Book•
Multiple regression in behavioral research
01 Jan 1973-
TL;DR: Kerlinger and Pedhazur as discussed by the authors present the three main applied analytical models which derive from the general linear hypothesis-analysis of variance, regression, and analysis of covariance.
Abstract: 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
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TL;DR: In this article, the authors proposed a three-component model of organizational commitment, which integrates emotional attachment, identification with, and involvement in the organization, and the normative component refers to employees' feelings of obligation to remain with the organization.
Abstract: 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.
10,654 citations
TL;DR: 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.
Abstract: 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.
10,234 citations
TL;DR: Self-efficacy represents an important individual trait, which moderates organizational influences on an individual's decision to use computers, and is important to the successful implementation of systems in organizations.
Abstract: 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.
5,717 citations
TL;DR: This research highlights the importance of the fit between technologies and users' tasks in achieving individual performance impacts from information technology and 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.
Abstract: 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.
4,809 citations
TL;DR: This study empirically test a model of knowledge contribution and finds that people contribute their knowledge when they perceive that it enhances their professional reputations, when they have the experience to share, and when they are structurally embedded in the network.
Abstract: Electronic networks of practice are computer-mediated discussion forums focused on problems of practice that enable individuals to exchange advice and ideas with others based on common interests. However, why individuals help strangers in these electronic networks is not well understood: there is no immediate benefit to the contributor, and free-riders are able to acquire the same knowledge as everyone else. To understand this paradox, we apply theories of collective action to examine how individual motivations and social capital influence knowledge contribution in electronic networks. This study reports on the activities of one electronic network supporting a professional legal association. Using archival, network, survey, and content analysis data, we empirically test a model of knowledge contribution. We find that people contribute their knowledge when they perceive that it enhances their professional reputations, when they have the experience to share, and when they are structurally embedded in the network. Surprisingly, contributions occur without regard to expectations of reciprocity from others or high levels of commitment to the network.
4,636 citations
Cites background from "Multiple regression in behavioral r..."
...A classical suppressor variable is a variable that has a zero-order correlation with the dependent variable, but is correlated with one or more predictor variables and leads to improved predic tion when included in multiple regression analysis (Pedhazur 1982)....
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