in a nominal variable to create new dichotomous variables.
Why levels of measurement are important:
- Because each of the many statistics designed for data analysis makes assumptions about the variables’ level of measurement. If you use an inappropriate statistic to evaluate your data, the results may be meaningless and lead you to draw erroneous conclusions.
- Always be aware of the level of measurement of your variables and of what levels the two rules will allow you to treat them as.
What is a statistic: P. 88 & 90.
Readings:
“The Multi-Layered Impact of Public Opinion”:
- One cannot understand the relationship between public opinion and policy without analyzing the interrelationships between the two over time.
- Without both a historical component and an investigation of implementation, policy theorists may underestimate the influence of public opinion on governmental programs in the states.
“The Poverty Measure”
- Economists have long argued that the poverty line should be revised to provide an accurate picture of who is actually poor.
- First developed in 1963 by Mollie Orshansky (an economist at the Social Security Administration). This was based on the affordability of an adequately nutritious diet. The cost was multiplied by three and if a family spent less than 1/3rd of their income on food then they were not poor.
- Hasn’t changed due to politics: decision to be made by the President, but does not affect a majority so is not a priority on the agenda.
“Bloomberg Seeks New Way to Decide who is Poor”
- Mayor Bloomberg of New York is developing and implementing a new poverty measure in hopes to gain national attention regarding the need for a new nationwide measure.
- Scholars say the new formula is likely to increase the poverty rate within the mayor’s city.
Lecture 10
Research Design (How research tends to fulfill the goal of a proposed study)
I. Role and Importance
Operationalization Overview
Research Design Definition
• The plan a researcher develops to fulfill the goals of a proposed study.
• The research design provides the empirical evidence necessary to evaluate the usefulness of a theory.
• There is continual back-and-forth and refinement between each stage of a research project.
- Imaginative process à not “cookbook” research.
- Research design can make or break the entire research process.
Causality (one variable causes change in another)
• Empirical, theory-oriented research is exclusively concerned with assessing causal relationships.
• Research design is where causal relationships are formally evaluated.
- Causality only come from our theory.
Criteria for Causality
1. The variables are empirically correlated (a covariational relationship).
2. The cause precedes the effect.
3. A causal linkage that accounts for process of interest can be logically derived.
4. The empirical correlation between the variables cannot be explained by a third variable (a spurious relationship).
False Criteria for Causality
• Complete causation
???? Because of parsimony, social scientific models do not seek to completely explain causality.
- Identify the variable that has the strongest effect.
• Exceptional cases
???? Because of probabilistic reasoning, cases that do not comport to our theoretical expectations, do not disprove a causal relationship.
• Majority of cases
???? Causal relationships can be true even if they do not apply to a majority of cases.
- Extreme cases (ex. An incumbent beat by a challenger because the challenger was able to raise tons of money)
Alternative or Rival Explanations
• A well-formulated research design also accounts for alternative explanations that could account for the causal process.
• A rival or alternative hypothesis is one that predicts the same outcome but asserts a different causal process.
- Traditionally done by controlling for an additional independent variable.
• Typically, this is done by controlling for other independent variables suggested by prior research.
Example:
Controlling for Alternative Explanations: Gender and Vote Choice Example
• Theory: there are gender differences (the iv) in voting behavior (the dv).
• Past research suggests that education, income, and ideology also affect voting behavior.
• To insure that the differences in the dependent variable are due to gender we also need to account for the influence of these other independent variables.
• This is more difficult, but increases validity.
Sources for Rival
Explanations
• Past research.
• Our own thinking.
• The review process.
Components of a Research Design
• 1 - Statement of research hypotheses to be tested.
• 2 - Discussion of sample and data sources.
• 3 - Discussion of the data collection.
• 4 - Precise definitions of the indicators used to measure each of our variables.
• 5 - Discussion of how the data that will be analyzed.
- Hypotheses: a single sentence statement that captures our theory.
The Unit of Analysis (what it is that we are studying)
• In social research there is virtually no limit to who or what can be studied.
• Units of analysis can be individuals, geographic entities, groups, outcomes.
• The ecological fallacy. (Drawing a conclusion on a level that your data was not measured at)
- Unit of analysis dictates what conclusions we can draw.
Research Design
I. Role and Importance
II. Research Design Templates
Research Design Templates
• Time and space
???? Research designs seek to capture process that exists in either time or space.
???? Thus, the relationship that we are interested in capturing may have a temporal or a spatial component (or in some cases both).
???? Space refers to what is occurring in a particular place at a particular point in time.
???? Time refers to process that develop over a period of time. (Ex. Bugeting, study of public opinion, etc. à how relationships change over time)
The Basic Experimental Design (Strength: maximum leverage over causality)
- 3 important elements:
1) Pre-test and post-test of dependent variable to measure change.
2) Experimental and control group
- Experimental: group experiences the dependent variable
- Control: does not experience the dependent variable (comparative category)
3) Independent variable is administered by the researcher at will.
- Quasi-experimental design: taking the logic of experimentation and applying it to non-experimental situations.
Cross Sectional Designs (most common because of the reliance on survey data)
• The independent and dependent variables are measured once and at the same time.
• The distribution of the independent variable creates quasi-experimental and control groups.
• Variation in the values of the independent variable are used to assess variation in the dependent variable.
Cross Sectional Designs
• Strengths
???? Data collected in natural setting.
???? Large and representative samples.
???? Allow for easy control of rival explanations. (Through statistics)
Cross Sectional Designs
• Weaknesses
???? Limited control over causality.
???? Less precision. (In developing measures preferred)
???? No point of comparison. (Major drawback)
Longitudinal Designs
• These designs examine the same phenomena over an extended period of time.
• Used in both qualitative and quantitative research.
• There are three type of longitudinal designs:
???? Trend or time series studies.
???? Cohort studies.
???? Panel studies.
Trend or Time Series Designs (Interested in examining changes in population over time)
• Time series examines changes in trends that occur overtime in response to the introduction of the independent variable.
• The before and after represent quasiexperimental and control groups.
• These designs require numerous measures of the dependent variable before and after the independent variable is introduced.
• Problems with aggregated data.
- This design is common to public opinion studies.
Time Series Analysis
Time Series Analysis (key to this type of analysis – lots and lots of data)
Time Series Analysis
Public Opinion and Abortion
- Aggregated data: you may miss stuff that occurs at a lower level of the data.
Public Opinion and Abortion
Cohort and Panel Designs
• Cohort designs examine specific subpopulations or cohorts over time. (Ex. Age groups)
???? The same subjects are not analyzed in successive waves.
???? instead, different samples from the cohort are taken.
- Cohort is more common in sociology.
• Panel studies are similar except the same subjects are analyzed at each wave.
???? Strengths: better able to assess causality.
???? Weaknesses: measurement error, cost, and panel morality. (Cost à need a large sample to start and need to keep track of all of the people.)
Advanced Designs
• Designs that attempt to overcome limitations associated with cross sectional and longitudinal designs.
• Pooled time series and pooled cross sectional approaches.
• Pooled cross sectional time series designs.
- These are attempts to get more validity out of the test.
- We don’t choose the research design we create one that is suitable for our purposes determined by creativity, resources available, and ethical concerns.
Lecture 11
Sampling and Data Sources
Sampling and Data Sources
I. Sampling (this is used in all research not just in surveys)
Sampling and Research Design
• Every research design involves sampling
???? A population is the universe of relevant observations.
???? A case is a single observation taken from a population.
• Samples may be constrained by data and resource limitations.
• The ease with which samples can be gathered affects the type of research that gets done.
- Importance: this type of research wants to draw a conclusion for an entire population à often impossible à so you take a sample à and then make inferences.
- Limitations:
- Data (Ex. Campaign finance data is only available as far back as the 1970s)
- Cost
- The data available dictates the type of research that is done.
Sampling and Research Design
• Samples and Populations
???? Researchers are interested in characteristics of populations.
???? Because it is usually impossible to survey an entire population, a sample is taken from the population.
???? What is learned from the sample is then used to make inferences about the population.
Sampling Logic
Characteristics of Good Samples
• A good sample is one that is representative of the population.
• A representative sample is one in which every attribute of interest in the population is present and roughly proportionate to the occurrence of each attribute in the population.
Sampling Methods:
1) Probability Sampling
• The preferred method because if it is done correctly, it yields representative samples.
• Every element of the population has a known, non-zero probability of being sampled.
• Random selection from the sampling frame.
• Allows for the calculation of the sampling error (e.g., the margin of error).
- We like this type because it allows for a margin of error to be calculated.
2) Non-probability Sampling
• Each element of the population has an unknown chance of being selected.
???? Does not allow for sampling error to be calculated.
???? Tend to be less representative. (and in many cases can be biased)
• Examples:
???? Volunteer subjects. (Ex. Students volunteer for a professors study)
???? Haphazard samples. (people happen to be where you are taking the sample)
???? Quota samples. (research sample can meet certain quotas à (weight for a certain number of age, sex, race, etc. à problem is that it is not random)
???? Purposive samples. (this is the only type that should be used in non-probability à it seeks out the sample (because they are rare)
The Process of Sampling
1. Identification of population of interest: who we are trying to learn about (defined by research question): Population: People likely to vote on Election Day
2. Select the sampling frame: a list of the target population from which sample is drawn (ideally, this is the same as the population): Sampling frame: List of phone numbers of registered voters
3. Draw sample using probability sampling method: Typically, this is accomplished via random digit dialing and screening questions.
