Simple and flexible SAS and SPSS programs for analyzing lag-sequential categorical data

This paper describes simple and flexible programs for analyzing lag-sequential categorical data, using SAS and SPSS. The programs read a stream of codes and produce a variety of lag-sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, adjusted residuals, z values, Yule’s Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests.     

Pitfalls in computing and interpreting randomization testp values: A commentary on Chen and Dunlap

Chen and Dunlap (1993) added to the growing list of papers promoting the use of randomization tests in statistical testing. Their particular contribution was an SAS program that could bring computation of these tests to a wider audience. The present paper points to several problems with the presentation of Chen and Dunlap and provides solutions to these problems. It is concluded that randomization tests deserve more attention, but that they are best computed by programs written in a low-level programming language or, if using SAS on a mainframe, by using the MULTTEST procedure.     

SPSS and SAS programs for addressing interdependence and basic levels-of-analysis issues in psychological data.

Levels-of-analysis issues arise whenever individual-level data are collected from more than one person from the same dyad, family, classroom, work group, or other interaction unit. Interdependence in data from individuals in the same interaction units also violates the independence-of-observations assumption that underlies commonly used statistical tests. This article describes the data analysis challenges that are presented by these issues and presents SPSS and SAS programs for conducting appropriate analyses. The programs conduct the within-and-between-analyses described by Dansereau, Alutto, and Yammarino (1984) and the dyad-level analyses described by Gonzalez and Griffin (1999) and Griffin and Gonzalez (1995). Contrasts with general multilevel modeling procedures are then discussed.     

SPSS and SAS programs for addressing interdependence and basic levels-of-analysis issues in psychological data

Levels-of-analysis issues arise whenever individual-level data are collected from more than one person from the same dyad, family, classroom, work group, or other interaction unit. Interdependence in data from individuals in the same interaction units also violates the independence-of-observations assumption that underlies commonly used statistical tests. This article describes the data analysis challenges that are presented by these issues and presents SPSS and SAS programs for conducting appropriate analyses. The programs conduct the within- and-between-analyses described by Dansereau, Alutto, and Yammarino (1984) and the dyad-level analyses describedby Gonzalez and Griffin (1999) and Griffin and Gonzalez (1995). Contrasts with general multilevel modeling procedures are then discussed.     

Randomization tests as alternative analysis methods for behavior‐analytic data

Randomization statistics offer alternatives to many of the statistical methods commonly used in behavior analysis and the psychological sciences, more generally. These methods are more flexible than conventional parametric and nonparametric statistical techniques in that they make no assumptions about the underlying distribution of outcome variables, are relatively robust when applied to small‐n data sets, and are generally applicable to between‐groups, within‐subjects, mixed, and single‐case research designs. In the present article, we first will provide a historical overview of randomization methods. Next, we will discuss the properties of randomization statistics that may make them particularly well suited for analysis of behavior‐analytic data. We will introduce readers to the major assumptions that undergird randomization methods, as well as some practical and computational considerations for their application. Finally, we will demonstrate how randomization statistics may be calculated for mixed and single‐case research designs. Throughout, we will direct readers toward resources that they may find useful in developing randomization tests for their own data.     

SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients

Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 ? α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.     

SAS procedures for approximate randomization tests

Approximate randomization tests are alternatives to conventional parametric statistical methods used when the normality and homoscedasticity assumptions are violated This article presents an SAS program that tests the equality of two means using an approximate randomization test This program can serve as a template for testing other hypotheses, which is illustrated by modifications to test the significance of a correlation coefficient or the equality of more than two means.     

An SPSS implementation of the nonrecursive outlier deletion procedure with shiftingz score criterion (Van Selst & Jolicoeur, 1994)

Sophisticated univariate outlier screening procedures are not yet available in widely used statistical packages such as SPSS. However, SPSS can accept user-supplied programs for executing these procedures. Failing this, researchers tend to rely on simplistic alternatives that can distort data because they do not adjust to cell-specific characteristics. Despite their popularity, these simple procedures may be especially ill suited for some applications (e.g., data from reaction time experiments). A user friendly SPSS Production Facility implementation of the shifting z score criterion procedure (Van Selst & Jolicoeur, 1994) is presented in an attempt to make it easier to use. In addition to outlier screening, optional syntax modules can be added that will perform tedious database management tasks (e.g., restructuring or computing means).     

