Validation and measurement invariance of the multidimensional qualitative job insecurity scale

This contribution introduces the Multidimensional Qualitative Job Insecurity Scale (MQJIS). Drawing from the qualitative job insecurity literature and addressing some of other scales’ limitations, a multidimensional model is proposed and investigated by means of confirmatory factor analysis and multilevel confirmatory factor analysis. Study 1 aims to explore the psychometric properties and factorial structure of MQJIS in an Italian sample of blue-collar workers (N?=?583), showing that a model with one higher-order factor (i.e., qualitative job insecurity) and four dimensions (i.e., social relationships, employment conditions, working conditions, and work content) shows a good fit to the data and good reliability indices. Study 2 aims to investigate MQJIS measurement invariance across several groups, based on country of origin, age, and gender. Results on a sample of Belgian and Italian workers (N?=?710) show that MQJIS met the criteria for uniqueness invariance across genders and scalar invariance across countries and age groups. Significance, implications, and future directions stemming from the initial validation and the confirmed measurement invariance of this scale are discussed.

     

Use of dimensional analysis in social science research

Dimensional analysis is a branch of applied mathematics which is only now beginning to emerge as an important methodology in social and behavioral science research. Unlike regression analysis, dimensional analysis “discovers” the functional relationship in the data from the denned dimensions; hence its ability to extend regression analysis in developing predictive models makes it particularly useful in social science research. In addition, many of the limitations with the use of regression analysis in social science which limit its usefulness in applications, such as multicollinearity, normality assumptions, homoscedasticity assumptions, etc. are not limitations in dimensional analysis. In this study, the application of dimensional analysis to the study of population migrations, specifically white flight in a large urban school district, is presented. Finally, a review of relevant literature and the use of dimensional analysis in other social science areas are presented.     

The application of generalizability analysis to observational studies

Research in the social and behavioral sciences is relying more on records of behavior made by human observers in naturalistic settings. Guidelines for these types of observational studies have emphasized the need to provide information on reliability. In this article, we present generalizability theory as a method for designing, assessing, and improving the reliability of observational studies.     

The Role of Behavioral Survey Methodologists in National Statistical

The expertise of behavioral survey methodologists is needed in national statistical agencies because of the necessity of using theory and research from the social sciences to reduce survey error. In this paper various social science based explanations for measurement error and nonresponse error are described in order to illustrate the conceptual foundations of such error reduction efforts. Three roles for behavioral survey methodologists in national statistical agencies are then discussed. They include: 1) bring an error reduction perspective to bear in an influential way for all aspects of designing and implementing agency surveys; 2) bring theoretical efficiency and effectiveness to experimental tests of alternative questionnaire designs and implementation procedures through the use of theories, concepts and pretests and findings of past behavioral science research; and 3) contribute to the expanding science of survey methodology.     

Inconsistencies Across Three Contextual Meanings of Reliability

The purpose of this article is to assess the nature of reliability and its inconsistent definitions across three contextual (conceptual, measurement and statistical) levels under the traditional true score theory. Due to such inconsistencies, two existing quantitative approaches (using r and covariance) are not uniformly understood in Psychology and other disciplines; consequently, their applications to measurements and testings are limited to ambiguous interpretations at the conceptual and measurement levels. To examine the extent of this problem, a questionnaire including various contextual definitions and interpretations of reliability in the literature was distributed in a nationwide survey. Results from six groups of experts representing editors, professors and advanced graduate students in both quantitative and clinical areas indicate that all subject groups generally agreed that a reliable instrument possesses the characteristics of the repeatability of responses of all test-takers at the conceptual level, and the reproducibility of the instrument with little or no variations from the underlying true scores at the measurement level. However, between the editors and noneditors, the endorsements of the common definition at the measurement level show obvious discrepancies. Further, at the statistical level, significant differences were found not only between but also within subject-groups in their interpretations of product-moment correlations and Alpha coefficients for the assessment of reliability at the conceptual and measurement levels. The causes of such inconsistencies were discussed in terms of the inherent limitations of the two statistical approaches used and their insufficiencies for indexing the conceptual and measurement meanings of reliability. Finally, this paper called for developing new statistical indices that are coherent with conceptual and measurement definitions. Before such development, the capacities of existing reliability indices shall be redefined and their application qualifications shall be proportionally re-established for educational, research and clinical purposes.     

