Ronald S. Landis

A Comparison of Approaches to Forming Composite Measures in Structural Equation Models

A common practice in applications of structural equation modeling techniques is to create composite measures from individual items. The purpose of this article was to provide an empirical comparison of several composite formation methods on model fit. Data from 1, 177 public school teachers were used to test a model of union commitment in which alternative composite formation methods were used to specify the measurement components of the model. Bootstrapping procedures were used to generate data for two additional sample sizes. Results indicated that the use of composites, in general, resulted in improved overall model fit as compared to treating all items as individual indicators. Lambda values and explained criterion variance indicated that this improved model fit was due to the creation of strong measurement models. Implications of these results for researchers using composites are discussed.

Applicant preferences for Web-based versus traditional job postings

The purpose of the current study was to assess the extent to which applicants preferred Web-based job postings to traditional paper-based materials. An integration of the traditional recruitment literature as well as the burgeoning literature related to the use of computer technology led to the development of two primary study hypotheses. First, applicants were expected to prefer Web-based job postings to more traditional paper postings. Second, applicants were expected to prefer jobs posted on Web pages of higher quality to those posted on pages of lower quality. Data collected from 92 undergraduate students provided support for the second hypothesis. Contrary to the first hypothesis, however, paper postings were preferred to those in a Web-based format. A discussion of the implications of these findings is presented along with recommendations for future research in the area.

Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems

This chapter emphasizes the importance of using multi-channel trace data to examine the complex roles of cognitive, affective, and metacognitive (CAM) self-regulatory processes deployed by students during learning with multi-agent systems. We argue that tracing these processes as they unfold in real-time is key to understanding how they contribute both individually and together to learning and problem solving. In this chapter we describe MetaTutor (a multi-agent, intelligent hypermedia system) and how it can be used to facilitate learning of complex biological topics and as a research tool to examine the role of CAM processes used by learners. Following a description of the theoretical perspective and underlying assumptions of self-regulated learning (SRL) as an event, we provide empirical evidence from five different trace data, including concurrent think-alouds, eye-tracking, note taking and drawing, log-files, and facial recognition, to exemplify how these diverse sources of data help understand the complexity of CAM processes and their relation to learning. Lastly, we provide implications for future research of advanced leaning technologies (ALTs) that focus on examining the role of CAM processes during SRL with these powerful, yet challenging, technological environments.

Moderated Multiple Regression Tests are Criterion Specific

This article examines how the ordering of variables impacts conclusions regarding the presence of a significant interactive relationship in moderated multiple regression (MMR). It is argued that whenever the designation of a criterion is arbitrary, researchers should consider performing two MMR analyses: one in which y is treated as the criterion, x the predictor, and m the moderator and a second in which x is the criterion and y the predictor. An example illustrates that these analyses are not symmetrical and that a significant interaction may be observed in one case but not the other. A simulation further illustrates conditions under which such effects are likely to occur. Implications for researchers studying interactions are discussed.

The Earth Is Not Round (p = .00)

Continued discussion and debate regarding the appropriate use of null hypothesis significance testing (NHST) has led to greater reliance on effect size testing (EST) in published literature. This article examines the myth that uncritical replacement of NHST with EST will improve our science. The use of NHST and EST is described along with a summary of the arguments offered in support and against both. After addressing the veracity of these assertions, the article describes the concept of the translation mechanism and compares the success of NHST and EST as mechanisms. Finally, the authors suggest changes that may facilitate translation in future research.

Interpretations of Multiple Regression Borrowed From Factor Analysis and Canonical Correlation

Abstract Because multiple regression (MR) is a simplified case of canonical correlation, the authors present an alternative interpretation of MR in which loadings, in addition to weights, may be computed for predictors. The loadings, computed by both canonical correlation and factor analysis, represent the correlations of each predictor with the predicted criterion score. The authors concluded that in some situations, researchers should examine those values in addition to testing for the significance of beta weights.

An Examination of the Relationship between Use of the Internet as a Recruitment Source and Student Attitudes.

The purpose of the current study was to investigate the extent to which applicant use of the Internet during recruitment was related to post-entry attitudes. Based on the extant organizational recruitment literature, a primary hypothesis was that Internet recruitment would be perceived as presenting less realistic information to applicants as compared to informal forms of recruitment. In addition, greater applicant use of Internet-based recruiting information was expected to be associated with lower satisfaction with the organization. The data did not support the hypotheses; however, study limitations that may explain the observed findings are described. Finally, suggestions for future research involving theory building and Internet recruitment are offered.

Self-Regulation and Performance in High-Fidelity Simulations: An Extension of Ego-Depletion Research

This article extends the research literature related to “ego-depletion.” Although numerous studies have focused on the self-regulatory failure associated with ego-depletion, the extant literature is generally characterized by relatively simple behavioral manipulations and dependent measures. Two studies are described that extend previous ego-depletion findings by employing a high-fidelity, customer service simulation as an ego-depleting manipulation (Study 1) and by using performance on a cognitively demanding naval combat simulator as a dependent measure (Study 2). Results of both studies show the generalizability of the effects of ego-depletion; the implications for self-regulatory failure in more naturalistic settings are discussed.

A note on the stability of team performance.

Yearly winning percentages of 23 professional basketball teams over a 10-year period were used to evaluate the stability of team performance. The intercorrelation matrix produced by these data is characterized by strong, positive correlations in adjacent time periods. As the number of intervening time periods increased, however, the observed correlations systematically decreased and ultimately became negative. Significant negative correlations of earlier performance with later performance are almost never observed with typical time-related performance data. Possible explanations and boundary conditions for these atypical results are discussed.

Examining the predictive relationship between personality and emotion traits and students' agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (