What causes the difference in mean structures between online and paper-and-pencil administrations?: Examination using the Self-report Depression Scale

Due to the many advantages of online surveys, many researchers are taking advantage of this survey method. Although for many psychological instruments, previous studies have shown that online and paper-and-pencil administration formats have equivalent results, other studies have shown that some online surveys result in different score distributions from those observed through paper-and-pencil administration. In this study, we conducted surveys using Zung’s self-report depression scale (SDS) to Japanese undergraduates both online and through paper-and-pencil and examined whether there are differences between different administration formats only in the scale’s mean structures and, if so, why the difference occurs. Analysis results showed that there was the difference only in mean structures. Results also implied that the online administration format lowers item thresholds; this decrease would cause the difference between the two formats’ mean structures. Finally, we think about the future directions of this research; to examine whether similar results would be seen in other scales, other countries, and other generations.

What causes the difference in mean structures between online and paper-and-pencil administrations?: Examination using the Self-report Depression Scale

Due to the many advantages of online surveys, many researchers are taking advantage of this survey method. Although for many psychological instruments, previous studies have shown that online and paper-and-pencil administration formats have equivalent results, other studies have shown that some online surveys result in different score distributions from those observed through paper-and-pencil administration. In this study, we conducted surveys using Zung’s self-report depression scale (SDS) to Japanese undergraduates both online and through paper-and-pencil and examined whether there are differences between different administration formats only in the scale’s mean structures and, if so, why the difference occurs. Analysis results showed that there was the difference only in mean structures. Results also implied that the online administration format lowers item thresholds; this decrease would cause the difference between the two formats’ mean structures. Finally, we think about the future directions of this research; to examine whether similar results would be seen in other scales, other countries, and other generations.

A Mixed-Effects Location Scale Model for Dyadic Interactions

We present a mixed-effects location scale model (MELSM) for examining thedaily dynamics of affect in dyads. The MELSM includes person and timevarying variables to predict the location, or individual means, and the scale,or within-person variances. It also incorporates a sub-model to account forbetween-person variances. The dyadic specification can accommodate individual and partner effects in both the location and the scale components,and allows random effects for all location and scale parameters. All covariances among the random effects, within and across the location and the scaleare also estimated. These covariances offer new insights into the interplayof individual mean structures, intra-individual variability, and the influenceof partner effects on such factors. To illustrate the model, we use data from274 couples who provided daily ratings on their positive and negative emotions toward their relationship – up to 90 consecutive days. The model is fitusing Hamiltonian Monte Carlo methods, and includes subsets of predictorsin order to demonstrate the flexibility of this approach. We conclude witha discussion on the usefulness and the limitations of the MELSM for dyadicresearch.

A Simulation Study on Clustering Multivariate Time Series Using Kernel Variant Multi-Way Principal Component Analysis

Conventional time series modeling may not satisfy the model validity for short-period time series data. In this study, we apply the Kernel Variant Multi-Way Principal Component Analysis (KMPCA) to cluster multivariate time series data which havemultiple dimensions with auto- and cross-correlations. We then check whether this method works well in clustering those data by employing simulation for generalization. Two simulation studies with two different mean structures with nine combinations of auto- and cross-correlations were conducted. The results showed that KMPCA cluster two different mean structure groups over 90% success rates with an appropriate kernel function. We also found that when the mean structures are the same, auto-correlation, the number of temporal points, and the kernel function parameter have the statistically significant effects on clustering performance. The second and third order interaction effects with each of those factors also have effects on clustering success rates. Among the effects of the main factors, the kernel function parameter is the most critical factor to consider for obtaining better performance. A similar error structure may obstruct the clustering performance: strong cross-correlation, weak auto-correlation, and a larger number of temporal points. The paper also discussed some limitations of the KMPCA model and suggested directions for future research that could improve the model.

Multivariate Fractional Regression Estimation of Econometric Share Models

This paper describes and applies econometric strategies for estimating regression models of economic share data outcomes where the shares may take boundary values (zero and 1) with nontrivial probability. The main focus of the paper is on the conditional mean structures of such data. The paper proposes an extension of the fractional regression methodology proposed by (Papke, L. E., and J. M. Wooldridge. 1996. “Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates.”