Two SPSS programs for interpreting multiple regression results

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http:// brm.psychonomic-journals.org/content/supplemental.     

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.     

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.     

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.]     

Dynamic melody recognition: Distinctiveness and the role of musical expertise

The hypothesis that melodies are recognized at moments when they exhibit a distinctive musical pattern was tested. In a melody recognition experiment, point-of-recognition (POR) data were gathered from 32 listeners (16 musicians and 16 nonmusicians) judging 120 melodies. A series of models of melody recognition were developed, resulting from a stepwise multiple regression of two classes of information relating to melodic familiarity and melodic distinctiveness. Melodic distinctiveness measures were assembled through statistical analyses of over 15,000 Western themes and melodies. A significant model, explaining 85% of the variance, entered measures primarily of timing distinctiveness and pitch distinctiveness, but excluding familiarity, as predictors of POR. Differences between nonmusician and musician models suggest a processing shift from momentary to accumulated information with increased exposure to music. Supplemental materials for this article may be downloaded from http://mc.psychonomic-journals.org/content/supplemental.     

The effect of different soothing interventions on infant crying and on parent–infant interaction

Conducted in the home environment, this study examines the effects of three complementary soothing techniques on the duration of infant crying and on caregiver–infant interaction. In one group, the caregivers apply massage; in another, they provide supplemental carrying to their infants, and in the last, the caregivers both massage and provide supplemental carrying. Measurements, for the one control and three treatment groups took place prenatally (third trimester) and in the first 16 weeks postpartum for parental perception of infant temperament, parental sense of competence, parent–infant interaction, and the duration of infant crying. Whereas analyses indicate no statistically significant differences between groups in reducing infant crying, results approached significance (p ≤ .06) in favor of the combined supplemental carrying/massage group. These results challenge accepted beliefs that tactile stimulation and/or supplemental carrying enhance parental sense of competence, positive parental perceptions, and interactions with infants. Practitioners can apply these conclusions when counselling parents on the advisability of selecting any one complementary soothing technique. ©2002 Michigan Association for Infant Mental Health.     

Developing a biodata measure and situational judgment inventory as predictors of college student performance

This article describes the development and validation of a biographical data (biodata) measure and situational judgment inventory (SJI) as useful predictors of broadly defined college student performance outcomes. These measures provided incremental validity when considered in combination with standardized college-entrance tests (i.e., SAT/ACT) and a measure of Big Five personality constructs. Racial subgroup mean differences were much smaller on the biodata and SJI measures than on the standardized tests and college grade point average. Female students tended to outperform male students on most predictors and outcomes with the exception of the SAT/ACT. The biodata and SJI measures show promise for student development contexts and for selecting students on a wide range of outcomes with reduced adverse impact.     

Identifying predictors of treatment response

This article provides a rationale for considering predictors of growth in a treatment group as inadequate to identifying predictors of treatment response. When we interpret predictors of growth in a treatment group as synonymous with predictors of treatment response, we implicitly attribute all of the treated children's growth to the treatment, an untenable assumption under most conditions. We also contend that the use of standard scores in predictors of growth studies does not allow us to differentiate growth from treatment, from growth from other factors. We present two research methodologies that are appropriate methods of identifying predictors of treatment response: (a) single-subject experimental logic utilized to identify the specific participants in which treatment responses (not just growth) were found, combined with follow-up group comparison logic to identify the characteristics on which responders and nonresponders differ, and (b) statistical interactions among child/family/context characteristics and randomly assigned group membership. Principles for selecting potential predictors of treatment response are provided.     

Cognitive aspects of lexical availability

Lexical availability measures the ease with which a word can be generated as a member of a given category. It has been developed by linguistic studies aimed, among other things, at devising a rational basis for selecting words for inclusion in dictionaries. The measure accounts for the number of people who generated a given word as a member of a designated semantic category and the position in which they produce the word. We present an analysis of lexical availability from a cognitive perspective. Data were analysed for Spanish speakers generating words from five semantic categories—clothes, furniture, body parts, animals, and intelligence. Six properties of words were investigated as potential predictors of lexical availability. Predictors were concept familiarity, typicality, imageability, age of acquisition, word frequency, and word length. Categories differed on these variables, and regression analysis found concept familiarity, typicality, and age of acquisition to be significant predictors of lexical availability. The cognitive basis of these findings and the practical consequences of selecting words on the basis of lexical availability are considered.     