Sampling
• Telephone Sampling (today most are done this way) à (downside: respondent fatigue – hang-up)
???? The majority of samples are drawn via random digit dialing. (originally suspect because the poor did not have phones à not today because 98% have phones)
???? Technological changes. (Problem today – cell phones, pagers, faxes, etc)
???? Response rates (difficult to get a hold of people à certain people underrepresented)
Sampling
• Sample Weighting
???? Samples may be weighted if the sample is biased due to problems with the response rate.
???? When weighting is done, some respondents are weighted more or less so that the overall sample is reflective of the population.
???? Weighting assumes that, for instance, sampled males are representative of unsampled males.
Sampling (never going to be 100% accurate)
• Sample Size and Sampling Error
???? How can a sample of 1,500 accurately represent the views of nearly 300 million Americans? (major criticism)
???? The precision that is lost is determined by the sample size and the sampling procedure
- 2 sources of error:
???? 1 - Systematic error: error that is built into the design that systematically biases results. (bad because it biases the entire sample)
???? 2 - Random error: the margin of error. (due to sampling procedure)
Sampling
• Famous Incorrect Samples
???? The 1936 Literary Digest presidential poll. (claimed incumbent FDR would lose à sample was not representative because they used car registrations and phone records à depression à sampled only the wealthy)
???? The 1948 presidential election.
Sampling Error: The 1948 (correct sampling methods but they stopped collecting too early)
Presidential Election
Sampling and Data Sources
I. Sampling
II. Data Sources
Data Sources
• The ubiquity of survey research. (surveys are commonly used à advantages – large number of observations, sample is representative, quantifiable, easy to use à problem – question development wording etc.)
• Government documents. (Federal, state, and local to a lesser extent Ex. Crime statistics à strength – easy to gather, already in a quantified format à Weakness – no way for researcher to check validity)
• Direct observation. (usually used by other social sciences à strength – observed in a natural setting à weakness – unclear on how representative, ethics issues, concerns of objectivity)
• Primary documents. (used more often with quantitative work (diaries, etc) à Weakness – reliability, validity.
• Content analysis. (take written speech with an eye toward developing a quantitative measure à weak – reliability)
• Interviews. (similar to surveys à not representative sample à strength – comprehensive information, multiple perspectives)
- Book: Monroe Pages. 32 – 46
- Book: Monroe Chapter 5
Lecture 12
Qualitative and Quantitative Approaches
Qualitative and Quantitative Approaches
I. Qualitative Approaches
Qualitative Approaches (interested in studying a case in its entirety)
• Definition
???? Qualitative methods are also referred to as case study methods.
• Qualitative research seeks to understand cases in their entirety.
• This leads to an emphasis on thick description.
???? A case study can be thought of as a setting or group that the analyst treats as an integrated social unit that is studied holistically and in its particularity.
* Trades breadth for depth
Qualitative Approaches
• Characteristics of qualitative approaches
???? Focus on a small number of cases.
???? Generally, large units of analysis. (e.g. countries, states)
???? Seek complete explanations of each case.
???? Study phenomena in the context in which they occur.
???? Focus on description and detail as opposed to numerical indicators.
Qualitative Approaches
• Examples of qualitative approaches
???? Participant observation
• Field research.
• Developing a substantial relationship with people while they go about their normal activities. (researcher is immersed within the case)
• Overt versus covert observation.
- Covert – researcher blends in and does not divulge role as a researcher to get better quality data.
- Overt – everyone knows that the researcher is conducting research.
Qualitative Approaches
• Examples of qualitative approaches
???? Intensive interviewing (open-ended, unstructured questioning)
• Relatively unstructured questioning that seeks to uncover in-depth information on the interviewee’s feelings, experiences, and perceptions.
• Much less structured as compared to a survey.
• Greater engagement between the subject and the interviewer.
Qualitative Approaches
• Examples of qualitative approaches
???? Focus groups (informal, unstructured à researcher leads discussion)
• Unstructured group interviews in which the group leader actively encourages discussion among participants on the topics of interest.
• Seeks to mimic the natural process of forming and expressing opinions.
• Unrepresentative samples. (Can undermine generalizability)
Qualitative Approaches
• Uses of qualitative approaches (5 reasons to use:)
???? Testing theories.
???? Creating theories. (theories are created while analyzing data à inductive approach)
???? Identifying antecedent conditions.
???? Testing the importance of antecedent conditions.
???? Examining cases of great importance. (e.g. WWI & WWII)
Qualitative Approaches
• Qualitative approaches and the research process
???? Defining the research question (dictates method)
???? Theorizing (observation precedes theory)
???? Research design (qusai-design less emphasis on causality)
???? Sampling (insight to specific causes)
???? Data collection (words as opposed to numbers)
???? Data analysis (greater modification)
???? Standards of evidence (lacks pre-defined rules to create a string argument)
???? Reporting the results (narratives describing what was observed)
* Problem with selecting on the dependent variable: selecting cases that support your theory and avoiding cases that do not.
Qualitative Methods
• Controlling for rival explanations
???? Research designs test both our theory and alternative explanations.
???? With quantitative methods this is done via statistical control.
???? With qualitative methods this is done via case selection.
• Most similar cases approach.
• Most different cases approach.
Qualitative and Quantitative Approaches
I. Qualitative Approaches
II. Quantitative Approaches
Quantitative Approaches (also referred to as large n studies)
• Characteristics of quantitative approaches
???? Use of numbers and statistics to examine political phenomena.
???? Emphasis on measurement of theoretical concepts.
???? Seeks to develop parsimonious and generalizable explanations.
???? Breadth versus depth.
Raw Data
Quantitative Approaches
• Goals of Quantitative Studies
???? To test theories by examining patterns within our data. (not so concerned about a single case, but about the general pattern)
???? Probabilistic as opposed to deterministic.
???? Seek to generalize back to populations. (parsimonious theories & representative samples)
Quantitative Approaches
• How large does a large n study need to be?
???? No magic number.
???? More is better.
???? Depends on statistical methods being used.
* Usually at least 120 cases for assumptions to be met.
Quantitative Approaches
• Quantitative Approaches and the
Research Process
???? Defining the research question
???? Theorizing (pre-formulated theories)
???? Research design
???? Sampling (large and representative)
???? Data collection (numerical measurement of variables)
???? Data analysis (planned in advance of data collection à uses computers)
???? Standards of evidence (clearer rules to establishing validity)
???? Reporting the results (numerical data to present evidence)
Quantitative Approaches
• Strengths of quantitative approaches
???? Control for alternative explanations.
???? Representative samples. (Increases generalizability)
???? Generalizability.
???? Replication. (Increases validity)
Quantitative Approaches
• Weaknesses of Quantitative
Approaches
???? Lack of depth. (Breadth at the expense of depth)
???? Abuse of statistics.
???? Data driven research.
???? Measurement issues.
Qualitative and Quantitative Approaches
I. Qualitative Approaches
II. Quantitative Approaches
III. The Qualitative/Quantitative Divide (This split is one of the biggest in the social sciences)
The Qualitative/Quantitative Divide
• Criticisms of Qualitative Approaches
???? Where is the parsimony?
???? Lack of generalizability.
???? Lack of predictive power.
???? Lack of attention to measurement.
???? Replication.
???? Controlling for alternative explanations.
* How representative is your sample?
The Qualitative/Quantitative Divide
• Criticisms of Quantitative Approaches
???? Ignores the richness of politics.
???? Lies, damn lies, and statistics.
???? Some questions defy systematic explanations.
???? Lack of accessibility of quantitative work. (math narrows focus of readership)
The Qualitative/Quantitative Divide
• The reality
???? Both approaches are guided by the same principles. (Provide empirical measure of how the world works)
???? The research question should determine the appropriate method.
???? Regardless of which approach is used, imperfection is a given.
Lecture 13
Inferential Limitations
Inferential Limitations
I. Validity
Validity
• Standards of Evidence
???? After empirical analysis, we are in a position to assess how well the evidence supports the theory.
???? This is a crucial step in the research process.
???? However, there are no magic formulas that can help us to determine this.
???? Rather, we assess the evidence in terms of the inferences and generalizations that we seek to make.
* @ types of conclusions we can draw: 1) Inference 2) Generalization.
Validity
• Inference – what specifically does our research tell us about the world?
???? These are the facts that we put forth, which are drawn directly from our analysis.
???? Inferences are narrowly drawn.
???? We seek to infer from sample to population.
Validity
• Generalization – how do our theory and findings bear on other similar contexts?
???? Generalizations are much more difficult than inferences because we are projecting our findings on to contexts that we did not examine.
???? Generalization is aided by parsimony.
* 2 types of validity: 1) Internal 2) External.
Validity (determines types of conclusions to draw)
• Internal validity
???? Bears on our ability to make inferences.
???? How well does our research design test the causal process posited by our theory?
• Type I Error (false positive). (There is a relationship but it does not occur in the population)
• Type II Error (false negative). (Failed to detect a relationship in the population even though it does occur)
Validity
• External validity (refers to generalizability of our results)
???? Do we expect to find the same causal process at work in other similar contexts.
???? Bears on the generalizability of our research and is shaped by the parsimony of our research. (largely comes from parsimony)
Validity
• Which Is More Important? (Dr. Damore argues that internal validity is more important)
???? Without internal validity neither accurate inferences about our sample cannot be made nor can we make inferences to the population of interest or other contexts in which our theory should be applicable.
???? If we have internal validity, we still may have problems with external validity (e.g., experiments).
???? Assessing internal and external validity is difficult because no rules exist to determine either.
???? Both are shaped by a number considerations.
