A Student Guide to SPSS
Edition: 3
Copyright: 2020
Pages: 168
Edition: 3
Copyright: 2021
Pages: 168
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The 3rd edition of A Student Guide to SPSS provides easy to follow step-by-step instructions on how to compute introductory and advanced statistics using one of the most popular statistical software programs in psychology, business, education, and other social sciences. Written in a non-intimidating, easy to read language, this guide is suitable for individuals with little to no experience using statistical software. As such, it would be of practical use to anyone who needs a simple and straightforward introduction to the most commonly used features of SPSS.
This guide to SPSS was originally developed to complement the lecture component of introductory undergraduate courses in statistics. The 2nd and 3rd editions were expanded to increase the guide’s suitability for more advanced undergraduate statistics courses. While most statistics textbooks teach students how to hand calculate statistics, this guide gives students the opportunity to learn how to analyze large datasets not conducive to hand calculations, providing them with the practical skills necessary for graduate school and/or a career in research.
Features
- Learning objectives at the beginning of each chapter help students keep on track and instructors apprised of the functions that students have learned so they can create SPSS assignments for students.
- Concrete examples with screenshots of SPSS are used throughout to make it easier for students to learn how to compute and interpret statistics.
- Examples of reporting statistics in the style of the American Psychological Association (APA)- using the 7th edition of their manual- are included.
New to the Third Edition
- Throughout the guide, elaborations on the meaning and interpretation of various statistics and demonstrations of more advanced statistical analyses have been added.
- The chapter on multiple regression has been expanded to include a new example that describes how to include a nominal predictor variable with more than two categories in a multiple regression analysis as well as how to interpret the results.
- A brief discussion of the tolerance statistic has been added to the advanced regression chapter.
- The chapter on one-way ANOVA has been expanded to include one-way within-groups ANOVA (in addition to one-way between-groups ANOVA).
Content
- The 3rd edition of A Student Guide to SPSS contains 9 chapters on getting started with SPSS, descriptive statistics, correlation, simple regression, multiple regression, advanced regression (hierarchal regression, stepwise regression), the sign test, t-tests (single sample, paired samples, independent samples), and one-way ANOVA (one-way between, one-way within).
1 AN INTRODUCTION TO SPSS
Getting Started
Opening SPSS
Data and Variable View Windows
Data View
Variable View
Creating Variables
Entering Data
Saving the SPSS Dataset (File→Save As)
Handy Tools and Tricks
Viewing Value Labels
Sorting Data (Data→Sort Cases)
Selecting Cases (Data→Select Cases)
Splitting Data into Groups (Data→Split File)
Recoding Variables (Transform→Recode into Different Variables)
Computing New Variables (Transform→Compute Variable)
Reporting Decimal Remainders and Rounding
Reporting Decimal Remainders
Rounding
2 DESCRIPTIVE STATISTICS
Central Tendency and Variability
Computing Indicators of Central Tendency and Variability (Analyze→Descriptive
Statistics→Frequencies)
Interpreting the Results
Descriptive Statistics
Sample Size (N)
Variability
Central Tendency
Reporting the Results
Skewness and Kurtosis
Skewness
Kurtosis
Computing Skewness and Kurtosis Statistics (Analyze→Descriptive
Statistics→Frequencies)
Interpreting the Results
Reporting the Results
z Scores
Transforming Raw Scores to z Scores (Analyze→Descriptive Statistics→Descriptives)
Interpreting the Results
3 CORRELATION
Sample Data Files
Opening Sample Data Files
Scatterplots
Generating Scatterplots (Graphs→Legacy Dialogs→Scatter/Dot)
Interpreting Scatterplots
Pearson Correlation Coefficients (r)
Computing Pearson Correlation Coefficients (Analyze→Correlate→Bivariate)
Interpreting the Results
Determining Statistical Significance
Reporting the Results
Spearman Rank Order Correlation Coefficients (rs)
Computing Spearman Rank Order Correlation Coefficients (Analyze→Correlate→
Bivariate)
Interpreting the Results
Reporting the Results
Point Biserial Correlation Coefficients (rpb)
Computing Point Biserial Correlation Coefficients (Analyze→Correlate→Bivariate)
Interpreting the Results
Reporting the Results
Phi Coefficients (ϕ)
Computing Phi Coefficients (Analyze→Caorrelate→Bivariate)
Interpreting the Results
Reporting the Results
Directional Hypotheses and One-Tailed Correlation Analyses
Conducting One-Tailed Correlation Analyses (Analyze→Correlate→Bivariate)
Interpreting the Results
Reporting the Results
Predicting the Wrong Direction
Partial Correlation (pr)
Interpreting Partial Correlations
Computing Partial Correlations (Analyze→Correlate→Partial)
Interpreting the Results
Reporting the Results
4 SIMPLE REGRESSION
Introduction to Regression
Conducting a Simple Regression Analysis (Analyze→Regression→Linear)
Interpreting the Results
Assessing the Model’s Accuracy
The Correlation Coefficient (r)
The Coefficient of Determination (r2)
The Standard Error of Estimate (SEE)
Assessing Statistical Significance
The Regression Model
The Predictor
Constructing the Equation for the Least-Squares Regression Line
Using the Regression Equation to Make Predictions
Reporting the Results
5 MULTIPLE REGRESSION
Introduction to Multiple Regression
Multiple Regression with Two Predictor Variables