Replicability and randomization test logic in behavior analysis

Randomization tests are a class of nonparametric statistics that determine the significance of treatment effects. Unlike parametric statistics, randomization tests do not assume a random sample, or make any of the distributional assumptions that often preclude statistical inferences about single‐case data. A feature that randomization tests share with parametric statistics, however, is the derivation of a p‐value. P‐values are notoriously misinterpreted and are partly responsible for the putative “replication crisis.” Behavior analysts might question the utility of adding such a controversial index of statistical significance to their methods, so it is the aim of this paper to describe the randomization test logic and its potentially beneficial consequences. In doing so, this paper will: (1) address the replication crisis as a behavior analyst views it, (2) differentiate the problematic p‐values of parametric statistics from the, arguably, more useful p‐values of randomization tests, and (3) review the logic of randomization tests and their unique fit within the behavior analytic tradition of studying behavioral processes that cut across species.     

Does It Matter Whether One Takes a Test on an iPad or a Desktop Computer?

To investigate possible iPad related mode effect, we tested 403 8th graders in Indiana, Maryland, and New Jersey under three mode conditions through random assignment: a desktop computer, an iPad alone, and an iPad with an external keyboard. All students had used an iPad or computer for six months or longer. The 2-hour test included reading, math, and writing items adapted from released NAEP 8th grade tests. Overall, no significant difference was found on the reading, math, or writing section scores or section response time among the three mode conditions. Further, roughly comparable numbers of students reportedly favored testing using an iPad or a desktop computer, but using a preferred mode did not lead to significantly higher section scores. These findings suggest that there is no noticeable disadvantage associated with taking a test on an iPad than on a desktop computer for experienced users of these two studied devices.     

A comparison of factor analysis programs in SPSS,BMDP, and SAS

Factor analysis programs in SAS, BMDP, and SPSS are discussed and compared in terms of documentation, methods and options available, internal logic, computational accuracy, and results provided. Some problems with respect to logic and output are described. Based on these comparisons, recommendations are offered which include a clear overall preference for SAS, and advice against general use of SPSS for factor analysis.     

Virtual reality exposure therapy: 150-degree screen to desktop PC.

Virtual reality exposure therapy (VRET) developed using immersive or semi-immersive virtual environments present a usability problem for practitioners. To meet practitioner requirements for lower cost and portability VRET programs must often be ported onto desktop environments such as the personal computer (PC). However, success of VRET has been shown to be linked to presence, and the environment's ability to evoke the same reactions and emotions as a real experience. It is generally accepted that high-end virtual environments (VEs) are more immersive than desktop PCs, but level of immersion does not always predict level of presence. This paper reports on the impact on presence of porting a therapeutic VR application for schizophrenia from the initial research environment of a semi-immersive curved screen to PC. Presence in these two environments is measured both introspectively and across a number of causal factors thought to underlie the experience of presence. Results show that the VR exposure program successfully made users feel they were "present" in both platforms. While the desktop PC achieved higher scores on presence across causal factors participants reported they felt more present in the curved screen environment. While comparison of the two groups was statistically significant for the PQ but not for the IPQ, subjective reports of experiences in the environments should be considered in future research as the success of VRET relies heavily on the emotional response of patients to the therapeutic program.     

SPSS programs for the measurement of nonindependence in standard dyadic designs

Dyadic research is becoming more common in the social and behavioral sciences. The most common dyadic design is one in which two persons are measured on the same set of variables. Very often, the first analysis of dyadic data is to determine the extent to which the responses of the two persons are correlated—that is, whether there is nonindependence in the data. We describe two user-friendly SPSS programs for measuring nonindependence of dyadic data. Both programs can be used for distinguishable and indistinguishable dyad members. Inter1.sps is appropriate for interval measures. Inter2.sps applies to categorical variables. The SPSS syntax and data files related to this article may be downloaded as supplemental materials from brm.psychonomic-journals.org/content/supplemental.     

Internetgest��tzte Nachsorge bei Bulimia nervosa

Based on the knowledge about high relapse rates in bulimia nervosa and the effectiveness of Internet-based prevention programs, a 9-month manualized Internet-based follow-up care program for women with bulimia nervosa after inpatient treatment will be evaluated. A total of 258 women are to be included in the randomized controlled trial and at least 180 women should complete the study. Women will be recruited from 13 psychosomatic hospitals in Germany. Primary outcome is the number of patients without symptoms 9 months after randomization. Data will be collected via self-monitoring diaries as well as a standardized diagnostic interview. Secondary outcomes are the frequency of objective binge episodes and compensatory behavior 9 and 18 months after randomization as well as changes in additional eating disorder symptoms, general psychopathology, self-esteem and impulsiveness 9 and 18 months after randomization.     