The quality of measurement of personal support subnetworks

Data about personal networks and their characteristics are increasingly used in social science research, especially in research about the quality of life, social support and similar topics (Fischer, 1982; Marsden, 1987; van der Poel, 1993b). Since all data about a persons social network are usually obtained from the respondent himself, the quality of such measurements is a very important issue. Among other factors, the type of social support can affect the quality of social network measurement (Ferligoj and Hlebec, 1998, 1999). Differences in the stability of measurement between the core and extended personal network have also been found (Marsden, 1990; Morgan et al., 1997). The closer and the more important an alter is, the more likely it is that (s)he will be named in any measurement (Hoffmeyer-Zlotnik, 1990; Van Groenou et al., 1990; Morgan et al., 1997). In this paper the results of a recent study on the quality of measurement of tie characteristics in different personal subnetworks are presented. The Multitrait-multimethod (MTMM) approach was used for estimating reliability and validity. A meta analysis of reliability and validity estimates was done by hierarchical clustering. The data were collected in the year 2000 by computer assisted face-to-face and telephone interviews from a random sample of 1033 residents of Ljubljana.     

Operationalization,a superfluous concept

This paper discusses a number of issues in the methodology of social and behavioral sciences which concern how concepts are defined and related to observations, how theories are constructed, and how we proceed in actual research. Examples are mainly taken from experimental psychology, and from social psychology, but the problems and answers apply to the whole of behavioral and social sciences. These issues are related to philosophy of science. They are, however, presented from the point of view of a researcher who asks himself how to proceed in actual research, and who is critical about the present status of social and behavioral science. The main point is a pledge for a renewed empiricism. This is close to what can be called logical empiricism but should not be identified with any particular-ism. Some issues raised by critics of logical empiricism are addressed in the final section. The first problem deals with how to clarify concepts in behavioral and social science. This will show an unresolvable circularity in operationalization of theoretical constructs. The position and role of measurement is discussed in relation to the objectives of behavioral and social research. Subsequently, it is pointed out that a clear distinction between empirical concepts and theoretical constructs solves many, if not all of the problems of defining and clarifying concepts, thereby obviating the need for a so-called operationalization. It should, however, be very clear from the beginning that we cannot do without operational definitions of theoretical concepts, and that to some extent we must return to the original spirit of operationism, although in a manner which is different from current practice. An important element of the advocated approach is the definition of the empirical domain of a theory in terms of observational categories. Guttman's facet design can be a useful tool to define domains, especially for non-experimental research.     

Assessing the Effects of Cultural Intelligence on Team Knowledge Sharing From a Socio‐Cognitive Perspective

Drawing upon social cognitive theory, this research postulates cultural intelligence as a key driver of knowledge sharing among culturally diverse teams. An empirical testing of the proposed model, by investigating team leaders from high‐tech industries, reveals the applicability of social cognitive theory in understanding cultural intelligence, perceived team efficacy, and knowledge sharing. Specifically, the test results herein show that knowledge sharing is directly influenced by metacognitive, cognitive, and motivational cultural intelligence. At the same time, knowledge sharing is indirectly impacted by metacognitive and behavioral cultural intelligence through the mediation of perceived team efficacy. Lastly, this research provides managerial implications and limitations. © 2013 Wiley Periodicals, Inc.     

Nonlinear Structural Equation Models with the Theory of Planned Behavior: Comparison of Multiple Group and Latent Product Term Analyses

Nonlinear relationships in structural equation analysis became moreinteresting for applied researchers since the implementation of nonlinearconstraints in software programs (i.e., LISREL). This article provides acomprehensive application of the expectancy × value part of the Theory of Planned Behavior (Ajzen, 1991) including interactions of latent variables.The main purpose of the study is to overcome limitations of similarprevious analyses of Baumgartner and Bagozzi (1995) and Yang Jonsson (1997,1998) with an empirical example from representative survey data. Nonlinearrelationships of the theories' constructs (Attitude toward the behavior, subjectivenorm and perceived behavioral control) are analyzed one upon another withmultiple group comparisons and latent interaction models. Results confirmthe strategy to use multiple group techniques for preliminary analyses(i.e., detection of an interaction effect). With latent interaction models thestrength and the significance of the interaction is estimated under controlfor random measurement error. Parameters, standard errors, andgoodness-of-fit statistics are compared between three estimationtechniques (ML, GLS and WLS). Multiple group analyses and latentinteraction modeling detect a significant interaction for perceivedbehavioral control but not for attitude toward the behavior and subjective norm. Variations of the estimators of the perceived behavioral controlsubmodel is proved by bootstrapping. Further model improvements andalternative model techniques are discussed in the final chapter.     

