The PSPP Guide: An Introduction to Statistical Analysis

95 Pages·2017·8.347 MB·English

by  Christopher P. Halter

The PSPP Guide:
An Introduction to
Statistical Analysis

Second Edition

Christopher P. Halter
CreativeMinds Press Group
San Diego, CA
PSPP Guide
Copyright © 2017 by Christopher P. Halter
All rights reserved. No part of this document may be reproduced or transmitted in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without prior written
permission of the CreativeMinds Press Group

ISBN: 0692866043
ISBN-13: 978-0692866047

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CONTENTS

Chapter 1 An Introduction to the Guide Second Edition 1
Notes about the statistics guide 1
Notes about the data 2
The Philosophy Behind This Book and the Open Source Community 3
Chapter 2 Overview of Statistical Analysis in Social Science 4
Why use statistics in Social Science research? 4
What is Continuous and Categorical Data? 5
Parametric versus Non-Parametric Data 10
Confidence Intervals (CI) 11
P-Value 12
Effect Size 14
Effect Size Calculations 15
Chapter 3 The PSPP Statistical Analysis Environment 17
What is PSPP? 17
Data Visualization 20
Chapter 4 Getting Started with PSPP 22
Preparing the Data and Making Decisions 22
Creating Your Variable Codebook 22
Creating Variable/Data Names in PSPP 25
Entering Data Directly into PSPP 29
Opening Data Files with PSPP (.sav files) 30
Importing Data Files into PSPP from a Spreadsheet (.ods files) 32
Chapter 5 Descriptive Statistics 36
What are descriptive statistics? 36
Creating Descriptive Statistics in PSPP for Categorical Data 36
Creating Visual Representations for Categorical Data 38
Creating Descriptive Statistics in PSPP for Continuous Data 39
Creating Visual Representations for Continuous Data 40
Exploring the Data 44
Chapter 6 Graphs: Scatterplot, Histogram, Barchart 48
Scatterplots 48
Histograms 49
Barcharts 51
Chapter 7 Relationship Analysis with Chi-Square 54
Chi-Square Analysis (Categorical Differences) 54
Using the Chi-Square Function in PSPP 54
Interpreting Output Tables: Chi Square 57
Chi-Square Crosstabs Table Analysis 60
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PSPP Guide
Chapter 8 Relationship Analysis with t-Test 68
t-Test Analysis (Continuous Differences, two groups) 68
One Sample t-Test using PSPP 69
Independent Samples t-Test using PSPP 71
Paired Samples t-Test using PSPP 78
Chapter 9 Relationship Analysis with ANOVA 81
Analysis of Variance (ANOVA) 81
One-Way ANOVA 81
Interpreting Output Tables: One-Way ANOVA 84
Introduction to Planned Contrasts 87
Conducting One-Way ANOVA with Planned Contrasts 87
ANOVA with Planned Contrasts for Orthogonal Polynomial Trends 99
Analyzing the ANOVA Output Tables for Orthogonal Polynomial Trends 102
Chapter 10 Univariate Analysis: General Linear Model (GLM) 104
Using Univariate Analysis for the General Linear Model (GLM) 105
Using Univariate Analysis for Two-Way (Factorial) ANOVA 107
Chapter 11 Associations with Correlation 110
Correlation Analysis with PSPP 110
Chapter 12 Associations with Regression (Linear) 114
Regression Analysis with PSPP 114
Interpreting Output Tables: Regression 117
Chapter 13 Associations with Regression (Binomial Logistic) 119
A Simple Binomial Logistic Regression Example with Categorical Data 121
A Simple Binomial Logistic Regression Example with Continuous Data 124
Chapter 14 Reliability 127
Reliability Using PSPP for Agreement 128
Reliability Using PSPP for Accuracy 130
Chapter 15 Factor Analysis 132
What is Factor Analysis? 132
Determining the Number of Factors to Extract 133
Conducting Factor Analysis with PSPP 136
Chapter 16 Why is Statistics So Confusing? 143
The Research Process 143
Exploring the data 144
The General Linear Model (GLM) 146
One-Way ANOVA with Confidence Intervals 147
One-Way ANOVA with Contrasts for Trends 148
Our Findings from the Data 148
Chapter 17 Concluding Thoughts 149
Resources 152
Analysis Memos 153
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Second Edition
High School & Beyond Codebook 155
High School & Beyond Sample Data Set 156
Reliability for Agreement Codebook 164
Reliability for Agreement Dataset 165
Reliability for Accuracy Codebook 166
Reliability for Accuracy Dataset 167
Test Scores Codebook 168
Test Scores Dataset 169
Effect Size Tables 170
Box & Whisker Plots Using OpenOffice Spreadsheets 174
Additional Resources 183
References 185
Index 186

