FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.     

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.     

COMMONALITY ANALYSIS: A METHOD FOR DECOMPOSING EXPLAINED VARIANCE IN MULTIPLE REGRESSION ANALYSES

Commonality analysis is a procedure for decomposing R² in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors. Commonality analysis thus sheds additional light on the magnitude of an obtained multivariate relationship by identifying the relative importance of all independent variables, findings which can be of theoretical and practical significance. In this paper we offer a brief explication of commonality analysis; a step-by-step discussion of how communication researchers may perform commonality analyses using output from computer-assisted statistical analysis programs like SPSS; and we provide an extended example illustrating a commonality analysis.     

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.     

Relative Importance Analysis: A Useful Supplement to Regression Analysis

This article advocates for the wider use of relative importance indices as a supplement to multiple regression analyses. The goal of such analyses is to partition explained variance among multiple predictors to better understand the role played by each predictor in a regression equation. Unfortunately, when predictors are correlated, typically relied upon metrics are flawed indicators of variable importance. To that end, we highlight the key benefits of two relative importance analyses, dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. We also describe numerous situations where relative importance weights should be used, while simultaneously cautioning readers about the limitations and misconceptions regarding the use of these weights. Finally, we present step-by-step recommendations for researchers interested in incorporating these analyses in their own work and point them to available web resources to assist them in producing these weights.     

Multivariate relative importance: extending relative weight analysis to multivariate criterion spaces

For years, organizational scholars have sought effective ways to evaluate the importance of predictors included in a regression analysis. Recent techniques, such as general dominance weights and relative weights, have shown great promise for guiding evaluations of predictor importance. Nevertheless, questions remain regarding how one should investigate relative importance in the presence of a multidimensional criterion variable. The purpose of this article is to extend understanding of relative importance statistics to multivariate designs. The authors review the concept of relative importance and discuss a new procedure for calculating estimates of importance in the presence of multiple correlated criteria. Finally, a published correlation matrix is reanalyzed and a Monte Carlo simulation conducted to compare the new procedure with another technique for estimating importance. Unlike canonical solutions, which are often uninterpretable, the proposed multivariate relative weights provide an intuitive index regarding the relationship between predictors and criteria. Implications for organizational researchers are discussed.     

Exploratory regression analysis: A tool for selecting models and determining predictor importance

Linear regression analysis is one of the most important tools in a researcher’s toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at . The program investigates all 2 ^p – 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.     

Rethinking how family researchers model infrequent outcomes: a tutorial on count regression and zero-inflated models.

Marital and family researchers often study infrequent behaviors. These powerful psychological variables, such as abuse, criticism, and drug use, have important ramifications for families and society as well as for the statistical models used to study them. Most researchers continue to rely on ordinary least-squares (OLS) regression for these types of data, but estimates and inferences from OLS regression can be seriously biased for count data such as these. This article presents a tutorial on statistical methods for positively skewed event data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. These statistical methods are introduced through a marital commitment example, and the data and computer code to run the example analyses in R, SAS, SPSS, and Mplus are included in the online supplemental material. Extensions and practical advice are given to assist researchers in using these tools with their data.     

The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives

Count data reflect the number of occurrences of a behavior in a fixed period of time (e.g., number of aggressive acts by children during a playground period). In cases in which the outcome variable is a count with a low arithmetic mean (typically < 10), standard ordinary least squares regression may produce biased results. We provide an introduction to regression models that provide appropriate analyses for count data. We introduce standard Poisson regression with an example and discuss its interpretation. Two variants of Poisson regression, overdispersed Poisson regression and negative binomial regression, are introduced that may provide more optimal results when a key assumption of standard Poisson regression is violated. We also discuss the problems of excess zeros in which a subgroup of respondents who would never display the behavior are included in the sample and truncated zeros in which respondents who have a zero count are excluded by the sampling plan. We provide computer syntax for our illustrations in SAS and SPSS. The Poisson family of regression models provides improved and now easy to implement analyses of count data.

[Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Personality Assessment for the following free supplemental resources: the data set used to illustrate Poisson regression in this article, which is available in three formats—a text file, an SPSS database, or a SAS database.]     

优势分析方法及其应用

优势分析是近年来由Budescu等人新发展起来的一种确定多元回归方程中各预测变量相对重要性的方法。与传统方法相比,优势分析突出的特点是,全面比较了在由全模型所衍生出来的所有子模型情况下,各预测变量(X1,X…XP)在解释或预测标准变量у时,它们之间的相对重要性。本文从基本原理以及具体操作过程对这一新的统计分析方法进行了详细介绍。     

Opposition to Affirmative Action: Racial Affect and Traditional Value Predictors Across Four Programs1

Research on white opinions of such compensatory policies as busing and affirmative action has suggested that prejudice is the primary determinant of policy attitudes (Jacobson, 1985; McConahay, 1982). Often, however, racism is measured in a manner that confounds prejudice with values and concerns about justice. A study was conducted in which undergraduates (N= 185) were told that one of four affirmative-action programs for black students would be implemented at their university either in the following year or in 5 years. We found that: (a) support varied considerably across programs and was greater when implementation was imminent; (b) separate operationalizations of race prejudice and dispositional justice beliefs accounted for equal, and at times greater, variance in affirmative action opinions relative to a measure of symbolic racism; and (c) correlates of policy endorsement, including dispositional justice beliefs but not racial affect, varied from program to program. It is suggested that future research should explicitly distinguish race prejudice from values as predictors. It is also suggested that justice concerns, particularly regarding policy specifics, are important predictors of affirmative action attitudes that to date have largely been overlooked.     