Randomization tests in single-subject designs: Illustrative examples

Randomization tests have recently been adapted for use in the analysis of single-subject data. The advantages of these tests lie in their ease of implementation and interpretation as well as their freedom from underlying distributions. Even though numerous articles and books have explicated randomization test procedures, due to the lack of appropriate examples, very little use of these procedures has been made by applied behavior analysts. Data sets reported in a prominent applied behavior journal are used to demonstrate the application of randomization tests to the following three single-subject design models: (a) two-phase random intervention point, (b) multiple phase, and (c) multiple phase with a predicted order of effect size.     

Dealing with Possible Baseline Inequalities Between Experimental Groups – The Case of Motor Learning

Abstract

One important concept of experimental design is the random assignment of participants to experimental groups. This randomization process is used to prevent selection bias, as well as to provide a strong basis for a cause-and-effect relationship between the independent variable/s and the dependent variable/s. In small sample sizes, simple randomization may not provide equal groups at baseline for one or more of the variables, and therefore more restricted types of randomization, such as the stratified permuted-block randomization, can be used. A code was written to calculate the probability that simple randomization will not lead to equality between groups at baseline, and then an example of stratified permuted-block randomization was examined. The findings suggest that for certain variables that are commonly measured in experiments in motor learning, there is a relatively high probability that groups will not be equal at baseline after simple randomization. This observation reflects the small sample sizes usually found in the literature on motor learning. However, stratified permuted-block randomization does lead to greater equality among groups. Implications for researchers are discussed, and a flowchart is proposed that will allow researchers to decide whether to use simple or stratified randomization.     

Randomization procedures in single‐case intervention research contexts: (Some of) “the rest of the story”

Following up on articles recently published in this journal, the present contribution tells (some of) “the rest of the story” about the value of randomization in single‐case intervention research investigations. Invoking principles of internal, statistical‐conclusion, and external validity, we begin by emphasizing the critical distinction between design randomization and analysis randomization, along with the necessary correspondence between the two. Four different types of single‐case design‐and‐analysis randomization are then discussed. The persistent negative influence of serially dependent single‐case outcome observations is highlighted, accompanied by examples of inappropriate applications of parametric and nonparametric tests that have appeared in the literature. We conclude by presenting valid applications of single‐case randomization procedures in various single‐case intervention contexts, with specific reference to a freely available Excel‐based software package that can be accessed to incorporate the present randomization schemes into a wide variety of single‐case intervention designs and analyses.     

CTTITEM: SAS macro and SPSS syntax for classical item analysis

This article describes the functions of a SAS macro and an SPSS syntax that produce common statistics for conventional item analysis including Cronbach’s alpha, item difficulty index (p-value or item mean), and item discrimination indices (D-index, point biserial and biserial correlations for dichotomous items and item-total correlation for polytomous items). These programs represent an improvement over the existing SAS and SPSS item analysis routines in terms of completeness and user-friendliness. To promote routine evaluations of item qualities in instrument development of any scale, the programs are available at no charge for interested users. The program codes along with a brief user’s manual that contains instructions and examples are downloadable from suen.ed.psu.edu/~pwlei/plei.htm.     

Power analysis for multivariate and repeated measures designs: A flexible approach using the SPSS MANOVA procedure

Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.     

On the Multilevel Nature of Meta-Analysis: A Tutorial,Comparison of Software Programs,and Discussion of Analytic Choices

The term “multilevel meta-analysis” is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term “multilevel meta-analysis” is redundant since all meta-analysis can be formulated as a special kind of multilevel model. To clarify the multilevel nature of meta-analysis the four standard meta-analytic models are presented using multilevel equations and fit to an example data set using four software programs: two specific to meta-analysis (metafor in R and SPSS macros) and two specific to multilevel modeling (PROC MIXED in SAS and HLM). The same parameter estimates are obtained across programs underscoring that all meta-analyses are multilevel in nature. Despite the equivalent results, not all software programs are alike and differences are noted in the output provided and estimators available. This tutorial also recasts distinctions made in the literature between traditional and multilevel meta-analysis as differences between meta-analytic choices, not between meta-analytic models, and provides guidance to inform choices in estimators, significance tests, moderator analyses, and modeling sequence. The extent to which the software programs allow flexibility with respect to these decisions is noted, with metafor emerging as the most favorable program reviewed.