So you think you are an ally? Effects of (in)congruence between men's self-perceptions and women's perceptions of men's values and allyship on women's inclusion and vitality

As public consciousness of sexism is increasing in the workplace (e.g., #MeToo movement), labelling oneself as an ally (e.g., UN HeforShe campaign) is becoming more socially desirable for men. However, do women agree with such men in their assessments of being allies? Importantly, how does women's agreement (or not) with men's self-assessments of allyship affect women's inclusion-relevant outcomes? Using a multi-informant design and data from 101 men–women colleague pairs, this study considered men's self-perceptions and women's other-reports of men's key allyship-relevant characteristics—justice, moral courage, civility and allyship. Polynomial regression and response surface analyses revealed differential impacts of (in)congruence between men's and women's perceptions on women's sense of inclusion and vitality. Simply, when women perceived men as higher (or the same) in justice, moral courage and civility than men reported themselves, it positively predicted women's outcomes. This suggests that humble self-presentation by men on characteristics that are parallel to allyship (but not allyship) may be ideal. Yet, both under- and overestimation by men on allyship itself predicted poorer outcomes for women, suggesting that the ideal is for men to have an accurate assessment of their own strengths and weaknesses as an ally.     

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.     

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets

Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components (PCs) is maximized. We propose a penalized version of non-linear PCA for ordinal variables that is a smoothed intermediate between standard PCA on category labels and non-linear PCA as used so far. The new approach is by no means limited to monotonic effects and offers both better interpretability of the non-linear transformation of the category labels and better performance on validation data than unpenalized non-linear PCA and/or standard linear PCA. In particular, an application of penalized optimal scaling to ordinal data as given with the International Classification of Functioning, Disability and Health (ICF) is provided.     

An alternating least squares method for the weighted approximation of a symmetric matrix

Bailey and Gower examined the least squares approximationC to a symmetric matrixB, when the squared discrepancies for diagonal elements receive specific nonunit weights. They focussed on mathematical properties of the optimalC, in constrained and unconstrained cases, rather than on how to obtainC for any givenB. In the present paper a computational solution is given for the case whereC is constrained to be positive semidefinite and of a fixed rankr or less. The solution is based on weakly constrained linear regression analysis.The authors are obliged to John C. Gower for stimulating this research.     

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.     

Interpreting canonical correlation analysis through biplots of structure correlations and weights

This paper extends the biplot technique to canonical correlation analysis and redundancy analysis. The plot of structure correlations is shown to the optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate regression and canonical correlation analysis/redundancy analysis is exploited for producing an optimal biplot that displays a matrix of regression coefficients. This plot can be made from the canonical weights of the predictors and the structure correlations of the criterion variables. An example is used to show how the proposed biplots may be interpreted.     

The creative potential of play and regression in analytical training: a personal reflection

The seminar group can serve as a framework in which to meet the fear generated during training. The group can become a maddening environment, but on occasions when it is experienced by the trainee as good enough, it can act like a playgroup which facilitates development. Experiences in the group can be viewed from the perspectives of group dynamics or of transference phenomena which embody regression. When this regression enables scope for play it can serve in the interests of ego development in the process of discovering the sort of analyst the trainee is becoming as the training unfolds. During play aspects of oneself can merge, then differentiate again. They can have different meanings at the same time. The rational and emotional, the adult and the childlike are all given a place in play.     

Assessing the stability of principal components using regression

This paper presents an analysis, based on simulation, of the stability of principal components. Stability is measured by the expectation of the absolute inner product of the sample principal component with the corresponding population component. A multiple regression model to predict stability is devised, calibrated, and tested using simulated Normal data. Results show that the model can provide useful predictions of individual principal component stability when working with correlation matrices. Further, the predictive validity of the model is tested against data simulated from three non-Normal distributions. The model predicted very well even when the data departed from normality, thus giving robustness to the proposed measure. Used in conjunction with other existing rules this measure will help the user in determining interpretability of principal components.The authors would like to thank the four anonymous reviewers and the two editors for their valuable comments. Atanu R. Sinha gratefully acknowledges the research support received from the Marketing Studies Center, AGSM, UCLA. Send requests for reprints to Atanu R. Sinha, B418 Gold Hall, 110 Westwood Plaza, Los Angeles, CA 90095.     

Isotonic ordinal probabilistic models (ISOP)

The concept of an ordinal instrumental probabilistic comparison is introduced. It relies on an ordinal scale given a priori and on the concept of stochastic dominance. It is used to define a weakly independently ordered system, or isotonic ordinal probabilistic (ISOP) model, which allows the construction of separate sample-free ordinal scales on a set of subjects and a set of items. The ISOP-model is a common nonparametric theoretical structure for unidimensional models for quantitative, ordinal and dichotomous variables.Fundamental theorems on dichotomous and polytomous weakly independently ordered systems are derived. It is shown that the raw score system has the same formal properties as the latent system, and therefore the latter can be tested at the observed empirical level.I wish to thank 3 reviewers and 2 editors who contributed a lot to the readability and precision of the article.     

The stability of depressive symptoms in college students: An empirical demonstration of regression to the mean

In a recent paper, Vredenburg, Flett, and Krames (1993) hypothesized that the apparent instability of depressive symptom scores in college students may be due, in part, to the phenomenon known as statistical regression to the mean. This statistical principle was demonstrated in the current study. A sample of 183 university students completed the Beck Depression Inventory (BDI) at two timepoints separated by a 3-month interval. Consistent with past results, analyses revealed substantial changes in symptom scores over time with decrements being evident among many subjects with elevated symptom scores at Time 1. Examination of the amount of change over time in BDI scores indicated a pattern of findings that approximated the regression to the mean phenomenon. Statistical tests confirmed that regression to the mean accounted for a significant amount of the change in symptom scores over time. The implications of these findings are discussed in terms of the nature of depressive symptoms in students and the inappropriateness of assigning subjects to depressed or nondepressed groups on the basis of elevated scores on a self-report measure.