Inferential Limitations
I. Validity
II. Errors in the Research
Process
* 2 types of errors: 1) Commission 2) Omission.
*Errors can undermine your internal or external validity.
Errors in the Research Process
• Errors of Commission
???? Errors that result from actions taken by the researcher during the research process.
Errors in the Research Process
• Errors of Omission
???? Errors that result from actions that the researcher should have taken during the research process.
Examples of Common Errors
• Focusing the research question. (causality)
• Reviewing the relevant literature. (too little/much focus on prior research)
• Development of theory. (valid assumptions)
• Deriving hypotheses. (failed to develop for each independent variable in a testable manner)
• Operationalization. (precise definition for concepts)
• Measurement.
• Data collection and research design. (what contexts to develop hypotheses)
Inferential Limitations
I. Validity
II. Errors in the Research
Process
III. Dealing With Limitations
Dealing With Limitations
• Self-restraint. (be temperate in language to open opportunity for difference of opinion or conclusions à stress flaws because no research is perfect)
• The review process. (Journals: 1) present at professional conference 2) submit for publication à blind review à editor sends to 3 reviewers you do not know – Books: 1) send prospectus to publisher à multiple reviewers that may or may not be anonymous)
• Iterative research. (Only after bodies of evidence by numerous scholars have researched can it be accepted as common knowledge)
Readings:
1. Wars and American Politics
2. War and the Fate of Regimes
3. A Spiral of Cynicism for Some
1
Lecture 15 – Introduction to Statistics
Introduction to Statistics
I. Statistics and Social Research
II. Measurement Revisited
III. Organizing the Data
Introduction to Statistics
I. Statistics and Social Research
Statistics and Social Research
???? The ubiquity of statistics
???? Most people have an aversion to statistics. (Too difficult, think irrelevant, or problems with math)
???? Statistics are a used in all of the social, behavioral, and biological sciences, as well as in business and by the government.
???? We are focusing on one application of statistics: using statistics to test hypotheses derived from social scientific theories. (We are focusing on only one sector of statistics)
Statistics and Social Research
???? Statistics Defined
???? Statistics are tools and techniques that are used to describe, organize, and interpret data.
???? Anything that can be quantified is potential data.
???? Statistics can be either descriptive or inferential.
Dr. Damore Pet Peeve:
Data = plural
Datum = singular
Statistics and Social Research
???? Statistics in practice
???? Statistical analysis occurs after the other stages of the research process have been completed.
???? However, in practice, many of the issues associated with data analysis are confronted throughout the research process. (Because often the types of analysis we want to perform affect earlier decisions we need to make)
Statistics and Social Research
???? Conceptualization versus doing the math
???? Being able to do the math is less important than understanding the concepts that underlie the math. (More important to understand the theory behind the math)
???? Learning and using statistics is an on- going process that necessitates practice.
Introduction to Statistics
I. Statistics and Social Research
II. Measurement Revisited
Measurement Revisited
???? Levels of Measurement
???? Nominal – classify or categorize. (Easiest to work with)
???? Ordinal – rank or order of categories.
???? Interval – order categories and indicate distances between categories. (Most complicated, but most useful)
- Statistics à assume that the numbers we provide represent the theoretical concept.
Measurement Revisited
???? Importance of Levels of Measurement
???? The level that data are measured at has two important consequences:
???? It determines the type of information and level (amount) of detail that is contained in the data.
???? It dictates the statistical methods that can be employed.
Nominal Data Coding (No ranking à values just differentiate)
Example: Gender
Male = 1
Female = 0
Nominal Data Coding
Example: Religious Affiliation
Catholic = 1
Jewish = 2
Protestant = 3
Muslim = 4
Ordinal Data Coding Example: (Separate categories à ranked on preference)
Attitudes
Ordinal Data Coding Example: (Separate categories)
Education
Ordinal Data Coding Example: (Order to the data, but do not represent exact differences)
Partisan Identification
Simple Interval Data Coding (Ordered with exact differences)
Examples: Age and Education
???? Age measured in years
???? Education measured in years
Complex Interval Data Coding (More commonly used in the social sciences)
Examples: Media Exposure
An index taken from the following NES question:
1. # of days per week respondent watches television news (0-7).
2. # of days per week respondent reads a newspaper (0-7).
3. How closely respondent followed campaign through television news (1-5).
4. How closely respondent followed campaign through newspaper (1-5).
The latter two variables are transformed into eight point scales to make them equivalent to the other two components of the index by subtracting one and multiplying by 7/4. Thus, the scale can range from 0 to 29. The index was then divided by 2.8 to create a scale ranging from 0 to 10.
Introduction to Statistics
I. Statistics and Social Research
II. Measurement Revisited
III. Organizing the Data
- In spreadsheets values for each case are placed in rows across values for each column.
- SPSS or DATA are types of statistical software.
Organizing the Data
???? Frequency distributions. (Helps to get a feel of what the data looks like)
Frequency distributions summarize a distribution of cases by their category value.
- Missing system = the number of respondents that did not answer the question. *These must be dropped from the analysis à the valid percent column takes this into account.
- Frequency distribution à better for ordinal data. Not good for interval data (if it is used you must categorize to make it manageable)
Organizing the Data
???? Frequency distributions.
???? Cross-Tabulations. (Relationship between two variables à better with nominal and ordinal level data)
Cross-Tab of Ideology and Gender (2000 ANES)
- Organization à Total is called “marginals” and the boxes where data appears are called “cells.”
Cross-Tab of Ideology and Gender (2000 ANES)
- Sometimes, percentages are easier to work with.
- Cross-Tabulations can be used for three or more variables, but it becomes difficult.
Organizing the Data
?? Frequency distributions.
???? Cross-Tabulations.
???? Graphical presentations.
The more common types of graphical representations:
Pie Chart of Ideology (2000 ANES)
Bar Graph of Ideology (2000 ANES)
Histogram of Bush Feeling Thermometer (2000 ANES)
- Histograms are related to the bar graph.
Cumulative Distribution of Bush Feeling Thermometer (2000 ANES)
Lecture 16 – Descriptive Statistics
Descriptive Statistics (2 types:)
I. Measures of Central Tendency à averages
II. Measures of Variability à range, standard deviation, etc.
Descriptive Statistics
I. Measures of Central Tendency à Used to learn more about the data.
The Mode (Use where there are categories à no ranking)
???? The mode is the most general and least precise average.
???? The mode is used for nominal data.
???? The mode is the most common value in a distribution.
Calculating the Mode: Partisan Identification Example
???? Raw data:
???? Democrat = 90
???? Republicans = 70
???? Independents = 140
???? The modal category is Independents because that category occurs with the greatest frequency
Unimodal Distribution (Only one high point in the distribution)
Bimodal Distribution (Two high points in the distribution)
The Median (Used at ordinal and interval levels of measurement)
???? The median is the midpoint of a distribution:
???? 50% of data is above.
???? 50% of data below.
???? Used with ordinal and interval data.
???? To calculate:
???? List a set of scores from highest to lowest
?? Find the midpoint
Calculating the Median
???? Raw data = 32, 45, 63, 77,55, 78, 25
???? Re-ordered data = 78, 77, 63, 55, 45, 32, 25 (Re-ordered from highest to lowest)
???? The median = 55
Calculating the Median
???? If there is an even number of values, then the median is the average of the two middle scores.
???? If the two middle most values are the same, then the median is simply that numerical value.
???? Percentiles are an extension of the median. (Percentage of data equal to or below the distribution)
The Mean
???? The mean is the most common measure of central tendency.
???? The mean is used with interval level data.
???? The mean is the arithmetical average.
Formula for the Mean
Comparing Measures of Central Tendency
???? The level of data (AND when most commonly used)
???? Mode – nominal.
???? Median – interval or ordinal.
???? Mean – interval.
???? The shape of the distribution (What can it tell us about the appropriate use according to levels of data)
Normally Distributed Data (The mean, median, and mode are more or less equivalent)
A Skewed Distribution (Use the median with skewed distributions à the mean is not a good measure)
Descriptive Statistics
I. Measures of Central Tendency
II. Measures of Variability (How scores differ from one another)
Variability Example
How Distributions Can Differ in Variability
The Range (This is the least precise measure of variability)
???? Provides a quick, but rough measure of variability.
???? Captures the difference between the lowest and highest values in a distribution.
???? The range is problematic because it only considers the largest and smallest cases.
Formula for the Standard Deviation (The most common measure of variability à measures the average given mean between data)
Calculating the Standard Deviation
1. List each score (the order of the scores does not matter).
2. Compute the mean for the distribution.
3. Subtract the mean from each score.
Calculating the Standard Deviation: Steps 1 - 3
Calculating the Standard Deviation
Calculating the Standard Deviation
6. Divide the sum by n-1(which is 9 in this case): 28/9 = 3.11.
7. Compute the square root to obtain the standard deviation: square root of 2.8 is 1.76.
Understanding the Standard Deviation
???? We do not add the deviations together because the result is zero.
???? We square each deviation to eliminate negative values.
???? The square root is taken to return to the original units.
Interpreting the Standard Deviation
???? The standard deviation tells us on average how far any score is from the mean.
???? Distributions with large standard deviations have greater variability.
???? Distributions with small standard deviations have less variability.
The Variance (Closely related to standard deviation)
???? The variance is closely related to the standard deviation.
???? It is calculated by following the steps of the standard deviation except taking the square root.
???? Because the variance captures variability in squared units, it is not all that useful in and of itself.
???? The variance is used with more sophisticated statistics. (Ex. Correlation, Regression analysis, etc.)