Policy and Planning Programs as Goals of Scientific Work: interdisciplinary Training for Social Planning as a Case Study in Applied Social Science

Abstract . Policy and planning have now become the field of a new profession—actually a series of professions—in and out of academic life as each of the specialized social and behavioral sciences is applied to the problems of society by some of its professional practitioners. Specialism, however, tends to make of problems in real life constructs that fit within the borders of one's specialty, constructs which have little relation to reality. To transcend specialism's limitations without prejudice to specialization, interdisciplinary training is proposed and worked out for a single area, social policy and social planning.     

The Employee Engagement Scale: Initial Evidence for Construct Validity and Implications for Theory and Practice

Interest in the employee engagement construct has gained increasing attention in recent years. Measurement tools focused on nuanced areas of engagement (i.e., job engagement and organizational engagement) have been offered; however, no measure of employee engagement has been advanced despite persistent calls in the research. We present the development, method, and results of a three‐dimensional employee engagement measurement tool developed for use in the human resource and management fields of study. Across four independent studies, the employee engagement scale (EES) was found to consist of three subfactors (cognitive, emotional, and behavioral) and a higher‐order factor (employee engagement). Across a series of four studies, we explored the factor structure and reliability of the EES (Study 1), then refined the scale, confirmed the factor structure, and examined reliability and both convergent and nomological validity evidence (Study 2). Next (Study 3), we completed a final reduction in scale items and examined additional evidence of reliability and nomological validity as well as evidence of discriminant validity. Finally (Study 4), we tested for evidence of incremental validity. In the implications for theory and practice section, we discuss the importance of an employee engagement measure aligned alongside an agreed‐upon definition and framework. Limitations and future directions for research—such as the need for further psychometric testing and exploring issues of measurement invariance—are discussed. © 2016 Wiley Periodicals, Inc.     

Business-oriented environmental regulation: Measurement and implications for environmental policy and business strategy from a sustainable development perspective

Business-oriented environmental regulation is expected to have a fundamental role in mitigating the adverse effects of human activity on the natural environment. However, its effectiveness and efficiency are not well established. A systematic literature review reveals validity and reliability problems in the measurement of business-oriented environmental regulation. From a sustainable development perspective, we develop a theoretical framework that aims to enhance the measurement and assessment of this kind of regulation. Our theoretical framework proposes that the goals of business-oriented environmental regulation must articulate a measurement system in a 3 × 3 matrix: three measurement levels (stringency, response, and outcome—in this cause–effect order) and three sustainability dimensions (environmental, social, and economic—in this constraint order). For each cell, we propose a combination of objective and subjective indicators. This theoretical framework expands existing approaches to business-oriented environmental regulation measurement by integrating a sustainable development perspective into a measurement framework in a structured theory-driven manner. Not only will this measurement system be useful for improving environmental policy, but it will also allow companies to improve their business strategy and come closer to complying with environmental regulations in order to effectively contribute to solutions for current environmental problems and help achieve a sustainable development.     

Interdisciplinary social science: a methodological analysis

Since the 1930's, interdisciplinarity has been advocated in the social sciences for the purpose of achieving more comprehensive explanations of observable social phenomena. However, the realization of this promising perspective has been rather poor. This article argues that two main causes of the failure to create interdisciplinary social science can be distinguished, i.e., methodological and theoretical problems. Methodological problems stem either from taking a reductionist approach towards interdisciplinarity, or by mistaking measurement issues for theoretical topics. Theoretical problems result from the poor state and rate of theory formation within psychology. The implications of these problems are that the validity and reliability of explanations of macro social phenomena, which are provided by disciplines such as sociology and macro economics, are seriously at stake.     

