RETURN TO SEARCH RESULTS

A Student Guide to SPSS

Author(s): Carrie Cuttler

Edition: 3

Copyright: 2020

Pages: 168

CHOOSE YOUR FORMAT Help Me Choose

Details: Electronic Delivery EBOOK | 180 days |

STEP 2: CHOOSE YOUR PLATFORM Help Me Choose

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

Carrie Cuttler

“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 

Related ISBN's: 9781792411854, 9781792408632

Ebook

$42.00

QTY:

ISBN 9781792411854

Details Electronic Delivery EBOOK 180 days