Organizational commitment has been conceptualized and measured in various ways. The two studies reported here were conducted to test aspects of a three-component model of commitment which integrates these various conceptualizations. The affective component of organizational commitment, proposed by the model, refers to employees' emotional attachment to, identification with, and involvement in, the organization. The continuance component refers to commitment based on the costs that employees associate with leaving the organization. Finally, the normative component refers to employees' feelings of obligation to remain with the organization. In Study 1, scales were developed to measure these components. Relationships among the components of commitment and with variables considered their antecedents were examined in Study 2. Results of a canonical correlation analysis suggested that, as predicted by the model, the affective and continuance components of organizational commitment are empirically distinguishable constructs with different correlates. The affective and normative components, although distinguishable, appear to be somewhat related. The importance of differentiating the components of commitment, both in research and practice, is discussed.

The measurement and antecedents of affective, continuance and normative commitment to the organization

The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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mice: Multivariate Imputation by Chained Equations in R

Structural Equations with Latent Variables.

This paper discusses the role of individuals' beliefs about their abilities to competently use computers (computer self-efficacy) in the determination of computer use. A survey of Canadian managers and professionals was conducted to develop and validate a measure of computer self-efficacy and to assess both its impacts and antecedents. Computer self- efficacy was found to exert a significant influence on individuals' expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), as well as their actual computer use. An individual's self-efficacy and outcome expecta- tions were found to be positively influenced by the encouragement of others in their work group, as well as others' use of computers. Thus, self-efficacy represents an important individual trait, which moderates organizational influences (such as encouragement and support) on an individual's decision to use computers. Understanding self-efficacy, then, is important to the successful implementation of systems in organizations. The existence of a reliable and valid measure of self-efficacy makes assessment possible and should have implications for organizational support, training, and implementation.

https://www.jstor.org/stable/pdfplus/249688.pdf

Computer self-efficacy: development of a measure and initial test

A key concern in Information Systems (IS) research has been to better understand the linkage between information systems and individual performance. The research reported in this study has two primary objectives: (1) to propose a comprehensive theoretical model that incorporates valuable insights from two complementary streams of research, and (2) to empirically test the core of the model. At the heart of the new model is the assertion that for an information technology to have a positive impact on individual performance, the technology: (1) must be utilized and (2) must be a good fit with the tasks it supports. This new model is moderately supported by an analysis of data from over 600 individuals in two companies. This research highlights the importance of the fit between technologies and users' tasks in achieving individual performance impacts from information technology. It also suggests that task-technology fit when decomposed into its more detailed components, could be the basis for a strong diagnostic tool to evaluate whether information systems and services in a given organization are meeting user needs.

https://www.jstor.org/stable/pdfplus/249689.pdf

Task-technology fit and individual performance

One of the dilemmas facing those who teach sociological methods and statistics these days is how to present the three main applied analytical models which derive from the general linear hypothesis-analysis of variance, regression, and analysis of covariance. The reason for this dilemma is that whereas there now exist in the sociological literature a number of theoretical expositions integrating these various models, nowhere has there existed a reference or, for that matter, a set of references which provided the computational integration in sufficient clarity that the teacher could assign them to his class and be assured that the student would obtain a clear picture of how the three models were computationally interrelated and interchangeable. Kerlinger and Pedhazur have painstakingly provided such a resource. For those looking for such a text (or reference book), it is a teacher's delight! The authors provide one with a consistency of framework which opens in Part 1 (five chapters). Those chapters are a review of the foundations of multiple regression and can be easily read by students who have had an introductory course in statistics. The review is, however, more than just a rehash of regression theory and procedures, as the authors are also developing a framework for the later integration of analysis of variance, analysis of covariance, time series analysis, path analysis and multivariate analysis (multivariate analysis of variance, canonical regression, and discriminant analysis). Part 2, which consists of six chapters, is the focal point of the book. For example, chapters 5, 6, and 7 give an introduction to the use of dummy coding to achieve the same results as one gets in one-way analysis of variance. Chapter 8 extends the procedures to multiple categorical variables and how they can be handled in the multiple regression framework to achieve the same results one would obtain via ANOV computational procedures in factorial designs. Chapter 9 departs from this theme to open considerations of testing for linear and curvilinear regression when working with continu'ous variables. Chapter 10 weaves these considerations into those developed earlier regarding categorical variables and discusses regression procedures for handling both continuous and categorical regressors in the same equation. (I have found this to be a topic of great interest among sociology students who wonder how to use

Multiple regression in behavioral research

Multiple Regression in Behavioral Research.

How do attitudes toward education affect perceptions of desirable traits of teachers? Are perceptions of desirable teacher traits congruent with the attitudes of the perceiving individual? The basic hypothesis of this study was: Perceptions of the characteristics of the "good" or effective teacher are in part determined by the perceiver's attitudes toward education. Previous research indicates that two basic factors, "progressivism" and "traditionalism," underlie attitudes toward education (Kerlinger, 1967a). Two basic factors, "person orientation" and "task orientation," seem also to underlie perceptions of desirable teacher characteristics (Kerlinger, 1967b). Another mode of expressing our hypothesis is to say that the two perceptual factors

/pdf/educational-attitudes-and-perceptions-of-desirable-traits-of-2ln6pgpw5o.pdf

Elazar J. Pedhazur

Papers

Multiple regression in behavioral research

Multiple Regression in Behavioral Research.

Multiple Regression in Behavioral Research.

Educational Attitudes and Perceptions of Desirable Traits of Teachers

Multiple Regression in Behavioral Research