Lecture 17 – Probability
Probability (Lays the basis for statistical significance)
I. Probability and Probability Distributions
II. The Normal Curve
III. Z Scores
IV. The Central Limit Theorem
Probability
I. Probability and Probability Distributions
Probability and Probability Distributions
???? “Probability is expectation founded upon partial knowledge. A perfect acquaintance with all the circumstances affecting the occurrence of an event would change expectation into certainty, and leave neither room nor demand for a theory of probabilities.”
???? “Probability theory is nothing but common sense reduced to calculation.”
- In essence: Study of likelihood that an event will/will not occur given a set of circumstances.
Probability and Probability Distributions
???? Probability Defined
???? Probability is the study of the likelihood that an event will occur given a set of circumstances.
???? With enough information, the probability of any outcome occurring can be calculated.
Probability Distributions
???? Probability distributions
???? Probability distributions capture a theoretical distribution of likely outcomes in a population.
???? The probabilities for each value represent the likelihood of that outcome occurring.
- Frequency distributions focus on samples while probability distributions focus on populations.
Probability versus Frequency Distributions
???? Statistics for probability (population) distributions are symbolized with Greek letters
???? Mean = ? (mu)
???? Standard deviation = ? (sigma)
???? Variance = ?2 (sigma squared)
???? Statistics for frequency (sample) distributions are symbolized with English letters
???? Mean = x
???? Standard deviation = s
???? Variance = s2
Probability
I. Probability and Probability Distributions
II. The Normal Curve (A visual representation of a distribution of scores)
Characteristics of the Normal Curve
- Always symmetrical
- Mean, median, and mode are always the same.
- Tails continue for infinity
The Area Under the Normal Curve
- Cases at the ends of the curve (tail) have less than a 1% chance of occurring.
Probability
I. Probability and Probability Distributions
II. The Normal Curve
III. Z Scores
Formula for Z Scores (Allows us to make comparisons across distributions)
Calculating a Z Scores
* Means à A score of “20” is “3” standard deviations away from the mean of “50.”
What a Z Score Represents
* If the z-score is not a whole number a z-table is available.
The Z Score Table
???? If our z scores are not whole values we need to use the z score table.
???? The table tells us the area under the curve between the mean and any z score.
???? We can use the table to determine the area between two scores as well.
Probability
I. Probability and Probability Distributions
II. The Normal Curve
III. Z Scores
IV. The Central Limit Theorem
The Central Limit Theorem
???? If repeated samples are taken from a population and each sample mean is calculated and plotted, the distribution eventually would resemble the normal curve.
Characteristics of a Sampling Distribution of Means
1. The sampling distribution of means approximates the normal curve.
2. The mean of a sampling distribution of means is equal to the true population mean.
3. The standard deviation of sampling distribution of means is smaller than the standard deviation of the population
Understanding the CLT
???? Most of outcomes occur around the population mean.
???? Such events have a high probability of occurring
???? The farther we move from the mean, the less likely an outcome will occur.
???? Such events have a low likelihood of occurring
The Standard Error of the Mean
???? In practice, researchers draw a single sample.
???? This does not yield information about the population mean or standard deviation.
???? The standard error of the mean provides an estimate of the variation around the population mean.
Formula for the Standard Error of the Mean
Confidence Intervals
???? The standard error of the mean allows one to generate confidence intervals.
???? Confidence intervals are a range in which one might expect to find the true population mean.
???? These intervals allow one to assess the confidence that the sample mean is an accurate estimate of the population mean.
The 68% Confidence Interval for the True Population Mean
The 95% Confidence Interval for the True Population Mean
The 99% Confidence Interval for the True Population Mean
Estimating the Standard Error of the Mean
???? In practice, the standard error of the mean is estimated from sampling data.
???? This adds additional uncertainty beyond that which arises due to sample variability.
???? As a consequence, researchers want to have a wider range in estimating this value. (Purpose of n - 1)
???? This means that instead of using the normal curve, the t distribution is used.
The t Distributions
Estimating the Standard Error of the Mean with Sample Data (This is very important to understand because it is the basis for all interval statistics)
Lecture 18 – Statistical Significance
Statistical Significance
I. The Logic of Statistical Inference
II. Type I and Type II Errors
III. Conducting a Test of Significance
IV. Substantive versus Statistical Significance
Statistical Significance
I. The Logic of Statistical Inference
The Logic of Statistical Inference
???? In an ideal research setting, data from populations would be used to test theories.
???? In nearly all applied research, data from samples are analyzed.
???? However, researchers want to draw conclusions about populations.
The Logic of Statistical Inference
???? Researchers need leeway in how confident they can be in concluding that what was found in a sample holds in a population.
???? Statistical inference is the process by which researchers make this leap.
???? Statistical significance captures the level of risk one is willing to take in making this leap.
Significance Levels (Level of risk you are willing to take of making an incorrect inference)
???? Significance levels capture the likelihood of making an incorrect inference.
???? Significance = .05 means a 1 in 20 chance of making an incorrect inference.
???? Significance = .01 means a 1 in 100 chance of making an incorrect inference.
???? Significance = .001 means a 1 in 1000 chance of making an incorrect inference.
- Why not always use the highest? à If a more difficult threshold is used we may miss certain instances.
Statistical Significance (Tells you the likelihood of making a Type I error)
I. The Logic of Statistical Inference
II. Type I and Type II Errors
Type I and Type II Errors
???? The Null and Research Hypotheses
???? The research hypothesis states the causal relationship between the independent and dependent variables. (This is what we are interested in)
???? The null hypothesis states there is no relationship between the independent and dependent variables. (what occurs in the population not samples)
Type I Errors (Reject the null)
Type II Errors (Accept the null)
Type I and Type II Errors
???? Why Sample Size Matters
???? Large samples are more likely to be representative and are less likely to result in sample specific results.
???? With small samples there are fewer combinations of the values of the variables.
???? This increases the likelihood of obtaining by chance a combination of values suggesting a strong relationship.
The Ratio of Baby Boys to Baby Girls
- 120 babies are born in Hospital 1 each day
- 12 babies are born in Hospital 2 each day
???? On average, the ratio of boys to girls born in each hospital is 50/50
???? One day, twice as many girls were born in one of the hospitals
???? In which hospital is this more likely to occur? (Hospital 2 <12 babies> because it is a small sample)
Type I and Type II Errors
???? Why Sample Size Matters
???? The smaller the relationship the larger the sample needed to detect the relationship.
???? The larger the relationship the smaller the sample needed to detect the relationship.
- We don’t know relationship beforehand à Err on the side of caution and use larger samples.
Statistical Significance
I. The Logic of Statistical Inference
II. Type I and Type II Errors
III. Conducting a Test of Significance
Conducting a Test of Significance
1. State null and research hypotheses.
2. Set the significance level. (Anything above the .05 level is conventionally used)
3. Select the appropriate test statistic.
4. Compute the test statistic.
5. Determine the critical value needed to reject the null.
6. Compare the value of test statistic to the critical value and make a decision.
Comparing the Test Statistic to the Critical Value
Statistical Significance
I. The Logic of Statistical Inference
II. Type I and Type II Errors
III. Conducting a Test of Significance
IV. Substantive versus Statistical
Significance
Statistical versus Substantive Significance
Most common mistake: If one finds statistics that are significant, one assumes there is a substantive significance. A mistake because this only tells the likelihood of case occurring, not the substantive importance (what the numbers mean).
Statistical versus Substantive Significance
???? Statistical significance is a measure of technical success.
???? Substantive significance focuses on the interpretation of the statistical results in the context of our theory.
Lecture 19 – Inferential Statistics I: Difference of Means and ANOVA
Inferential Statistics I
I. Difference of Means (t Test)
II. ANOVA
Inferential Statistics I
I. Difference of Means (t Test)
-Inferential Statistics are used for hypothesis testing.
When Do We Use the Difference of Means or t Test?
???? We want to examine comparisons between groups.
???? The independent variable is nominal or ordinal.
?? The dependent variable is interval.
???? There are only two categories for the independent variable.
???? Equal variances among groups in the population.
???? The difference of means is also known as the t test.
- Are differences due to chance or is there a systematic difference between groups.
- Independent samples that are measured only once is most common with the difference of means test.
Formula for the Difference of Means or t Test
Standard Error of the Difference of Means
Determining the Critical Value
???? After t has been calculated, we need to determine if it is statistically significant.
???? The null is rejected if the value of the test statistic exceeds the critical value associated with the .05 level.
Comparing the Test Statistic to the Critical Value
- The (5% of all values) is rare and unlikely to occur by chance, but more likely to have a systematic differences between the dependent and independent variables.
The Critical Value and Degrees of Freedom (Sample sizes of both groups minus 2)
The t Distributions
- As sample sizes get larger we have more confidence.
Steps to Conducting a Difference of Means or t Test
1. Calculate the mean for each group.
2. Calculate the variance for each group.
3. Calculate the standard error of the difference of means.
4. Calculate the t ratio.
5. Determine the critical value, compare it to the obtained t value, and either accept or reject the null.
6. Interpret the substantive importance of the statistical analysis.
SPSS Output for t Test of Gender Differences in Thermometer Ratings of Bush (2000 ANES)
- Large t values indicate statistical and substantive significance.
SPSS Output for t Test of Racial Differences in Thermometer Ratings of Bush (2000 ANES)
Inferential Statistics I
I. Difference of Means (t Test)
II. ANOVA
When Do We Use ANOVA? (Analysis of variance)
???? ANOVA tests for significant differences in the mean values of the dependent variable for different categories of the independent variables.
