POLS 7000X Statistics in political science Class 1 Brooklyn College – CUNY Shang E. Ha

1. POLS 7000XStatistics in political scienceClass 1Brooklyn College – CUNYShang E. Ha Leon-Guerrero and Frankfort-Nachmias, Essentials of Statistics for a Diverse Society, Chapter 1

2. Empirical Research • What is Empirical Research? • Research based on information that can be verified by using our direct experience. • A research… • That poses a question • That constructs hypotheses (i.e., potential answers) • That creates, gathers, and analyzes data • That tests hypotheses – correct or wrong? • That cumulates knowledge • Not only in political science but also in any other social science disciplines

3. Asking a Research Question • To answer research questions we cannot rely on speculation, moral judgment, or subjective preference • Instead, we create a set of potential answers (i.e., hypotheses) and see whether data support our hypotheses by carefully analyzing them • Empirical (a “Why” question): • Why do some people commit suicide (and others don’t)? • Why are some individuals favorable to homosexuality (and others are not)? • Why do some people vote in the mayoral elections (and others don’t)? • Not Empirical: • Is racial equality good for society? • Should death penalty be abolished?

4. Units of Analysis The level of social life on which social scientists focus (individuals, groups). Examples: • Individual as unit of analysis: • Why are some people politically liberal while others are conservative? • Cityas unit of analysis: • Why is crime rate higher in some cities than others? • Countryas unit of analysis: • What determines income inequality across different countries?

5. Formulating the Hypotheses • Hypotheses: • Tentative answers to research questions (subject to empirical verification) • A statement of a relationship between characteristics that vary (variables) • Variable: • A property of people or objects that takes on two or more values • Must include categories that are both exhaustive and mutually exclusive • Examples: Social class, age, gender, income

6. Types of Variables • Dependent • The variable to be explained (the “effect”). • Should be identified in our research question • Why are some people favorable to homosexuality? • Dependent variable – attitudes toward homosexuality • Independent • The variable expected to account for (the “cause” of) the dependent variable. • Should be identified in our hypothesis • The more educated are more likely to harbor favorable attitudes toward homosexuality • Independent variable – level of education

7. The Role of Theory • A theory is an explanation of the relationship between two or more observable attributes of individuals or groups. • Social scientists use theory to attempt to establish a link between what we observe (the data) and our understanding of why certain phenomena are related to each other in a particular way.

8. Hypothesis: An Example • Research Question: What determines white Americans’ attitudes toward immigrants from Latin American countries? • Hypothesis: White Americans who live in an area populated by a significant proportion of immigrants are more likely to harbor favorable attitudes toward them than those who live in another area where few immigrants reside • Theory: Contact Theory (contact makes friends) • Dependent variable: attitudes toward immigrants (favorable – hostile) • Independent variable: residential segregation (high: no immigrants – low: many immigrants)

9. Cause and Effect Relationships Cause and effect relationshipsbetween variables are not easy to infer in the social sciences. Causal relationships must meet three criteria: • The cause has to precede the effect in time • There has to be an empirical relationship between the cause and effect • This relationship cannot be explained by other factors

10. Guidelines for Independent and Dependent Variables • The dependent variable is always the property you are trying to explain; it is always the object of the research. • The independent variable usually occurs earlier in time than the dependent variables. • The independent variable is often seen as influencing, directly or indirectly, the dependent variable.

11. One Hypothesis, Many Hypotheses • Social phenomena are complex • Most of the social phenomena require researchers to assess the effects of several independent variables on one dependent variable • One independent variable usually explains only a certain amount of the change in the values observed in the dependent variable; hence, additional independent variables have to be introduced in order to explain more of that variation.

12. Hypothesis: People who attend church regularly are more likely to oppose abortion than people who do not attend church regularly. Example 1 • Identify the IV and DV • Independent variable: • Dependent variable: • Church attendance Attitudes toward abortion • Identify other independent variables • Gender • Age • Religious affiliation (Catholic, Jewish, Methodist, Islamic…) • Political party identification • Are the causal arguments sound? • e.g. Does party id affect abortion views or vice versa?

13. Example 2 Hypothesis: The number of books read to a child per day positively affects a child’s word recognition. • Identify the IV and DV • Number of books read • independent variable: • dependent variable: Word recognition • Identify other independent variables • Older siblings • Gender Birth order Health status • Are the causal arguments sound? • Most likely. It is hard to construct an argument where a 36 month old child affects the number of books her/his parent reads to her/him.

14. Collecting Data Examine a social relationship, study the relevant literature Formulating the Hypotheses Asking the Research Question Contribute new evidence to literature and begin again Develop a research design THEORY Collecting Data Evaluating the Hypotheses Analyzing Data

15. Collecting Data Researchers must decide three things: • How to measure the variables of interest • How to select the cases for the research • What kind of data collection techniques to use

16. Levels of Measurement Not every statistical operation can be used with every variable. The type of statistical operations we employ will depend on how our variables are measured. Variables are measured in three ways: Nominal Ordinal Interval-Ratio

17. Nominal Level of Measurement Numbers or other symbols are assigned to a set of categories for the purpose of naming, labeling, or classifying the observations. • Examples: Political Party (Democrat, Republican, Independent) Religion (Catholic, Jewish, Muslim, Protestant) Race (African American, Latino, Native American)

18. Ordinal Level of Measurement Nominal variables that can be ranked from low to high. • Example: Social Class Upper Class Middle Class Working Class

19. Interval-Ratio Level of Measurement Variables where measurements for all cases are expressed in the same units. (Variables with a natural zero point, such as height and weight, are called ratio variables.) • Examples: Age Income SAT scores

20. Cumulative Property of Levels of Measurement Variables that can be measured at the interval-ratio level of measurement can also be measured at the ordinal and nominal levels. However, variables that are measured at the nominal and ordinal levels can’t be measured at higher levels.

21. Discrete and Continuous Variables • Discrete variables:variables that have a minimum-sized unit of measurement, which cannot be sub-divided • Example: the number children per family • Continuous variables:variables that, in theory, can take on all possible numerical values in a given interval • Example: length

22. Analyzing Data: Descriptive and Inferential Statistics • Population:The total set of individuals, objects, groups, or events in which the researcher is interested. • Sample:A relatively small subset selected from a population. • Descriptive statistics: Procedures that help us organize and describe data collected from either a sample or a population. • Inferential statistics: The logic and procedures concerned with making predictions or inferences about a populationfrom observations and analyses of a sample.