Estimating confidence intervals for eigenvalues in exploratory factor analysis

Exploratory factor analysis (EFA) has become a common procedure in educational and psychological research. In the course of performing an EFA, researchers often base the decision of how many factors to retain on the eigenvalues for the factors. However, many researchers do not realize that eigenvalues, like all sample statistics, are subject to sampling error, which means that confidence intervals (CIs) can be estimated for each eigenvalue. In the present article, we demonstrate two methods of estimating CIs for eigenvalues: one based on the mathematical properties of the central limit theorem, and the other based on bootstrapping. References to appropriate SAS and SPSS syntax are included. Supplemental materials for this article may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.     

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights’ estimates unstable (i.e., the “bouncing beta” problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.     

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.     

What’s in a name? The role of graphics,functions, and their interrelationships in icon identification

Communication using icons is now commonplace. It is therefore important to understand the processes involved in icon comprehension and the stimulus cues that individuals utilize to facilitate identification. In this study, we examined predictors of icon identification as participants gained experience with icons over a series of learning trials. A dynamic pattern of findings emerged in which the primary predictors of identification changed as learning progressed. In early learning trials, semantic distance (the closeness of the relationship between icon and function) was the best predictor of performance, accounting for up to 55% of the variance observed, whereas familiarity with the function was more important in later trials. Other stimulus characteristics, such as our familiarity with the graphic in the icon and its concreteness, were also found to be important for icon design. The theoretical implications of these findings are discussed, with particular emphasis on the parallels with picture naming. The icon identification norms from this study may be downloaded from brm.psychonomic-journals .org/content/supplemental.     

Objective age of acquisition for 223 Italian words: Norms and effects on picture naming speed

The present study provides a set of objective age of acquisition (AoA) norms for 223 Italian words that may be useful for conducting cross-linguistic studies or experiments on Italian language processing. The data were collected by presenting children from the ages of 2 to 11 with a normed picture set (Lotto, Dell’Acqua, & Job, 2001). Following the study of Morrison, Chappell, and Ellis (1997), we report two measures of objective AoA. Both measures strongly correlated with each other, and they also showed a good correlation with the rated AoA provided by adult participants. Furthermore, we assessed the relationship between the AoA measures and other variables used in psycholinguistic experiments. Regression analyses showed that familiarity, typicality, and word frequency were significant predictors of AoA. AoA, but not word frequency, was found to determine naming latencies. Finally, we present a path model in which AoA is a mediator in predicting speed in picture naming. The norms and the picture set can also be downloaded from http://dpss.psy.unipd.it/files/strumenti.php and from http://brm.psychonomic-journals.org/content/supplemental.     

Desistance from Intimate Partner Violence: the Role of Legal Cynicism, Collective Efficacy, and Social Disorganization in Chicago Neighborhoods

This paper examined the relationship between reported Intimate Partner Violence (IPV) desistance and neighborhood concentrated disadvantage, ethnic heterogeneity, residential instability, collective efficacy and legal cynicism. Data from the Project on Human Development in Chicago Neighborhoods (PHDCN) Longitudinal survey were used to identify 599 cases of IPV in Wave 1 eligible for reported desistance in Wave 2. A Generalized Boosting Model was used to determine the best proximal predictors of IPV desistance from the longitudinal data. Controlling for these predictors, logistic regression of neighborhood characteristics from the PHDCN community survey was used to predict reported IPV desistance in Wave 2. The paper finds that participants living in neighborhoods high in legal cynicism have lower odds of reporting IPV desistance, controlling for other variables in the logistic regression model. Analyses did not find that IPV desistance was related to neighborhood concentrated disadvantage, ethnic heterogeneity, residential instability and collective efficacy.     

Comparing regressions when measurement error variances are known

In a multiple (or multivariate) regression model where the predictors are subject to errors of measurement with a known variance-covariance structure, two-sample hypotheses are formulated for (i) equality of regressions on true scores and (ii) equality of residual variances (or covariance matrices) after regression on true scores. The hypotheses are tested using a large-sample procedure based on maximum likelihood estimators. Formulas for the test statistic are presented; these may be avoided in practice by using a general purpose computer program. The procedure has been applied to a comparison of learning in high schools using achievement test data.     

A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables

Uncorrectable skew and heteroscedasticity are among the "lemons" of psychological data, yet many important variables naturally exhibit these properties. For scales with a lower and upper bound, a suitable candidate for models is the beta distribution, which is very flexible and models skew quite well. The authors present maximum-likelihood regression models assuming that the dependent variable is conditionally beta distributed rather than Gaussian. The approach models both means (location) and variances (dispersion) with their own distinct sets of predictors (continuous and/or categorical), thereby modeling heteroscedasticity. The location sub-model link function is the logit and thereby analogous to logistic regression, whereas the dispersion sub-model is log linear. Real examples show that these models handle the independent observations case readily. The article discusses comparisons between beta regression and alternative techniques, model selection and interpretation, practical estimation, and software.