Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

Section II: Statistical Procedures for Data from a Single Group of Participants 707
DESCRIPTIVE
STATISTICS
INFERENTIAL
STATISTICS
Numerical scores from
interval or ratio scales
Mean (chapter 3) and
standard deviation
(chapter 4)
Proportions or Percentages
to describe the distribution
across categories
Single-sample t test
(chapter 9). Use the sample
mean to test a hypothesis
about the population mean.
Chi-square test for
goodness of fit (chapter 17).
Use the sample frequencies
to test a hypothesis about
the proportions in the
population.
Ordinal scores
(Ranks or ordered
categories)
Median (chapter 3)
Proportions or Percentages
to describe the distribution
across categories
Chi-square test for
goodness of fit (chapter 17).
Use the sample frequencies
to test a hypothesis about
the proportions in the
population.
Nominal Scores
(Named categories)
Mode (chapter 3)
Proportions or Percentages
to describe the distribution
across categories
Chi-square test for
goodness of fit (chapter 17).
Use the sample frequencies
to test a hypothesis about
the proportions in the
population.
FIGURE 1
Statistics for Category 1 data. A single group of participants with one score per participant. The goal is to describe the
variable as it exists naturally.
Section II: Statistical Procedures for Data from a Single
Group of Participants with Two Variables Measured
for Each Participant
The goal of the statistical analysis for data in this category is to describe and evaluate
the relationships between variables, typically focusing on two variables at a time. With
only two variables, the appropriate statistics are correlations and regression (Chapters 15
and 16), and the chi-square test for independence (Chapter 17). With three variables,
another alternative is a partial correlation (Chapter 15), and multiple regression
(Chapter 16).
■ Two Numerical Variables from Interval or Ratio Scales
The Pearson correlation measures the degree and direction of linear relationship between
the two variables (see Example 15.3 on p. 494). Linear regression determines the equation
for the straight line that gives the best fit to the data points. For each X value in the data, the