1. Statistics: A Tool For Social Research
Seventh Edition
Joseph F. Healey

2. Chapter 1
Introduction

3. Chapter Outline Why Study Statistics?
The Role of Statistics in Scientific Inquiry
The Goals of This Text
Descriptive and Inferential Statistics
Discrete and Continuous Variables
Level of Measurement

4. In This Presentation The role of statistics in the research process
Statistical applications
Types of variables

5. The Role Of Statistics
Statistics are mathematical tools used to organize, summarize, and manipulate data.

6. Data Scores on variables.
Information expressed as numbers (quantitatively).

7. Variables Traits that can change values from case to case. Examples:
Age
Gender
Race
Social class

8. Case The entity from which data is gathered. Examples People Groups
States and nations

9. The Role Of Statistics:Example
Describe the age of students in this class.
Identify the following:
Variable
Data
Cases
Appropriate statistics

10. The Role Of Statistics: Example
Variable is age.
Data is the actual ages (or scores on the variable age): 18, 22, 23, etc.
Cases are the students.

11. The Role Of Statistics: Example
Appropriate statistics include:
average - average age of students in this class is 21.7 years.
percentage - 15% of students are older than 25

12. Statistical Applications
Two main statistical applications:
Descriptive statistics
Inferential statistics

13. Descriptive Statistics
Summarize variables one at a time.
Summarize the relationship between two or more variables.

14. Descriptive Statistics
Univariate descriptive statistics include:
Percentages, averages, and various charts and graphs.
Example: On the average, students are 20.3 years of age.

15. Descriptive Statistics
Bivariate descriptive statistics describe the strength and direction of the relationship between two variables.
Example: Older students have higher GPAs.

16. Descriptive Statistics
Multivariate descriptive statistics describe the relationships between three or more variables.
Example: Grades increase with age for females but not for males.

17. Inferential Statistics
Generalize from a sample to a population.
Population includes all cases in which the research is interested.
Samples include carefully chosen subsets of the population.

18. Inferential Statistics
Voter surveys are a common application of inferential statistics.
Several thousand carefully selected voters are interviewed about their voting intentions.
This information is used to estimate the intentions of all voters (millions of people).
Example: The Republican candidate will receive about 42% of the vote.

19. Types Of Variables Variables may be: Independent or dependent
Discrete or continuous
Nominal, ordinal, or interval-ratio

20. Types Of Variables In causal relationships: CAUSE  EFFECT
independent variable  dependent variable

21. Types Of Variables
Discrete variables are measured in units that cannot be subdivided.
Example: Number of children
Continuous variables are measured in a unit that can be subdivided infinitely.
Example: Age

22. Level Of Measurement
The mathematical quality of the scores of a variable.
Nominal - Scores are labels only, they are not numbers.
Ordinal - Scores have some numerical quality and can be ranked.
Interval-ratio - Scores are numbers.

23. Nominal Level Variables
Scores are different from each other but cannot be treated as numbers.
Examples:
Gender
1 = Female, 2 = Male
Race
1 = White, 2 =Black, 3 = Hispanic
Religion
1 = Protestant, 2 = Catholic

24. Ordinal Level Variables
Scores can be ranked from high to low or from more to less.
Survey items that measure opinions and attitudes are typically ordinal.

25. Ordinal Level Variables: Example
“Do you agree or disagree that University Health Services should offer free contraceptives?”
A student that agreed would be more in favor than a student who disagreed.
If you can distinguish between the scores of the variable using terms such as “more, less, higher, or lower” the variable is ordinal.

26. Interval-ratio Variables
Scores are actual numbers and have a true zero point and equal intervals between scores.
Examples:
Age (in years)
Income (in dollars)
Number of children
A true zero point (0 = no children)
Equal intervals: each child adds one unit

27. Level of Measurement
Different statistics require different mathematical operations (ranking, addition, square root, etc.)
The level of measurement of a variable tells us which statistics are permissible and appropriate.