This study was designed to examine predictors of instructional beliefs related to problem solving that influence mathematics in-service teachers' practices in the Chilean context. A total of 713 in-service mathematics teachers from various elementary schools participated in the survey study during 2015 and 2016. Results showed that teachers'…

Peer play provides ample opportunities for the use and development of self-regulatory and mathematical skills. This study aimed at examining whether children's engagement in solitary low-level play, interactive play and competent play influences their self-regulatory and number sense skills. The effect of demographic variables and children's…

Critical to many science, technology, engineering, and mathematics (STEM) career paths is mathematical modeling--specifically, the creation and adaptation of mathematical models to solve problems in complex settings. Conventional standardized measures of mathematics achievement are not structured to directly assess this type of mathematical…

The relationship between complexity and usefulness can be captured by a U-shaped curve. This comment explores that relationship. Complexity may be useful for one of the main aims of developmental psychology (causal inference) but not for another (description of developmental phenomena). Currently, developmentalists conduct complex analyses that…

When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for…

Ridge regression is an approach to the problem of large standard errors of regression estimates of intercorrelated regressors. The effect of ridge regression on the estimated squared multiple correlation coefficient is discussed and illustrated. (JKS)

The first eigenvalue of a correlation matrix indicates the maximum amount of the variance of the variables which can be accounted for with a linear model by a single underlying factor. The first eigenvalue measures the primary cluster in the matrix, its number of variables and average correlation. (Author/RL)

Prior work has proposed different theoretical mechanisms to explain students' course-taking patterns in schools. On the one hand, there are oversocialized accounts that claim that rules, social background factors, and supply-side factors shape observed career patterns. On the other hand, there are undersocialized accounts that claim that the…

A reanalysis of Shaycroft's matrix of intercorrelations of 10 test variables plus 4 random variables is discussed. Three different procedures were used in the reanalysis: (1) Image Component Analysis, (2) Uniqueness Rescaling Factor Analysis, and (3) Alpha Factor Analysis. The results of these analyses are presented in tables. It is concluded from…

Following Conger's revised definition of suppressor variables, the universe relationships among two predictors and a criterion is analyzed. A simple mapping of relationships, based on the correlation between two predictors and the ratio of their validities, is provided. The relation between suppressor and part correlation is also discussed.…

A general model for prediction and association is described for the situation in which both criterion and predictor(s) are discrete variables. The approach can be employed for the general case involving any number of predictors, and the related measures of multiple and partial association are described. (Author/GK)

The Automatic Interaction Detector (AID) is discussed as to its usefulness in multiple regression analysis. The algorithm of AID-4 is a reversal of the model building process; it starts with the ultimate restricted model, namely, the whole group as a unit. By a unique splitting process maximizing the between sum of squares for the categories of…

A generalized measure of association and an associated test of significance are presented for nominal independent variables in which any number or combination of interval, ordinal, or nominal dependent variables can be analyzed. A permutation test of significance is provided for the new measure. (SLD)

Common applications of the part correlation coefficient are in causal regression models and estimation of suppressor variable effects. However, there is no statistical test of the significance of the difference between a zero-order correlation and a part correlation, nor between a pair of part correlations. Hotelling's t is used for contrasting:…

A definition of a suppressor variable is presented which is based on the relation of the semipartial correlation to the zero order correlation. Advantages of the definition are given. (Author/JKS)