1. Chapter 1: The What and the Why of Statistics
Leon-Guerrero and Frankfort-Nachmias,
Essentials of Statistics for a Diverse Society

2. 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:
“Are women paid less than men for the same types of work?”
Not Empirical:
“Is racial equality good for society?”

3. 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.

4. 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
Examples: Social class, age, gender, income

5. 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?

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

7. 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!!

8. 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.

9. Example 1
Research Question: 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:
Identify possible control variables
Gender
Are the causal arguments sound?
e.g. Does party id affect abortion views or vice versa?
Church attendance
Age
Attitudes toward abortion
Religious affiliation (Catholic, Jewish, Methodist, Islamic…)
Political party identification

10. Example 2 Number of books read independent variable:
Research Question: The number of books read to a child per day positively affects a child’s word recognition.
Identify the IV and DV
Identify possible control variables
Are the causal arguments sound?
independent variable:
dependent variable:
Gender
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.
Number of books read
Older siblings
Word recognition
Health status
Birth order

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

12. 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

13. 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.
Variables are measured in three ways:
Nominal
Ordinal
Interval-Ratio
Nominal -- means “in name only.” Also known as categorical or qualitative. Ask them for examples of nominal variables: 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)

14. 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)

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

16. 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

17. 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.
Level
Different or Equivalent
Higher or
Lower
How Much
Higher
Nominal
Yes No
Ordinal
Interval-ratio

18. 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

19. 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

20. 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.

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

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