???? ANOVA is used when there are more than two categories of the independent variable.
???? The dv is measured at the interval level.
???? The iv is measured at the nominal or ordinal level.
???? Equal variances among groups in the population.
- ANOVA can handle multiple categories in the independent variable.
Within Group Variation
- Difference between group means.
Between Group Variation
- Difference among group means.
ANOVA as an Extension of the t Test
Understanding Sum of Squares
???? ANOVA begins by considering the total variation in the dv.
???? This is the Total Sum of Squares (SStotal)
???? ANOVA separates SStotalinto two categories.
???? Variation in the dv resulting from differences within group (SSwithin)
???? Variation in the dv resulting from differences between groups (SSbetween)
Understanding Sum of Squares
Mean Squares
???? The various sums of squares increase with variation in the data and as sample size increases
???? Because the sums of squares increase with sample size, the number of scores used to calculate the sums of squares needs to be controlled for
???? This is done by dividing the SSwithin and SSbetween by their respective degrees of freedom to yield the mean square between (MSbetween) and mean square within (MSwithin)
Calculating Mean Square Between
Calculating Mean Square Within
The F Distribution
- Also known as a right skewed distribution.
- Tail shows when to reject null.
Calculating the F Ratio (If F value exceeds critical value à reject null)
Steps to Using ANOVA
1. Calculate the mean for each group.
2. Calculate the various sums of squares.
3. Calculate the within groups and between groups mean square.
4. Calculate the F ratio.
5. Determine the critical value, compare it to the obtained F ratio, and either accept or reject the null.
6. Interpret the substantive importance of the statistical analysis.
SPSS Output for ANOVA test of Regional Differences in Thermometer Ratings of Bush (2000 ANES)
Essays:
A. Explain the difference between causation and correlation. In your response be sure to address how the two concepts are differentiated from one another in the research process and provide an example of a correlation that is causal and one that is not
B. Discuss the process by which social scientific theories are developed. In particular, your response should examine how the building blocks of theories (e.g., concepts, variables, assumptions, and paradigms) are synthesized into a coherent framework and how this process is aided by deduction and induction.
Social scientific theories are developed by refining the research question. The purpose of theories is to create possible explanations for observed events. Theories help gain an understanding of reality, provide direction for understanding events, and aid in interpreting events or data. Theories provide context for why the data that is observed exists. Theories are logically related prepositions that represent what we think occur. Theories are crafted. They go beyond description to address the ‘why’ question. Theories begin with a thorough knowledge of what we want to explain (the purpose of literature review). (Lecture 5)
Concepts are the initial building blocks of theories. Theories are composed of sets of concepts that are related by logical propositions. They are a symbol that represents an idea. Concepts are mental constructs that need to be further defined. Once they have been defined and have been associated with specific properties than they become variables. Variables are logical groupings of attributes. Attributes are characteristics that describe an object. Attributes are the categories that make up a variable and represent concepts. Variables must have more than one attribute. Assumptions are the glue that holds a theory together. Assumptions must be logically derived. This is done through deduction and induction. With deduction, assumptions come first. With induction, assumptions are made after observation. To aid in developing reasonable assumptions paradigms may be used. Paradigms are fundamental models used to organize observations and reasoning. (Lecture 6)
This process is aided by the flow of induction and deduction. Induction is used to translate observations into general assumptions. Then deduction is used to develop predictions. These predictions are then tested with new data. Then assumptions based upon the analysis are revised. Then the process is repeated to refine and clarify the theory. Most theories involve an interaction between induction and deduction in this way. (Lecture 5)
C. One way to think about the scientific method is that science seeks to be progressively less wrong. Explain what is meant by this claim and discuss how science seeks to achieve this end.
The statement that science seeks to progressively be less wrong refers to the idea that research is provisional and iterative. (Lecture 1)
Science seeks to achieve being progressively less wrong by implementing controlled characteristics and outputs on scientific knowledge. The characteristics of scientific knowledge include explicit, systematic, controlled, empirical, and objectivity. Scientific knowledge must be explicit in that all rules for defining and examining are predetermined before the process begins. It must be systematic in that each item of evidence is linked by reason or observation by all other items of evidence. It is controlled in that it is observed in as rigorous manner as possible. It is empirical in that it seeks to explain how the world works rather than how it should work. It is objective in that our own objectives do not bias our inquiries. Also, a finding cannot be accepted unless it can be replicated by others, a term referred to as intersubjective testability (Monroe, 2). The controlled outputs of scientific knowledge include being generalizable, predictive, provisional, and iterative. Scientific knowledge is generalizable because it seeks to explain a broad class of phenomenon or a general explanation that can account for all similar types of cases. It is predictive because it seeks to explain what has happened and what to expect in the future. It is provisional because all scientific research is provisionally accepted until it is either disproved or improved by other researchers. It is iterative because no single piece of research is accepted as the final word. Truth is accepted only after a body of evidence develops over time. (Lecture 1)
All of these characteristic and output constraints that have been placed on scientific knowledge help to achieve the goal of being progressively less wrong. Every characteristic and output creates a design of progression. In other words, scientific knowledge is perpetual.
Lecture 8
Operationalization – Concepts - Indicators:
Operationalization is constructed to flow from the more abstract to the more complete – Theories (Concept relations), Hypothesis (Variable Relations), Measurement (Indicator relations), and Research Design (Values on Indicators). (Lecture 8) In other words, operationalization is the process of defining a concept that will measure variables through specific observations. A good example was presented in The Multi-Layered Impact of Public Opinion article. In the article, the researcher was researching the influence that public opinion had on public policy. For the purpose of operationalization, the researcher combined four typical models of opinion representation in policy. This combination formed the Historical Chain Model which focused on interrelationships between policy and public opinion. This combination formed a historical chain of events. In making this combination model, the way the four original models defined and measured public opinion’s influence was discussed as well as the how the combination model was used in operationalization. (Norrander, 773 – 77)
The process of conceptualization is the foundation for measurement. An important element to empirical research is the idea that any concept can be measured through indicators. Concepts can be seen as mental images. It is important to use the best wording possible to describe concepts in order to properly convey its meaning to others. Measurement is the careful observation of the real world for the purpose of describing events and objects in terms of attributes composed of variables. (Lecture 8)
Scientists seek to measure three things: 1) Direct observables 2) Indirect observables, and 3) Constructs. Direct observables can be though of as something that we can observe directly. An example of this would be the direct observation of color. Indirect observables are things that we indirectly observe based on information taken from another source. For example, we may be able to indirectly observe certain things from information gather from survey data. Constructs are theoretical creations based on observations, but they cannot be observed directly. For example, to measure intelligence an IQ test may be administered. (Lecture 8)
Conceptualization gives a definite meaning to a concept by specifying one or more indicators. An indicator allows us to determine the presence or absence of the concept being studied. Therefore, since concepts are descriptions of mental images they are really not measured. Instead, indicators of concepts are measured. Often there are many indicators that can be used to measure indicators of concepts. For example, measuring the indicators of the concept of the current state of the economy may include measurement of indicators such as inflation, unemployment rate, gross domestic product, etc. (Lecture 8)
Conceptual definitions give a definite meaning to the terms we use in research. This meaning builds the construct for the operational definition. This defines the procedures that will result in the empirical observations of these concepts. The will allow us to precisely specify our variables and the indicators to be analyzed. (Lecture 8)
Formulation of Hypothesis:
A hypothesis is the expected probability of the relationship between independent and dependent variables. It is a single sentence statement that captures the theory. Hypotheses explore the causal relationship of a theory. In forming hypothesis it is important to remember that each independent variable must have a separate hypothesis. (Lecture 8)
The formulations of good hypotheses are dependent on certain characteristics. They must be empirical statements, in other words, they seek to explain how the world really works rather than how the world should work. A hypothesis should not focus on specific relationships, but on general relationships. They should be based on logical reasoning. This would be an explanation as to why it is expected that the independent variable will affect the dependent variable. Hypotheses should be stated in specific terms. This can be directional. The term directional means to be stated in a manner showing a positive or negative expected result. The final, and very important characteristic, is that hypotheses must be testable. There must be evidence or data available in the real world that can be collected in order to test whether a hypothesis is correct or incorrect. (Lecture 8)
Nothing is set in stone in the research process. However, an example of a hypothesis exploring the causal relationship of a theory may be; if educational expenditures rise there will be a decrease in the student to teacher ratio, increase the quality of teachers, and an increase in the quality of school facilities. These will, in turn, improve student performance. (Lecture 8)
Lecture 9
Measurement:
Measurement is the process by which we develop rules for defining what the indicators of variables are. This is important because it is the process by which the rules and procedures for defining indicators for theoretical concepts. It is also impossible to reach empirical validation without valid and reliable measurement. (Lecture 9)
For many commonly used concepts measurement is not given much thought. We take for granted things like the system of weights and measures (ex. Metrics are used in Europe) and the measurement of days (ex. the calendar). This is a problem throughout the scientific world. Therefore, measurement is of great importance when working with abstract concepts. Measurement provides the operational choices used to develop empirical indicators. (Lecture 9)
Defining categories and precision are very important for measurement. Conceptual and operational definitions specify variables and their attributes. An example could be the measurement of political affiliation. Attributes composing a variable should be exhaustive (ex. Republican, Democrat, or Independent). Further still, they should be mutually exclusive (ex. only one category, ex. Republican). Precision focuses on the amount of information about a concept. Higher levels of measurement possibly provide more information about the concept. The level of measurement also has implications in the use of statistics. (Lecture 9)
There are three levels of measurement: 1) Nominal 2) Ordinal, and 3) Interval. “The term level of measurement refers to the classifications or units that result when a variable has been operationally defined” (Monroe, 83). Nominal levels of measurement are variables that are mutually exclusive and exhaustive, such as categories. It just places the variable in several unordered categories. Nominal levels of measurement are considered the lowest level of measurement or the least precise. Nominal variables contain information on “what kind” rather than “how much.” (Monroe 83 – 4) Ordinal levels of measurement are variables that can be logically ranked. In this case, there is an order, but there is not a precise difference. Ordinal variables can take two forms: ranked order or ordered categories. Ranked order puts the case in exact order according to some characteristic. Ordered categories put variables into categories, but the categories have an inherent order. (Monroe, 84) Examples might be ranking order of population (ranked order) or determining age in terms of decades (order of categories). Interval levels of measurement are ideal as they are considered to be the highest levels of measurement (Monroe, 85). They are levels of measurement that indicate ranking and specify the exact difference between categories. This gives the actual distance separating attributes. An example would be determining age in terms of years. Monroe also measures one other level of measurement called ratio scale. However, it is not discussed further because ratio scale and interval levels of measurement rarely make an important difference in social statistics. (Lecture 9)