Conducting a Multiple Regression Analysis (Analyze→Regression→Linear)
Interpreting the Results
Assessing the Model’s Accuracy
The Multiple Correlation (R)
The Multiple Coefficient of Determination (R2)
The Standard Error of Estimate (SEE)
Assessing Statistical Significance
The Regression Model
The Predictors
Constructing the Equation for the Least-Squares Regression Line
Using the Regression Equation to Make Predictions
Bivariate, Partial, Semipartial, and Squared Semipartial Correlations
Bivariate Correlations (r)
Partial Correlations (pr)
Semipartial Correlations (sr)
Squared Semipartial Correlations (sr2)
Reporting the Results
Multiple Regression with a Nominal Predictor with Two Categories
Conducting a Multiple Regression Analysis (Analyze→Regression→Linear)
Interpreting the Results
Assessing the Model’s Accuracy
The Multiple Correlation (R)
The Multiple Coefficient of Determination (R2)
The Standard Error of Estimate (SEE)
Assessing Statistical Significance
The Regression Model
The Predictors
Constructing the Equation for the Least-Squares Regression Line
Using the Regression Equation to Make Predictions
Bivariate, Partial, Semipartial, and Squared Semipartial Correlations
Bivariate Correlations (r)
Partial Correlations (pr)
Semipartial Correlations (sr)
Squared Semipartial Correlations (sr2)
Reporting the Results
Multiple Regression with a Nominal Predictor with More than Two Categories
Creating New Dichotomous Variables
Conducting a Multiple Regression Analysis (Analyze→Regression→Linear)
Interpreting the Results
Assessing the Model’s Accuracy
The Multiple Correlation (R)
The Multiple Coefficient of Determination (R2)
The Standard Error of Estimate (SEE)
Assessing Statistical Significance
The Regression Model
The Predictors
Constructing the Equation for the Least-Squares Regression Line
Using the Regression Equation to Make Predictions
Reporting the Results
6 ADVANCED REGRESSION
Hierarchical Regression
Conducting a Hierarchical Regression Analysis (Analyze→Regression→Linear)
Interpreting the Results
Model Comparisons
Model Statistics
Predictor Statistics
Excluded Variables
Reporting the Results
Conducting a More Complex Hierarchical Regression Analysis
(Analyze→Regression→Linear)
Interpreting the Results
Model Comparisons
Predictor Statistics
Model Statistics
Excluded Variables
Reporting the Results
Stepwise Regression
Conducting a Stepwise Regression Analysis (Analyze→Regression→Linear)
Interpreting the Results
Finding the Best Model
Model Statistics
Predictor Statistics
Excluded Variables
Constructing the Equation for the Least-Squares Regression Line
Using the Regression Equation to Make Predictions
Reporting the Results
7 SIGN TEST
Introduction to the Sign Test
Two-Tailed Sign Test
Conducting a Sign Test (Analyze→Nonparametric Tests→Legacy Dialogs→2 Related
Samples)
Interpreting the Results
Reporting the Results
One-Tailed Sign Test
Conducting a Sign Test (Analyze→Nonparametric Tests→Legacy Dialogs→
2 Related Samples)
Interpreting the Results
Reporting the Results
Predicting the Wrong Direction
8 t-TESTS
Single Sample t-Test
Conducting a Single Sample t-Test (Analyze→Compare Means→
One-Sample t Test)
Interpreting the Results
Descriptive Statistics
Assessing Statistical Significance
Calculating Confidence Intervals for the Population Mean
Effect Sizes (Cohen’s d and Hedges’ g)
Reporting the Results
One-Tailed Single Sample t-Test
Reporting the Results
Predicting the Wrong Direction
Paired Samples t-Test
Conducting a Paired Samples t-Test (Analyze→Compare Means→PairedSamples T Test)
Interpreting the Results
Descriptive Statistics
Assessing Statistical Significance
Effect Sizes (Cohen’s d and Hedges’ g)
Reporting the Results
Calculating the 99% Confidence Intervals for the Difference in Means
Reporting the Results
One-Tailed Paired Samples t-Test
Reporting the Results
Predicting the Wrong Direction
Independent Samples t-Test
Conducting an Independent Samples t-Test (Analyze→Compare Means→IndependentSamples T Test)
Interpreting the Results
Descriptive Statistics
Levene’s Test of Homogeneity of Variance
Assessing Statistical Significance
Effect Sizes (Cohen’s d, Hedges’ g, and Glass’s Δ)
Reporting the Results
Calculating the 99% Confidence Intervals for the Difference in Means
Reporting the Results
One-Tailed Independent Samples t-Test
Reporting the Results
Predicting the Wrong Direction
9 ONE-WAY ANOVA
Introduction to One-Way ANOVA
One-Way Between-Groups ANOVA
Conducting a One-Way Between-Groups ANOVA (Analyze→General Linear
Model→Univariate)
Interpreting the Results
Descriptive Statistics
Levene’s Test of Homogeneity of Variance
Main Effect
Effect Size
Post Hoc Tests
Reporting the Results
One-Way Within-Groups ANOVA
Conducting a One-Way Within-Groups ANOVA (Analyze→General Linear
Model→Repeated Measures)
Interpreting the Results
Descriptive Statistics
Mauchly’s Test of Sphericity
Main Effect
Effect Size
Post Hoc Tests
Reporting the Results
“Written in a clear and accessible style, Dr. Cuttler’s Student Guide to SPSS provides an excellent introduction to one of the most popular statistical software packages used by psychologists. It is an outstanding resource for any undergraduate course in behavioral statistics.”
—D. Geoffrey Hall, PhD - University of British Columbia
“A Student Guide to SPSS lays out step-by-step the most common kinds of simple analyses for introductory students. With the help of the guide, students are able to complete analyses in SPSS on their own, outside the classroom or computer lab. An invaluable resource for anyone wanting to incorporate an SPSS lab component to an introductory statistics course.”
—Dana S. Thordarson, PhD - University of British Columbia