We illustrate the idiographic/nomothetic debate by comparing 3 approaches to using daily self-report data on affect for predicting relationship quality and breakup. The 3 approaches included (a) the first day in the series of daily data; (b) the mean and variability of the daily series; and (c) parameters from dynamic factor analysis, a…

Sorting procedures are frequently adopted as an alternative to dissimilarity ratings to measure the dissimilarity of large sets of stimuli in a comparatively short time. However, systematic empirical research on the consequences of this experiment-design choice is lacking. We carried out a behavioral experiment to assess the extent to which…

Incomplete or missing data is a common problem in almost all areas of empirical research. It is well known that simple and ad hoc methods such as complete case analysis or mean imputation can lead to biased and/or inefficient estimates. The method of maximum likelihood works well; however, when the missing data mechanism is not one of missing…

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an…

Time-use has already been the subject of numerous studies across multiple disciplines such as economics, marketing, sociology, transportation and urban planning. However, most of this research has focused on comparing demographic groups on a few broadly defined activities (e.g., work for pay, leisure, housework, etc.). In this study we take a…

The advantages and disadvantages of balanced incomplete block designs are clarified and their use is demonstrated with an empirical example. A procedure for reducing data of this type to analyzable form is proposed, and an analytical approach that is appropriate for the resulting data is illustrated. (Author/BW)

Discusses ipsatizing variables prior to component analysis by subtracting the mean score of each individual from all the scores of that individual, showing technical objections to component analysis of ipsatized variables to be based on erroneous premises. Suggests partialling the mean component as a superior procedure. (SLD)

This Monte Carlo study examined the impact of misspecifying the [big sum] matrix in longitudinal data analysis under both the multilevel model and mixed model frameworks. Under the multilevel model approach, under-specification and general-misspecification of the [big sum] matrix usually resulted in overestimation of the variances of the random…

Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap…

The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal…

The use of analysis of covariance in simple repeated measures designs is considered. Conditions necessary for the analysis of covariance adjusted main effects and interactions to be meaningful are presented. (Author/JKS)

Through Monte Carlo simulation, small sample methods for evaluating overall data-model fit in structural equation modeling were explored. Type I error behavior and power were examined using maximum likelihood (ML), Satorra-Bentler scaled and adjusted (SB; Satorra & Bentler, 1988, 1994), residual-based (Browne, 1984), and asymptotically…

Computer generated data representative of 16 Auto Regressive Integrated Moving Averages (ARIMA) models were used to compare the results of interrupted time-series analysis using: (1) the known model identification, (2) an assumed (l,0,0) model, and (3) an assumed (3,0,0) model as an approximation to the General Transformation approach. (Author/BW)

The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)