There are two main rules for using levels of measurement. 1) “Down, but not up.” This means a variable can be measured at a lower level of measurement, but it can never be treated at a higher level. 2) “Dichotomies are wild.” This means that a variable with only two possible values can be treated at any level of measurement. (Monroe, 89)
Measurement is centered on reliability and validity. A measure is reliable to the extent that it gives the same results when repeated. In other words, the primary concern is consistency. The social sciences data are rife with sources of unreliability. For example, attitudinal measurement may cause problems due to different interpretations. For this reason, the prior development of precise conceptual and operational definitions is the best way to avoid unreliable measures. In the testing of reliability there are two methods: 1) Test-retest 2) Multiple coders. The test-retest method is the process of repeating the measurement a second time. In doing so, the same results should occur. The multiple coder method is when different individual measure the same concepts and then inter-coded reliability check are performed. In measurement, validity is of more concern than reliability. The primary concern of validity is accuracy. It is concerned with how accurately the measure captures the theoretical concept. Testing for validity is much more difficult than testing for reliability. In doing so, there are four methods: 1) Face validity 2) Construct validity 3) Discriminant validity, and 4) Pragmatic validity. Face validity is the most common, but is less rigorous. It ask the question, on its face does the measure seem valid (self-evident)? Construct validity tries to determine whether the measure performs as expected in relation to other concepts. Discriminant validity seeks to know how the measure differs from the indicators and other concepts it is related to. Pragmatic validity is comparing how well the indicator performs when compared to another measure that is known to be valid. (Lecture 9)
No measurement is 100% accurate. The two sources of measurement error are: 1) Random error, and 2) Systematic error. Random error stems from reliability. In this case, it defies data randomly. Systematic error stems from validity. This is error that is of much more concern. This is more problematic because it defies all data. The goal of a researcher is to eliminate systematic error and to minimize random error. (Lecture 9)
Example of Measurement & Indexes – Scales – Typologies (Study guide pgs. 6 – 9)
Lecture 10
Research Design: (Role & Importance)
The research design seeks to determine how the research tends to fulfill the goal of the proposed study. It provides the necessary empirical evidence to evaluate the use of a theory. In each stage of a research project there is continual refinement. It is an imaginative process not something that can be inflexible be adhered to like a recipe from a cookbook. The research design is very important because is can help or destroy the entire research process. (Lecture 10)
The research design is where causal relationships are formally evaluated. Causality is empirical research that is concerned with assessing causal relationships. Causality can only come from a theory. There are some criteria for establishing causality. First, the variables have a covariational relationship (they are empirically correlated). Second, the cause must come before the effect. Third, the causal link can be logically founded. Finally, there is not a spurious relationship; that is, the empirical relationship between the variables cannot be explained by a third variable. In scientific research there are some false criteria for establishing criteria. First, complete causation; social science does not seek to completely explain causality due to parsimony. Instead, it is important to identify that variable that has the strongest effect. Second, exceptional cases; just because certain cases do not conform to our theoretical expectations does not means there is not a causal relationship. Finally, majority of cases; causal relationship can be true even if it does not apply to a majority of the cases. (Lecture 10)
A well formulated research design will anticipate alternative explanations that could account for causality. This can be covered by developing an alternative hypothesis that would predict the same outcome, but convey a different causal process. This is usually done by controlling for an additional independent variable. Sources for controlling for an alternative explanation include paste research, our own thinking, and the review process. (Lecture 10)
The components to the research design are: 1) the statement of research hypotheses to be tested, 2) discussion of sample and data sources, 3) discussion of data collection, 4) precise definitions of the indicators used to measure each variable, and 5) discussion of how the data will be analyzed. (Lecture 10)
Research Design: (Templates)
Research design templates are hinged on capturing time and/or space. Time refers to a process that develops over a period of time. Space refers to what is occurring in a particular place at a particular point in time. (Lecture 10)
The basic experimental design is strengthened by its maximum leverage of causality. There are three important elements: 1) a pre-test and post-test of dependent variables to measure change, 2) an experimental and a control group – the experimental experiences the dependent variable and the comparative category (control) does not, and 3) the independent variable is administer at the will of the researcher. (Lecture 10)
Cross sectional design is the most common because of its reliance on survey data. The independent and dependent variables are measured one at a time. The distribution of the independent variable creates quasi-experimental and control groups. Quasi-experimental design is the taking of the logic of experimentation and applying it to non-experimental situations. The variation of the independent variable is used to measure the variations in the dependent variable. Cross sectional design has a number of strengths including data being collected in a natural setting, large and representative samples, and the control over alternative explanations through the use of statistics. It also has some weaknesses including limited control over causality, less precision in developing preferred measures, and no point of comparison. (Lecture 10)
Longitudinal studies examine the same phenomena over a period of time. It is used in both qualitative and quantitative research. There are three types of longitudinal designs: 1) Time series studies, 2) Cohort studies, and 3) Panel studies. (Lecture 10)
Time series designs are interested in examining changes in trend that occur over time with the introduction of the independent variable. Time series design represents quasi-experimental and control groups. This design requires numerous measures of the dependent variable before and after the introduction of the independent variable. However, there are problems with aggregated data. This type of design is commonly used in public opinion studies. (Lecture 10)
Cohort designs examine specific subpopulations over a period of time. This type of study is common in sociology. The same subjects are not analyzed in waves, but instead different samples from the subpopulation are taken. (Lecture 10)
Panel studies are similar to Cohort designs. The main difference is that with Panel studies the same subjects are analyzed in waves. This allows the better assessment of causality. However, measurement error, cost, and panel morality is of great concern. (Lecture 10)
There are also a number of advanced designs which attempt to increase validity. They are aimed at overcoming the limitations of Cross Sectional designs and Longitudinal designs. It important to remember that the research design is not chosen, rather it is created in a manner that is suitable for the purpose determined by creativity, resources available, and ethical concerns. (Lecture 10)
Lecture 11 (Monroe Chapter 4 for sources into existing texts)
Sampling and Data Sources:
Sampling is used in all research, not just in surveys. Every research design involves sampling. Population and case are terms common to sampling. A population is the universe of relevant observations. A case is a single observation that is taken from a population. Samples can be limited by data and resources (cost). For example, if research focused on campaign finance data is only available as far back as the 1970s. The ease of gathering samples largely dictates the type of research pursued. Researchers are often interested in populations so large that it would be impossible to interview all of the members. So, a research will take a sample of the population. (Monroe, 67) What is learned from the sample is then used to make inferences about the population. (Lecture 11)
The characteristic of a good sample is a sample that is representative of the population. A representative sample is one that contains every attribute of interest to the population. It is also approximately proportionate to the occurrence of each attribute in the population. (Lecture 11)
Two sampling methods are probability sampling and non-probability sampling. Probability sampling is the preferred method. This is because, if it is completed correctly, it produces representative samples. In this method every element has a known chance of being sampled. Random selection of the sampling frame is then used. This method is particularly attractive because it allows for a margin of error to be calculated. Non-probability sampling is not as desirable. In non-probability sampling a margin of error cannot be calculated, each element of the population has an unknown chance of being selected, and it tends to be less representative (and often biased). Examples include volunteer subjects (ex. Students volunteer for a professors study), haphazard samples (ex. people just happen to be where you were taking the sample), quota samples (ex. The sample must meet certain quotas like age which is, of course, not random), and purposive samples (ex. Seeking out the sample). In non-probability sampling, purposive samples are the only type that should be employed. (Lecture 11)
The process of sampling is three-fold. First, the population of interest must be identified. This should be who the researcher is trying to learn about as defined by the research question. An example would be the people who are likely to vote on an election day. Second, the sampling frame must be selected. This is a list of the target population from which the sample is drawn (ideally, this is the same as the population). An example may be the list of phone numbers of registered voters. The final step is to draw the sample by using the probability sampling method. Often, this can be accomplished via random digit dialing and screening questions. (Lecture 11)
Today, sampling is most common via telephone sampling. The downside of this is known as respondent fatigue (the person hangs up). The majority of samples come from random digit dialing. Originally, this method was suspect because it was unlikely for the poor to have telephones, but today 98% have telephones. The problem today is the result of technological changes such pagers and faxes. This can affect response rates because of the difficulty associated with contacting certain people which can lead to underrepresented people. (Lecture 11) However, personal interviews are usually considered to be a higher quality of measurement. This is because respondents in personal interviews have been found to be more at ease, better understand the questions, and are more likely to express preferences. (Monroe, 71)
When a sample is biased due to problems with the response rate it can be weighted. Weighting is when some respondents are weighted more or less so the overall sample reflects the population. This makes the assumption that the sampled respondents are representative of the unsampled respondents. (Lecture 11)
Sampling is never 100% accurate. A major criticism is the wondering of how a small sample can accurately represent the views of a large population. The accuracy that is lost is due to sample size and procedure. There are two sources of error. One type is known as systemic error which is built into the design that systemically biases results. This is bad because it biases the entire sample. The other type is random error. Random error is the margin of error due to the sampling procedure. There are several examples of sampling error. The 1936 Literary Digest presidential poll was flawed because it did not use a representative sample. This happened because they used car registrations and phone numbers to collect their sample. This was a time of economic depression so only the wealthy were sampled. As a result, it was projected that FDR would lose the election which he won. Another sampling error occurred in the 1948 presidential election. In this case the sampling method was correct, but the collecting of the sample was stopped too early. (Lecture 11)
Data sources are commonly drawn from survey research. The advantages to this are that there are a large number of observations, the sample is representative, it is quantifiable, and it is easy to use. Government documents such as crime statistics can be used. This is good because the sample is representative and is already in a quantified format. However, there is no way for the researcher to check validity. Direct observation is often used in other social sciences such as psychology. The strength is that the observation takes place in a natural setting. The problem arise because it is unclear on how representative it is, ethical issues, and concerns of objectivity. Another data source may be primary documents. This source is often used with quantitative work. The weakness is regarding reliability and validity. Content analysis can be another source. A final source includes interviews. Interviews are strong data sources because they include comprehensive information with multiple perspectives. The drawback is that the sample is not representative. (Lecture 11)
