Skip to Content

Analysis of Pretest-Posttest Designs

By Peter L. Bonate

Chapman and Hall/CRC – 2000 – 224 pages

Purchasing Options:

  • Add to CartHardback: $154.95
    978-1-58488-173-5
    May 11th 2000

Description

How do you analyze pretest-posttest data? Difference scores? Percent change scores? ANOVA? In medical, psychological, sociological, and educational studies, researchers often design experiments in which they collect baseline (pretest) data prior to randomization. However, they often find it difficult to decide which method of statistical analysis is most appropriate to use. Until now, consulting the available literature would prove a long and arduous task, with papers sparsely scattered throughout journals and textbook references few and far between.

Analysis of Pretest-Posttest Designs brings welcome relief from this conundrum. This one-stop reference - written specifically for researchers - answers the questions and helps clear the confusion about analyzing pretest-posttest data. Keeping derivations to a minimum and offering real life examples from a range of disciplines, the author gathers and elucidates the concepts and techniques most useful for studies incorporating baseline data.

Understand the pros and cons of different methods - ANOVA, ANCOVA, percent change, difference scores, and more

Learn to choose the most appropriate statistical test - Numerous Monte Carlo simulations compare the various tests and help you select the one best suited to your data

Tackle more difficult analyses - The extensive SAS code included saves you programming time and effort

Requiring just a basic background in statistics and experimental design, this book incorporates most, if not all of the reference material that deals with pretest-posttest data. If you use baseline data in your studies, Analysis of Pretest-Posttest Designs will save you time, increase your understanding, and ultimately improve the interpretation and analysis of your data.

Contents

INTRODUCTION

Clinical Applications of Pretest-Posttest Data

Why use the Pretest Data

Graphical Presentation of Pretest-Posttest Data

How to Analyze Pretest-Postest Data: Possible Solutions

A Note on SAS Notation

Focus of the Book

MEASUREMENT CONCEPTS

What is Validity?

What is Reliability?

What is Regression Towards the Mean?

Why is Regression Towards the Mean Important?

Dealing with Regression Towards the Mean and How to Take Advantage of Test-Retest Reliability

What is Pretest Sensitization?

Controlling for Pretest Sensitization with Factorial Designs

Alternative Methods for Controlling for Pretest Sensitization

DIFFERENCE SCORES

Definition and Assumptions

Case 1: The Absence of a Treatment Intervention Between Measurement of the Pretest and Posttest Scores

Case 2: The Application of a Treatment Intervention Between Measurement of the Pretest and Posttest Scores

Nonparametric Alternative to Case 1 or Case 2

Case 3: Two Groups with Different Treatment Interventions Between Measurement of Pretest and Posttest Scores

Case 4: More than Two Groups with Different Treatment Interventions Between Measurement of Pretest and Posttest Scores

Unreliability of Difference Scores

Testing the Distribution of Change and Relative change Scores

Effect of Regression Towards the Mean on Difference Scores

RELATIVE CHANGE FUNCTIONS

Definitions and Assumptions

Statistical Analyses with Change Scores

Change Scores and Regression Towards the Mean

Difference Scores or Relative change Scores?

Other Relative change Functions

Distribution of Relative change Scores

ANALYSIS OF COVARIANCE

Definitions and Assumptions

Parametric ANCOVA

ANCOVA with Difference Scores as the Dependent Variable

ANCOVA using Percent change as the Dependent Variable

Assumptions of the ANCOVA

Violation of Homogeneity of Within-Groups Regression Coefficients

Error-in-Variables ANCOVA

Other Violations

Effect of Outliers and Influential Observations

Nonrandom Assignment of Subject to Treatment Groups

BLOCKING TECHNIQUES

Using Stratification to Control for the Pretest

Post-Hoc Stratification

REPEATED MEASURES ANALYSIS OF VARIANCE

Using Repeated Measures ANOVA for Analysis of Pretest-Posttest Data

Regression Towards the Mean with Multiple Posttest Measurements

Using Repeated Measures ANOVA for Analysis of Pretest-Posttest Data with Multiple Posttest Measurements

Analysis of Repeated Measures using Summary Measures

CHOOSING A STATISTICAL TEST

Choosing a Test Based on how the Data will be Presented

Generation of Bivariate, Normally Distributed Data with a Specified Covariance Structure

Monte Carlo Simulation when the Assumptions of the Statistical Test are Met

Monte Carlo simulation when Systematic Bias Affects the Pretest and Posttest Equally

Monte Carlo Simulation when the variance of the Posttest Scores does not Equal the Variance of the Pretest Scores

Monte Carlo Simulation when Subjects are Grouped A Priori based on Pretest Score

Monte Carlo Simulation when the Marginal Distribution of the Pretest and Posttest Scores is Non-Normal

RANDOMIZATION TESTS

Permutation Tests and Randomization Tests

Randomization Tests and Pretest-Posttest Data

Analysis of Covariance

Resampling within Block or Time Periods

Resampling with Missing Data

SPECIAL TOPICS: EQUALITY OF VARIANCE

Methods and Procedures

APPENDIX: SAS Code

Name: Analysis of Pretest-Posttest Designs (Hardback)Chapman and Hall/CRC 
Description: By Peter L. Bonate. How do you analyze pretest-posttest data? Difference scores? Percent change scores? ANOVA? In medical, psychological, sociological, and educational studies, researchers often design experiments in which they collect baseline (pretest) data prior to...
Categories: Statistics for the Biological Sciences, Mathematics & Statistics for Engineers, Statistical Theory & Methods