Problems in the estimation and interpretation of the reliability of survey data

In this paper I discuss several of the difficulties involved in estimating the reliability of survey measurement. Reliability is defined on the basis of classical true-score theory, as the correlational consistency of multiple measures of the same construct, net of true change. This concept is presented within the framework of a theoretical discussion of the sources of error in survey data and the design requirements for separating response variation into components representing such response consistency and measurement errors. Discussion focuses on the potential sources of random and nonrandom errors, including “invalidity” of measurement, the term frequently used to refer to components of method variance. Problems with the estimation of these components are enumerated and discussed with respect to both cross-sectional and panel designs. Empirical examples are given of the estimation of the quantities of interest, which are the basis of a discussion of the interpretational difficulties encountered in reliability estimation. Data are drawn from the ISR's Quality of Life surveys, the National Election Studies and the NORC's General Social Surveys. The general conclusion is that both cross-sectional and panel estimates of measurement reliability are desirable, but for the purposes of isolating the random component of error, panel designs are probably the most advantageous.     

基于公允价值的政府资产与负债计价研究

本文介绍了我国政府会计中资产与负债的范畴及政府资产、负债计价的必要性,对现有政府资产与负债计价存在的局限性进行了探讨,指出在政府资产与负债计价中引入公允价值将成为未来发展趋势。     

Retrospective studies with survey data: Problems in social science and epidemiology research

Large-scale population surveys are a valuable source of data for epidemiologists and social scientists interested in retrospective studies of health conditions. It may not be apparent that such data sources present many problems, one of which is data reliability. It can be shown statistically that where a relation exists between two variables measured with no or minimum error, this relation vanishes if either or both variables have sizable error components. Thus such data have little utility in assessing simple relations, let alone in making causal inferences. Another problem is that unreliability or measurement error affects the sensitivity and specificity of a measuring instrument in unpredictable ways. Solutions to the problems caused by data unreliability are suggested.     

GPS快速静态法在地质工程测量中的应用研究

文章介绍了GPS快速静态测量法的概念、运作原理、作业模式及实际作业中的优缺点和改进办法。在某些地区,动态卫星定位无法实施地质测绘与测量,而GPS静态测量方法却效率太低。使用GPS快速静态测量方法,既克服了动态卫星定位在地质工程测量中的局限性,也弥补了GPS静态测量方法速度慢的不足,值得推广。     

Social Network Analysis and the Sociology of Economics: Filling a Blind Spot with the Idea of Social Embeddedness

Today, social networks analysis has become a cross‐disciplinary subject with applications in diverse fields of social and economic life. Different network designs provide different opportunities to communicate, to receive information, and to create different structures of cultural capital. Network analysis explores modes and contents of exchanges between different agents when symbols, emotions, or goods and services are exchanged. The message of the article is that social network analysis provides a tool to foster the understanding of social dynamics, which enhances recent debate on a micro‐macro gap and on limitations of the cognitive and explanatory potential of economics.     

Daily diaries in time use surveys. A solution to overcome measurement problems in single-activity events with long characteristic rhythms

Daily diaries are a widely used measurement tool in studies on the use of social time. However, while their high level of accuracy is supported by a lot of research, scholars often voice doubts regarding the validity of the estimates obtained if the characteristic rhythm of the events in question is not completed within a 24-h period. This study attempts to explain why, although these statistics are not wrong, they need to be understood correctly before proceeding with research based on them. To overcome the problem we will propose a solution for new surveys where use of diaries of this type is scheduled. Although this solution is not without limitations, they are much less restrictive than those imposed by traditional daily diaries.     

关于会计信息可靠性的衡量与评价

可靠性是指会计信息与其意欲反映的经济事实的符合程度,可靠性可分解为“真实性”和“公允性”等指标。对于如何衡量会计信息的可靠性,国外学者提出用独立计量数据的均方差来代表真实性,并以数据的真实来表示可靠性。本文认为,可靠性程度不仅与多次独立计量的离散程度有关,而且还取决于被计量项目可容忍误差的大小,根据这一思路,笔者创立了可靠性计量模型,并在此基础上提出了会计信息可靠性的评级标准。