The problems associated with survey research lay in question development. There are nine rules that should be followed in writing survey items: 1) Respondent must be competent to answer, 2) Avoid biased or emotional language, 3) Avoid leading questions, 4) Short and simple questions are best, 5) Do not state questions in the negative, 6) Avoid unfamiliar language, 7) Avoid ambiguous questions, 8) Minimize threats, and 9) Avoid double-barreled questions. (Monroe, 75 – 76)
Lecture 12
Qualitative and Quantitative Approaches:
Qualitative approaches, also referred to as case study methods, are interested in studying a case in its entirety. This tends to lead to the use of thick descriptions. A case study can be seen as a setting or group which the researcher treats as an integrated social unit. That unit is to be studied in depth. Indeed, qualitative approaches trade breadth for depth. (Lecture 12)
There are several characteristics of qualitative methods. They focus on a small number of cases. The method generally uses large units of analysis; for example, states or countries. It seeks to completely explain each case. Importance is given to studying phenomena in the context in which they occur. Qualitative approaches also focus on description and detail rather than numerical indicators. (Lecture 12)
Some examples of the use of qualitative approaches include: 1) Participant observation, 2) Intensive interviewing, and 3) Focus groups. Participant observation is ingrained in field research. The researcher gets immersed into the case. In doing so, the researcher develops a relationship with the people as they carry on with their normal activities. With this type of approach, observation can be conducted covertly or overtly. In covert observation the researcher blends in and does not divulge their role as a researcher for the purpose of gaining better quality data. Overt observation is an approach in which everyone knows that the researcher is conducting research. Intensive interviewing used open-ended, unstructured questioning to uncover in-depth information on the interviewee’s feelings, experiences, and perceptions. This approach is much less structured when compared to a survey, but it accomplishes a greater interaction between the subject and the interviewer. Focus groups are informal and unstructured discussion lead by the researcher. In these unstructured interviews the researcher encourages discussion between participants on particular topics. Focus groups seek to replicate the natural process of forming and expressing opinions. The problem is that samples can be unrepresentative which can undermine generalizability. (Lecture 12)
There are five main reasons to use qualitative approaches. First, they can be used to test theories. Second, they can be useful in creating theories. Third, they can be used to recognize antecedent conditions. Fourth, they can be used to test the importance of the antecedent conditions. Finally, a reason to use qualitative approaches is in the examining of cases of great importance. (Lecture 12)
Study guide p. 21 – Qualitative approaches in relation to the research process
The research design tests both the theory and alternative explanations. Qualitative approaches attempts to control for these rival explanations through case selection. The two ways this is accomplished is through choosing the most similar cases approach or the most different cases approach. (Lecture 12)
Quantitative approaches, also known as large n studies, also have a number of characteristics. Quantitative approaches rely on numbers and statistics to examine political occurrences. There is an emphasis on the measurement of theoretical concepts. These approaches seek to explain things in a parsimonious and generalizable manner. Quantitative approaches often trade depth for breadth. (Lecture 12)
The goals of quantitative approaches are as follows. They test theories by examining patterns within the data. This concern is not on a single case rather than a general pattern. Quantitative approaches are probabilistic. They seek to generalize back to populations through parsimonious theories and representative samples. (Lecture 12)
When using quantitative approaches, large n studies, there is not magic number in how large the study needs to be. With this approach the larger the study the better. It also depends on the statistical methods used. In order to meet assumptions it is usually wise to have at least 120 cases. However, even well-financed studies rarely exceed 2,000 cases due to statistical confidence (Monroe, 68). (Lecture 12)
Study guide p. 23 – Quantitative approaches in relation to the research process
Quantitative approaches have strengths and weaknesses. Some strength includes the controlling for alternative explanations, having representative samples (this increases generalizability), generalizability, and replication (this increases validity). Some weaknesses include a lack of depth (because it trade for breadth), possible abuse of the statistics, data driven research, and issues with measurement. (Lecture 12)
There is an ongoing debate between researchers between the use of qualitative methods or quantitative methods. Researchers who advocate the quantitative approaches may argue the lack of parsimony, generalizability, predictive power, attention to measurement. They may also raise questions about replication, controlling for alternative explanations, and how representative the sample is. The researchers that have an affinity for qualitative approaches have their own criticisms of the quantitative approach. They may say it ignores the richness of politics. People that are mathematically deficient may reduce the accessibility of quantitative research narrowing the focus of readership. Questions may arise regarding the statistical components of the research. Regardless of the in-fighting, both approaches are guided by the same basic principles. They both seek to empirically explain how the world works. In reality, the rule of thumb is that the research question should determine the appropriate method. Based on this, there will always be imperfections regardless of which approaches are used. (Lecture 12)
Lecture 13
Inferential Limitations:
Validity seeks to bring standards to evidence. In conducting empirical analysis it is possible to assess how well evidence supports a theory. This is essential in the research process. There is no formula to help assess evidence. So, to assess evidence focused is placed in terms of the types of conclusions to draw; inferences and generalizations. (Lecture 13)
Inferences ask what specifically the research explains about the world. These are the facts presented which are drawn directly from the analysis. Inferences are narrowly drawn. A researcher seeks to infer from the sample to the population. (Lecture 13)
Generalization asks how the theory and findings stand up to similar contexts. Generalizations are much more difficult than inferences. This is due to the projecting of findings on to contexts that have not been examined. However, generalization is aided by parsimony. (Lecture 13)
Validity determines the types of conclusions to draw. There are two types of validity, internal and external. Internal validity stands on the ability to make inferences. It seeks to answer how well the research design test the causal process put forward by the theory. Internal validity can face Type I error or Type II error. Type I error, a false positive, shows that there is a relationship, but it does not occur in the population. Type II error, a false negative, fails to detect a relationship in the population even though it does occur. External validity refers to the generalizability of the results. It seeks to answer if the same causal process of other similar contexts is expected to be found. External validity stands on generalizability of the research and is largely shaped by the parsimony of the research. (Lecture 13)
Both internal and external validity are important, but it can be argued that internal validity is more important. Without internal validity accurate inferences about the sample cannot be made. It is also not possible to make inferences about the population or the causal process of other similar contexts that the theory could apply to. Even with internal validity, external validity issues may exist. For example, experiments that are valid in a lab are not valid in the real world. There are really no rules, so it is difficult to determine either type of validity. (Lecture 13)
There are two types of errors in the research process that can undermine internal or external validity. Those errors are errors of commission or errors of omission. Errors of commission result from actions taken by the researcher during the research process. Errors of omission are the result of actions that the researcher should have taken during the research process. (Lecture 13)
Some common errors may occur. When focusing the research question a common error centers on causality. In conducting the literature review there may be too much or too little focus on prior research. During the development of the theory valid assumptions are important. A common error in hypotheses is failing to develop for each independent variable in a testable manner. With operationalization there must be precise definitions for concepts. Common errors can occur in measurement. In data collection and the research design it must be considered in what contexts to develop hypotheses. (Lecture 13)
There are a few ways to deal with limitations. One way is the use of self-restraint. This is the exercise of using temperate language to allow for the opportunity for a difference of opinion or conclusion. Self-restraint should also focus on stressing flaws in the research because no research is perfect. The review process is another way to deal with limitations. Scholarly journals, for example, employ a process that often begins with a presentation at a professional conference, then submittal for publication, then a blind review in which the editor sends the work to three anonymous reviewers. A final way to deal with limitations is that research is iterative. Only after bodies of evidence by numerous scholars have researched a topic can it then be accepted as common knowledge. (Lecture 13)
Lecture 14
The Ethics of Social Research:
Ethics are the rules that dictate appropriate behavior related to an activity. Ethics are involved in virtually every action humans are involved in. Ethics touch on morality through defining what is considered “right” or “wrong.” This underlying morality can be rooted in a number of sources such as religion, common sense, political ideology, etc. Ethics derive from a common understanding as to the code of conduct in a group or profession. (Lecture 14)
In the social sciences, ethics are shared among researchers as to the correct and incorrect in conducting research. These ethics focus on three concerns: 1) subjects, 2) reporting, and 3) application. (Lecture 14)
Ethics as related to subjects are in place to make sure the subject is not being taken advantage of. No harm should come to subjects. In the social sciences, physical damage is unlikely, but psychological damage could occur possible causing a subject to question their own morality or self-worth. Participation must be voluntary. This is a way to reduce the exposure of researchers to the possibility of subject harm. A researcher could provide written information about the study and give it to subjects beforehand. This raises ethical issues between covert and overt research. The subjects of research should remain anonymous and confidential. No one should be identified by their responses. This is usually accomplished by using codes and numbers for names and identifiers. This is especially important because scientific research is not considered to be privileged information in a court of law. Also, it is never acceptable to be deceptive. If the true purposes of a study are hidden to avoid canned responses it must be justified on scientific grounds. Even in such a case, the subjects must be de-briefed fully disclosing the purpose of the study after it is completed. (Lecture 14)
Ethics in reporting refers to reporting the shortcomings of the research. This is usually included in footnotes or an appendix. In research it is important to report both positive and negative results. Even though negative results may be more difficult to get published, both types of findings can contribute to knowledge. In reporting, a researcher must not misrepresent the research process. It is unethical to present unexpected results as if they were a pre-planned hypothesis. Submission to journals and publishers also has ethical guidelines. Work should be submitted to a single journal at a time. This avoids multiple acceptances in multiple journals. However, with book publishers it is okay to send a manuscript to multiple publishers. Fellow scientists have an obligation in how research is conducted and how that research is reported. With these ethical guidelines, science should progress through honesty rather than deception. (Lecture 14)
Ethics in application focus on what to do with the knowledge created. Application is rife with ethical debate. Should certain findings be made available and allowed to be commonly practiced such as human cloning or nuclear weapons? Usually these concerns are hashed out between the researcher, the parties affected, and the politicians because there are no set rules. However, it is common for political considerations to overpower science. (Lecture 14)
Study Guide p. 29 - Institutions for Insuring Ethics
Actual Essays:
A. Explain the process by which social scientific theories are operationalized and how errors of omission and errors of commission at each stage of this process can undermine the internal and external validity of a researcher’s efforts.
