1. Introduction to Statistics and Research
Chapter 1
Introduction to Statistics and Research

2. Going Forward Your goals in this chapter are to learn:
The logic of research and the purpose of statistical procedures
What a relationship between scores is
When and why descriptive and inferential statistical procedures are used
What the difference is between an experiment and a correlational study, and what the independent variable, the conditions, and the dependent variable are
What the four scales of measurement are

3. Learning About Statistics

4. What is Statistics? Statistics help make sense of data in four ways:
Organize scores to see patterns
Summarize data to understand general characteristics
Communicate results of a study
Interpret what the data indicate

5. Studying Statistics Carefully read and study the material
Use the in-chapter “Quick Practice”
Learn the terminology
Do the end-of-chapter study questions
Review the Chapter Summary tear-out card
Complete the Putting It All Together tear-out card
Visit the CourseMate website

6. The Logic of Research

7. Behavioral Research
The goal of behavioral research is to understand the “laws of nature” that apply to the behaviors of living organisms.

8. Samples and Populations
The entire group to which a law of nature applies is the population
A sample is a relatively small subset of a population intended to represent, or stand in for, the population
The individuals measured in a sample are called the participants

9. Samples and Populations
Use the scores in a sample to infer—that is, to estimate—the scores we would expect to find in the population.
This assumes a sample is representative of the population.
If a sample is unrepresentative, it inaccurately reflects the population. Unrepresentative samples may give misleading results.

10. Understanding Variables
A variable is anything that can produce two or more different scores. Some common variables in behavioral research are:
Age
Race
Gender
Personality type
Physical attributes

11. Types of Variables The two categories of variables are:
Quantitative variables in which a score indicates the amount of a variable that is present and
Qualitative variables that classify or categorize an individual on the basis of some characteristic

12. Understanding Relationships

13. Relationships
In a relationship, as the scores on one variable change, the scores on the other variable change in a consistent manner.

14. Types of Relationships
Simple relationships have one of two patterns. If we call one variable X and the other variable Y, then
Pattern 1: The more you X, the more you Y
Pattern 2: The more you X, the less you Y
Example: The more you drive distracted, the more likely it is you will have an accident (Pattern 1).

15. Relationship Consistency
If a score on one variable is always paired with one and only one score on the other variable, we have a perfectly consistent relationship.
Perfect consistency is not required to have a relationship, only some degree of consistency. This means as the scores on one variable change, the scores on the other variable tend to change in a consistent fashion.

16. Relationship Consistency
When essentially the same set of Y scores are paired with every X score, a relationship does not exist.

17. Applying Descriptive and Inferential Statistics

18. Applying Statistics
Descriptive statistics are procedures for organizing and summarizing sample data
Inferential statistics are procedures for drawing inferences about the scores and relationship that would be found in the population

19. Statistics Vs. Parameters
A statistic is a number describing an aspect of the scores in a sample
A parameter is a number describing an aspect of the scores in the population

20. Statistics Vs. Parameters
Statistics are represented using English letters such as A, B, C, etc.
Parameters are represented using Greek letters such as a, b, c, etc.

21. Understanding Experiments and Correlational Studies

22. Research Designs A study’s design is the way the study is laid out
Different designs require different descriptive and inferential procedures, so learn when to use each procedure
There are two major types of designs:
Experiments
Correlational studies

23. Experiments
In an experiment, the researcher actively changes or manipulates one variable and then measures participants’ scores on another variable to see if a relationship is produced.

24. The Independent Variable
The independent variable is changed or manipulated by the experimenter
A condition is the specific amount or category of the independent variable creating the specific situation under which participants are studied

25. The Dependent Variable
The dependent variable is the variable measuring a behavior or attribute of participants we expect will be influenced by the independent variable.

26. Can You?
Identify the independent variable, the conditions of the independent variable, and the dependent variable for the following study: The effect of an intensive summer school college preparatory program (compared to no program) on the GPAs of at-risk freshmen students.

27. Correlational Studies
In a correlational study, the researcher measures participants’ scores on two variables and then determines whether a relationship exists.

28. The Characteristics of Scores

29. Measurement Scales
The kind of information scores convey depends on the scale of measurement used. There are four types of measurement scales:
A nominal scale does not measure an amount; rather, it categorizes or classifies individuals.
An ordinal scale indicates rank order. There is no score of 0 (zero), and the same amount does not separate every pair of adjacent scores.

30. Measurement Scales (cont’d)
An interval scale indicates an actual quantity, and there is an equal amount separating any adjacent scores. Interval scales do not have a “true” 0.
A ratio scale also measures an actual quantity. There is an equal amount separating any adjacent scores, and 0 truly means none of the variable is present.

31. Continuous Versus Discrete
Any variable also may be either continuous or discrete.
A continuous variable can be measured in fractional amounts and so decimals make sense
A discrete variable can only be measured in fixed amounts, which cannot be broken into smaller amounts

32. Examples
For each of the following variables, indicate (1) the measurement scale and (2) whether it is continuous or discrete:
The number of tickets sold to an event
Your flavor preferences in soft drinks
Weight IQ

33. Examples The number of tickets sold to an event
ratio, discrete
Your flavor preferences in soft drinks
ordinal, discrete
Weight
ratio, continuous IQ
interval, continuous