A Macintosh and Windows program for assessing body-image disturbance using adjustable image distortion

Body image is one of the most important concepts in the study of eating disorders. The assessment and treatment of body-image issues are considered to be integral aspects of assessment and clinical management of eating disorders (Thompson, 1996b). The program, BodyImage, is software for the assessment of body-image disturbance. It uses an image-distorting technique to estimate body size. The image of the whole body or parts of the body can be captured as a digital image by a digital camera. Response data from participants are recorded as ASCII files so that other computer programs such as spreadsheets or word processing programs can handle the data. BodyImage works on personal computers, both Macintosh and Windows. It is available at no cost, and it can be obtained from the following URL: http://homepage2.nifty.com/s_shibata/softwares/bodyimage.html.     

Tracking the psychosocial predictors of older individuals' television uses

The elderly spend considerable amounts of time with mass media, but little is known about the psychology of their viewing habits. This study compared the relative impact of social, structural, and psychosocial variables on the television uses of elderly viewers. The results of a survey of 113 older respondents indicated that psychosocial factors accounted for variance above and beyond that of demographic and situational factors in viewing of television entertainment programs, parasocial programs (e.g., soap operas), as well as in watching of television for companionship purposes. Emotional loneliness and locus of control proved particularly significant predictors of television behavior.     

Tracking the Psychosocial Predictors of Older Individuals' Television Uses

The elderly spend considerable amounts of time with mass media, but little is known about the psychology of their viewing habits. This study compared the relative impact of social, structural, and psychosocial variables on the television uses of elderly viewers. The results of a survey of 113 older respondents indicated that psychosocial factors accounted for variance above and beyond that of demographic and situational factors in viewing of television entertainment programs, para-social programs (e.g., soap operas), as well as in watching of television for companionship purposes. Emotional loneliness and locus of control proved particularly significant predictors of television behavior.     

The dominance analysis approach for comparing predictors in multiple regression

A general method is presented for comparing the relative importance of predictors in multiple regression. Dominance analysis (D. V. Budescu, 1993), a procedure that is based on an examination of the R2 values for all possible subset models, is refined and extended by introducing several quantitative measures of dominance that differ in the strictness of the dominance definition. These are shown to be intuitive, meaningful, and informative measures that can address a variety of research questions pertaining to predictor importance. The bootstrap is used to assess the stability of dominance results across repeated sampling, and it is shown that these methods provide the researcher with more insights into the pattern of importance in a set of predictors than were previously available.     

An Extension of Dominance Analysis to Canonical Correlation Analysis

Dominance analysis (Budescu, 1993 Budescu, D. V. 1993. Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression.. Psychological Bulletin, 114: 542–551. [Crossref], [Web of Science ®] , [Google Scholar]) offers a general framework for determination of relative importance of predictors in univariate and multivariate multiple regression models. This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models. In this article we extend dominance analysis to canonical correlation analysis to explore the relative importance of the variables in both sets. The proposed extension provides (a) a decomposition of the models' fit into components associated with the individual variables; (b) the ability to compare the relative importance of variables from the two sets; (c) the ability to perform multistage analyses, involving all canonical variates; and (d) a bootstrapping inference procedure. The approach is illustrated with an empirical data example involving parenting styles and youth outcomes and its unique features are highlighted and discussed.     

Welfare policies and adolescents: exploring the roles of sibling care, maternal work schedules, and economic resources

This study uses data from three longitudinal experimental evaluations of US state welfare reform programs to examine whether program‐induced changes in families' reliance on sibling care are linked with the effects of welfare programs on selected schooling outcomes of high risk, low‐income adolescents. The findings from two of the welfare programs indicate that increased reliance on sibling care was concomitant with unfavorable effects of the programs on adolescent schooling outcomes. In the third welfare program examined, the program did not yield any increases in the use of sibling care or unfavorable effects on adolescent schooling outcomes, suggesting that sibling care is one likely contributor to the negative effects of welfare programs on adolescent schooling outcomes. These findings are discussed in terms of the pattern of the programs’ effects on families' income, as well as maternal work on nonstandard schedules, aside from the programs’ effects on maternal employment, which play contributory roles in shaping the extent to which welfare programs led to less favorable effects on the schooling outcomes of adolescents with younger siblings.