C. Explain the purpose of ethics in the social scientific research proces, the primary areas of the research process where ethics come into play, and the process by which these ethical obligations are implemented and enforced.
A. Operationalization is constructed to flow from the more abstract to the more complete – Theories (Concept relations), Hypothesis (Variable Relations), Measurement (Indicator relations), and Research Design (Values on Indicators). (Lecture 8) In other words, operationalization is the process of defining a concept that will measure variables through specific observations. A good example was presented in The Multi-Layered Impact of Public Opinion article. In the article, the researcher was researching the influence that public opinion had on public policy. For the purpose of operationalization, the researcher combined four typical models of opinion representation in policy. This combination formed the Historical Chain Model which focused on interrelationships between policy and public opinion. This combination formed a historical chain of events. In making this combination model, the way the four original models defined and measured public opinion’s influence was discussed as well as the how the combination model was used in operationalization. (Norrander, 773 – 77)
The process of conceptualization is the foundation for measurement. An important element to empirical research is the idea that any concept can be measured through indicators. Concepts can be seen as mental images. It is important to use the best wording possible to describe concepts in order to properly convey its meaning to others. Measurement is the careful observation of the real world for the purpose of describing events and objects in terms of attributes composed of variables. (Lecture 8)
Scientists seek to measure three things: 1) Direct observables 2) Indirect observables, and 3) Constructs. Direct observables can be though of as something that we can observe directly. An example of this would be the direct observation of color. Indirect observables are things that we indirectly observe based on information taken from another source. For example, we may be able to indirectly observe certain things from information gather from survey data. Constructs are theoretical creations based on observations, but they cannot be observed directly. For example, to measure intelligence an IQ test may be administered. (Lecture 8)
Conceptualization gives a definite meaning to a concept by specifying one or more indicators. An indicator allows us to determine the presence or absence of the concept being studied. Therefore, since concepts are descriptions of mental images they are really not measured. Instead, indicators of concepts are measured. Often there are many indicators that can be used to measure indicators of concepts. For example, measuring the indicators of the concept of the current state of the economy may include measurement of indicators such as inflation, unemployment rate, gross domestic product, etc. (Lecture 8)
Conceptual definitions give a definite meaning to the terms we use in research. This meaning builds the construct for the operational definition. This defines the procedures that will result in the empirical observations of these concepts. The will allow us to precisely specify our variables and the indicators to be analyzed. (Lecture 8)
Validity determines the types of conclusions to draw. There are two types of validity, internal and external. Internal validity stands on the ability to make inferences. It seeks to answer how well the research design test the causal process put forward by the theory. Internal validity can face Type I error or Type II error. Type I error, a false positive, shows that there is a relationship, but it does not occur in the population. Type II error, a false negative, fails to detect a relationship in the population even though it does occur. External validity refers to the generalizability of the results. It seeks to answer if the same causal process of other similar contexts is expected to be found. External validity stands on generalizability of the research and is largely shaped by the parsimony of the research. (Lecture 13)
There are two types of errors in the research process that can undermine internal or external validity. Those errors are errors of commission or errors of omission. Errors of commission result from actions taken by the researcher during the research process. Errors of omission are the result of actions that the researcher should have taken during the research process. (Lecture 13)
Some common errors may occur in the process. When focusing the research question a common error centers on causality. Also, in conducting the literature review there may be too much or too little focus on prior research. During the development of the theory valid assumptions are important in avoiding common error. A common error in hypotheses is failing to develop for each independent variable in a testable manner. With operationalization there must be precise definitions for concepts. Common errors can occur in measurement. In data collection and the research design it must be considered in what contexts to develop hypotheses. (Lecture 13)
There are a few ways to deal with limitations. One way is the use of self-restraint. This is the exercise of using temperate language to allow for the opportunity for a difference of opinion or conclusion. Self-restraint should also focus on stressing flaws in the research because no research is perfect. The review process is another way to deal with limitations. Scholarly journals, for example, employ a process that often begins with a presentation at a professional conference, then submittal for publication, then a blind review in which the editor sends the work to three anonymous reviewers. A final way to deal with limitations is that research is iterative. Only after bodies of evidence by numerous scholars have researched a topic can it then be accepted as common knowledge. (Lecture 13)
C. Ethics are the rules that dictate appropriate behavior related to an activity. Ethics are involved in virtually every action humans are involved in. Ethics touch on morality through defining what is considered “right” or “wrong.” This underlying morality can be rooted in a number of sources such as religion, common sense, political ideology, etc. Ethics derive from a common understanding as to the code of conduct in a group or profession. (Lecture 14)
In the social sciences, ethics are shared among researchers as to the correct and incorrect methods in conducting research. These ethics focus on three concerns: 1) subjects, 2) reporting, and 3) application. (Lecture 14)
Ethics as related to subjects are in place to make sure the subject is not being taken advantage of. No harm should come to subjects. In the social sciences, physical damage is unlikely, but psychological damage could occur possible causing a subject to question their own morality or self-worth. Participation must be voluntary. This is a way to reduce the exposure of researchers to the possibility of subject harm. A researcher could provide written information about the study and give it to subjects beforehand. This raises ethical issues between covert and overt research. The subjects of research should remain anonymous and confidential. No one should be identified by their responses. This is usually accomplished by using codes and numbers for names and identifiers. This is especially important because scientific research is not considered to be privileged information in a court of law. Also, it is never acceptable to be deceptive. If the true purposes of a study are hidden to avoid canned responses it must be justified on scientific grounds. Even in such a case, the subjects must be de-briefed fully disclosing the purpose of the study after it is completed. (Lecture 14)
Ethics in reporting refers to reporting the shortcomings of the research. This is usually included in footnotes or an appendix. In research it is important to report both positive and negative results. Even though negative results may be more difficult to get published, both types of findings can contribute to knowledge. In reporting, a researcher must not misrepresent the research process. It is unethical to present unexpected results as if they were a pre-planned hypothesis. Submission to journals and publishers also has ethical guidelines. Work should be submitted to a single journal at a time. This avoids multiple acceptances in multiple journals. However, with book publishers it is okay to send a manuscript to multiple publishers. Fellow scientists have an obligation in how research is conducted and how that research is reported. With these ethical guidelines, science should progress through honesty rather than deception. (Lecture 14)
Ethics in application focus on what to do with the knowledge created. Application is rife with ethical debate. Should certain findings be made available and allowed to be commonly practiced such as human cloning or nuclear weapons? Usually these concerns are hashed out between the researcher, the parties affected, and the politicians because there are no set rules. (Lecture 14)
It is common for political considerations to overpower science. This was exemplified in the article The Politics of Government-Funded Research. Representative Bill Clay attacked research science to its core as a result of fear over a particular study. The study, Candidate Emergence Study (CES), was aimed at studying the decision-making process of potential candidates. Representative Clay perceived this study as an effort in candidate recruitment and used his power in Congress to deter studies like these in the future. He did this by making it clear that government funded research better be careful in studying the body that provides the money. (Maisel & Stone) However, there are a few institutions for insuring ethics: the peer review process, institutional review boards, and professional codes of ethics. (Lecture 14)
