chapter 1: the what and the why of statistics the research process asking a research question the...

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

© 2011 SAGE PublicationsFrankfort-Nachmias and Leon-Guerrero, Social Statistics for a Diverse Society, 6e

The Research Process


Asking the Research Question


Formulating the

Hypotheses

Formulating the

Hypotheses

Evaluating the

Hypotheses

Evaluating the

Hypotheses

Analyzing DataAnalyzing Data

Develop a research design

Contribute new evidence to literature and begin again

THEORY

Examine a social relationship, study the relevant literature

Collecting Data

Collecting Data

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?”


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.


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


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?

Society as unit of analysis: What was the crime rate last year?


Types of Variables

Dependent The variable to be explained (the “effect”).

Independent The variable expected to account

for (the “cause” of) the dependent variable.


Cause and Effect RelationshipsCause and effect relationships

between variables are not easy to infer in the social sciences. Causal relationships must meet three criteria:

1. The cause has to precede the effect in time

2. There has to be an empirical relationship between the cause and effect

3. This relationship cannot be explained by other factors


Guidelines for Independent and Dependent Variables1. The dependent variable is always

the property you are trying to explain; it is always the object of the research.

2. The independent variable usually occurs earlier in time than the dependent variables.

3. The independent variable is often seen as influencing, directly or indirectly, the dependent variable.



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

Example 1


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

Example 2

Collecting Data




Formulating the

Hypotheses

Formulating the

Hypotheses

Evaluating the

Hypotheses

Evaluating the

Hypotheses




THEORY


Collecting 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

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:

NominalOrdinalInterval-Ratio


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

Variables that can be ranked from low to high.

Example: Social Class

Upper Class

Middle ClassWorking 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: AgeIncomeSAT 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 can’t be measured at higher levels.


Level Different or Equivalent

Higher orLower

How MuchHigher

Nominal Yes No No

Ordinal Yes Yes No Interval-ratio Yes Yes Yes

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




Formulating the

Hypotheses

Formulating the

Hypotheses

Evaluating the

Hypotheses

Evaluating the

Hypotheses




THEORY


Collecting Data

Collecting Data

Begin the Process Again...




Formulating the

Hypotheses

Formulating the

Hypotheses

Evaluating the

Hypotheses

Evaluating the

Hypotheses




THEORY


Collecting Data

Collecting Data

chapter 1: the what and the why of statistics the research process asking a research question the...

Documents

social statistics

diverse society

social scientists

social differences

social class

empirical research

unit of analysis

research questions subject