Chapter 1: The What and the Why of Statistics The Research Process Asking a Research Question The Role of Theory Formulating the Hypotheses Independent & Dependent Variables: Causality Independent & Dependent Variables: Guidelines Collecting Data Levels of Measurement Discrete and Continuous Variables Analyzing Data & Evaluating Hypotheses Descriptive and Inferential Statistics Looking at Social Differences

The Research Process THEORY Examine a social relationship, study the relevant literature Asking the Research Question Formulating the Hypotheses Contribute new evidence to literature and begin again Develop a research design THEORY Need to add an arrow from THEORY to ANALYZING DATA and back. Evaluating the Hypotheses Analyzing Data Collecting Data

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.

Asking a Research Question What is Empirical Research? Research based on information that can be verified by using our direct experience. To answer research questions we cannot rely on reasoning, speculation, moral judgment, or subjective preference Empirical (fact): “Are women paid less than men for the same types of work?” Not Empirical /Normative (value): “Is racial equality good for society?”

Formulating the 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

Example Variable Categories Religion Christian Jewish Muslim Monthly Income ($) 0-1999 2000-2999 2000-& above Marital Status Single Married Divorced Widowed

Units of Analysis The level of social life on which social scientists focus (individuals, groups). Examples: Individual as unit of analysis: What are your political views? Family as unit of analysis: Who does the housework? Organization as unit of analysis: What is the gender composition? City as unit of analysis: What was the crime rate last year?

Types of Variables IV  DV Dependent The variable to be explained (the “effect”). Independent The variable expected to account for (the “cause” of) the dependent variable. IV  DV

Cause and Effect Relationships Cause and effect relationships between 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 Don’t include in PFP version!!

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.

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

Example 2 Identify the IV and DV Identify possible control variables The number of books read to a child per day positively affects a child’s word recognition. Identify the IV and DV independent variable: dependent variable: Number of books read Word recognition Identify possible control variables Gender Older siblings Health status Birth order 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 his or her parent reads to him/her.

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

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

Levels of Measurement Nominal Ordinal Interval-Ratio 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. Nominal Ordinal Interval-Ratio Nominal -- means “in name only.” Also known as categorical or qualitative. Ask them for examples of nominal vars: gender, religion, type of company (manufacturing, retail, health services, etc.) Ordinal -- e.g., attitudinal variables (views on abortion) Interval-Ratio -- can ask how much more of X (temperature, income, test scores)

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) Religion (Catholic, Jewish, Muslim, Protestant) Race (African American, Latino, Native American)

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

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

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 cannot be measured at higher levels. Different or Higher or How Much Level Equivalent Lower Higher Nominal Yes No Ordinal Interval-ratio

Cumulative Property of Levels of Measurement There is one exception, though Dichotomous variables Because there are only two possible values for a dichotomy, we can measure it at the ordinal or the interval-ratio level (e.g., gender) There is no way to get them out of order This gives the dichotomy more power than other nominal level variables

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

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 population from observations and analyses of a sample.

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

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