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PSPP Guide

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Second Edition
ACKNOWLEDGMENTS

Whether knowingly or unknowingly, those of us using technology owe a great deal to the open
source software community. It is through projects such as PSPP, OpenOffice, Linux, and
others that useful applications can be freely distributed. The programmers who make up this
community of professionals offer their time and effort for nothing more than the ability to
share something worthwhile with the rest of us.

THANK YOU.
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Chapter 1
An Introduction to the Guide
Second Edition

Notes about the statistics guide

So let’s get this out of the way right from the start. This is NOT a math book.

The PSPP Guide will not contain beautiful, complex statistical equations. It will not
explain the formulas and mathematics behind the statistical tests. It will not provide
step by step mathematical guidance to reproduce the statistical results by hand.

So what is the purpose of this book? I am glad you asked.

The purpose of this guide is to assist the novice social science and educational
researcher in interpreting statistical output data using the PSPP Statistical Analysis
application. Through the examples and guidance, you will be able to select the
statistical test that is appropriate for your data, apply the inferential test to your data,
and interpret a statistical test’s output table.

The Guide goes into the uses of some of the most commonly used statistical tests
and discusses some of the limitations of those tests, i.e. Chi-square, t-Test, ANOVA,
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PSPP Guide
Correlation, and Regressions (Linear and Binomial). The ANOVA description
included procedures for conducting the One-Way ANOVA with Planned Contrasts
so that you may test a specific hypothesis concerning group interactions, as well as
the General Linear Model (GLM) for other types of ANOVA analysis. Exploratory
Factor Analysis has been included in this guide as a valuable procedure for data
reduction. The use of Reliability tests will be discussed as a way to verify the
reliability of coding data between researchers.

Statistical tests are designed to handle either parametric or non-parametric data. The
differences between these types of data will be discussed in a later chapter. The
majority of the tests included in PSPP are designed for parametric data analysis and
will be the focus of this book. PSPP also contains a handful of non-parametric data
tools, but these will not be discussed here.

The sample window views and output tables shown in this guide were mainly created
from PSPP 0.10.x, the graphical user interface version of PSPP called PSPPIRE.

PSPP is officially described as a “replacement” application for IBM’s Statistical
Package for the Social Sciences (SPSS). However, PSPP does not have any official
acronym expansion. The developers of PSPP have some suggestions, such as;

• Perfect Statistics Professionally Presented.
• Probabilities Sometimes Prevent Problems.
• People Should Prefer PSPP.

The examples shown in this guide represent a subset of the data obtained in the
1988-2000 High School & Beyond (HS&B) study commissioned by the Center on
Education Policy (CEP). The sample datasets contains 200 cases and are intended to
provide statistical analysis practice and not to draw any conclusions about the sample
population.

Notes about the data

The High School & Beyond study was commissioned by the Center on Education
Policy (CEP) and conducted by researcher Harold Wenglinsky. The study was based
on the statistical analyses of a nationally representative, longitudinal database of
students and schools from the National Educational Longitudinal Study of 1988-
2000 (NELS). The study focused on a sample of low-income students from inner-
city high schools. The study compared achievement and other education-related
outcomes for students in different types of public and private schools, including
comprehensive public high schools (the typical model for the traditional high
school); public magnet schools and “schools of choice;” various types of Catholic
parochial schools and other religious schools; and independent, secular private
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