Table of Contents

1 Variables and their measurement

Learning objectives
The conceptualization and operationalization of variables  
Scales of measurement  
Levels of measurement  
Univariate, bivariate, and multivariate analysis  
Descriptive statistics  
Exercises  

Learning objectives
Obtaining a copy of SPSS  
Alternatives to SPSS  
Options for data entry in SPSS  
The SPSS Data Editor  
Assigning a variable name  
Setting the data type  
Setting the data width and decimal places  
Defining variable labels  
Defining value labels  
Setting missing values  
Setting the column format and alignment  
Specifying the level of measurement  
Shortcuts for defining variables
Generating variable definitions in SPSS  
The SPSS Viewer window  
Saving a data file  
Working with a large data set
Data entry  
Checking for incorrect values: Data cleaning  
Summary  
Exercises  

3 The graphical description of data

Learning objectives
Some general principles  
The SPSS Chart Builder
Pie graphs  
Bar graphs  
Histograms and polygons  
Interpreting a univariate distribution   
Graphing two variables  
Common problems and misuses of graphs  
Exercises  

Learning objectives
Listed data tables  
Simple frequency tables  
Relative frequency tables: percentages, proportions, and rates  
Cumulative frequency tables  
Class intervals  
Percentiles  
Frequency tables using SPSS  
Valid cases and missing values  
Improving the look of tables  
Choosing between graphs and tables
Exercises  

Learning objectives
Crosstabulations as descriptive statistics  
Types of data suitable for crosstabulations  
Crosstabulations with relative frequencies  
Crosstabulations using SPSS  
Interpreting a crosstabulation: The pattern and strength of a relationship  
Interpreting a crosstabulation when both scales are at least ordinal  
Summary  
Exercises  

Learning objectives
Measures of association as descriptive statistics  
Measures of association for nominal scales  
Properties of lambda  
Lambda using SPSS  
Limitations on the use of lambda  
Standardizing table frequencies  
Exercises  

Learning objectives
Data considerations  
Concordant pairs  
Discordant pairs  
Measures of association for ranked data  
Gamma  
Somers’ d  
Kendall’s tau-b  
Kendall’s tau-c  
Measures of association using SPSS  
Summary  
Exercises   

Learning objectives
Direct relationship  
Elaboration of crosstabs using SPSS  
Partial gamma  
Spurious or intervening relationship?  
Conditional relationship  
Summary  
Exercises  

9 Measures of central tendency

Learning objectives
Measures of central tendency  
The mode  
The median  
The mean  
Choosing a measure of central tendency  
Measures of central tendency using SPSS: Univariate analysis  
Measures of central tendency using SPSS: Bivariate and multivariate analysis  
Summary  
Exercises  

Learning objectives
The range  
The interquartile range  
The standard deviation  
Coefficient of relative variation  
Index of qualitative variation  
Measures of dispersion using SPSS  
Summary  
Exercises  

Learning objectives
The normal distribution  
Using normal curves to describe a distribution  
z-scores  
Normal curves on SPSS  
Exercises  

Learning objectives
Scatter plots  
Linear regression  
Pearson’s product moment correlation coefficient  
Explaining variance: The coefficient of determination  
Plots, correlation, and regression using SPSS  
The assumptions behind regression analysis  
Spearman’s rank-order correlation coefficient  
Spearman’s rho using SPSS  
Correlation where the independent variable is categorical: Eta  
Summary  
Exercises   

Learning objectives
Introduction to multiple regression  
Multiple regression with SPSS  
Testing for the significance of the multivariate model  
Alternative methods for selecting variables in the regression model  
Stepwise regression  
Extending the basic regression analysis: Adding categorical independent variables  
Further extensions to the basic regression analysis: Hierarchical regression  
The assumptions behind multiple regression  
Exercises  

1 Sampling distributions

Learning objectives
Random samples  
The sampling distribution of a sample statistic  
The central limit theorem  
Generating random samples using SPSS  
Summary  
Exercises  

Learning objectives
Step 1: State the null and alternative hypotheses  
Step 2: Choose the test of significance  
Step 3: Describe the sample and derive the p-score  
Step 4: Decide at what alpha level, if any, the result is statistically significant  
Step 5: Report results  
What does it mean when we ‘fail to reject the null hypothesis’?  
What does it mean to ‘reject the null hypothesis’?  
A two-tail z-test for a single mean  
The debate over one-tail and two-tail tests of significance  
A one-tail z-test for a single mean  
Summary  
Appendix: Hypothesis testing using critical values of the test statistic  
Exercises  

Learning objectives
The Student’s t-distribution  
The one sample t-test for a mean  
The one sample t-test using SPSS  
Summary  
Exercises  

Learning objectives
The sampling distribution of sample means  
Estimation  
Changing the confidence level  
Changing the sample size  
Estimation using SPSS  
Confidence intervals and hypothesis testing  
Exercises  

Learning objectives
Dependent and independent variables  
The sampling distribution of the difference between two means  
The two samples t-test for the equality of means  
The two samples t-test using SPSS  
Exercises  

Learning objectives
The one-way analysis of variance F-test  
ANOVA using SPSS  
Summary  
Exercises  

Learning objectives
Dependent and independent samples  
The two dependent samples t-test for the mean difference  
The two dependent samples t-test using SPSS  
Exercises  

21 One sample tests for a binomial distribution

Learning objectives
Data considerations  
The sampling distribution of sample percentages  
The z-test for a binomial percentage  
The z-test for a binomial percentage using SPSS  
Estimating a population percentage  
The runs test for randomness  
The runs test using SPSS  
Exercises  

Learning objectives
The chi-square goodness-of-fit test  
Chi-square goodness-of-fit test using SPSS  
The chi-square goodness-of-fit test for normality  
Summary  
Exercises  

Learning objectives
The chi-square test and other tests of significance  
Statistical independence  
The chi-square test for independence  
The distribution of chi-square  
The chi-square test using SPSS  
Problems with small samples  
Problems with large samples  
Appendix: hypothesis testing for two percentages  
Exercises  

Learning objectives
The McNemar chi-square test for change  
The McNemar test using SPSS  
The sign test  
Summary  
Exercises  

25 Rank-order tests for two or more samples

Learning objectives
Data considerations  
The rank sum and mean rank as descriptive statistics  
The z-test for the rank sum for two independent samples  
Wilcoxon’s rank sum z-test using SPSS  
The Wilcoxon signed-ranks z-test for two dependent samples  
The Wilcoxon signed-ranks test using SPSS  
Other non-parametric tests for two or more samples  
Appendix: the Mann–Whitney U test  
Exercises   

Learning objectives
The t-test for Pearson’s correlation coefficient  
Testing the significance of Pearson’s correlation coefficient using SPSS  
The t-test for Spearman’s rank-order correlation coefficient  
Testing the significance of Spearman’s correlation coefficient using SPSS  
Testing for significance in multiple regression  
Exercises  

27 Statistical power

Learning objectives
Calculating statistical power  
Effect size  
Prospective power analysis  
Retrospective power analysis
Summary  

Learning objectives
Recoding variables
Using Recode to convert a string variable to a numeric variable
Some issues with recoding
Computing new variables
The SPSS Multiple Response command
Summary  

Table A1 Area under the standard normal curve  
Table A2 Critical values for t-distributions  
Table A3 Critical values for F-distributions (= 0.05)  
Table A4 Critical values for chi-square distributions  
Table A5 Sampling errors for a binomial distribution (95% confidence level)  
Table A6 Sampling errors for a binomial distribution (99% confidence level)  

Glossary

Answers  

Index