{"title":"Mixture of Covariance Structure Models to Identify Different Types of Life Style","authors":"Petra Stein","doi":"10.1027/1614-2241.2.3.86","DOIUrl":"https://doi.org/10.1027/1614-2241.2.3.86","url":null,"abstract":"A central topic of empirical social research is the problem of unobserved heterogeneity. To solve this problem at least partially, a statistical model is presented: the finite mixture of conditional mean and covariance structure models. In this approach, the expected values in each component of a mixture may depend on normally or nonnormally distributed regressor variables. The expected value and the covariance matrix in each component of the mixture are parameterized using conditional mean and covariance structure models. Three different procedures for estimating the parameters of these models are briefly discussed. The model and the estimation procedures are applied to data of the German General Social Survey 1998 to identify heterogenous types of life style. Since different regression models with latent variables may be used for each type, it is not only possible to cover different types of life style, but also different types of relationships between life style dimensions and the influences of sociode...","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Special Issue: “Social Network Analysis”","authors":"J. Vermunt, M. Duijn","doi":"10.1027/1614-2241.2.1.1","DOIUrl":"https://doi.org/10.1027/1614-2241.2.1.1","url":null,"abstract":"","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1027/1614-2241.2.1.1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Collection of ego-centered network data with computer-assisted interviews.","authors":"J. Gerich, Roland Lehner","doi":"10.1027/1614-2241.2.1.7","DOIUrl":"https://doi.org/10.1027/1614-2241.2.1.7","url":null,"abstract":"Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tucker's congruence coefficient as a meaningful index of factor similarity.","authors":"U. Lorenzo-Seva, J. F. Berge","doi":"10.1027/1614-2241.2.2.57","DOIUrl":"https://doi.org/10.1027/1614-2241.2.2.57","url":null,"abstract":"When Tucker's congruence coefficient is used to assess the similarity of factor interpretations, it is desirable to have a critical congruence level less than unity that can be regarded as indicative of identity of the factors. The literature only reports rules of thumb. The present article repeats and broadens the approach used in the study by Haven and ten Berge (1977). It aims to find a critical congruence level on the basis of judgments of factor similarity by practitioners of factor analysis. Our results suggest that a value in the range .85-.94 corresponds to a fair similarity, while a value higher than .95 implies that the two factors or components compared can be considered equal.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1027/1614-2241.2.2.57","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"What is special about social network analysis","authors":"V. Duijn, J. Vermunt","doi":"10.1027/1614-2241.2.1.2","DOIUrl":"https://doi.org/10.1027/1614-2241.2.1.2","url":null,"abstract":"In a short introduction on social network analysis, the main characteristics of social network data as well as the main goals of social network analysis are described. An overview of statistical models for social network data is given, pointing at differences and similarities between the various model classes and introducing the most recent developments in social network modeling.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1027/1614-2241.2.1.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Barbara Schober, Petra Wagner, Ralph Reimann, Moira Atria, C. Spiel
{"title":"Teaching Research Methods in an Internet-Based Blended-Learning Setting: Vienna E-Lecturing (VEL)","authors":"Barbara Schober, Petra Wagner, Ralph Reimann, Moira Atria, C. Spiel","doi":"10.1027/1614-2241.2.2.73","DOIUrl":"https://doi.org/10.1027/1614-2241.2.2.73","url":null,"abstract":"This article gives a survey of a blended learning approach called Vienna E-Lecturing (VEL), implemented in the course Research Methods and Evaluation, which is required by the psychology program at the University of Vienna, Austria. VEL replaces a main lecture and has been designed to teach methodological issues more effectively as well as to strengthen students' learning competences in this field. The program's conceptualization is based on instructional and motivational findings yielding the program's two main teaching principles: (1) networking and (2) optimal instructions. The Internet-based course lasts two semesters and is composed of 10 online learning modules and 11 face-to-face meetings (including tutorials). The modules, which are available successively via a learning platform, systematically instruct students to learn more effectively by cooperating and fulfilling different tasks within small groups. The current article describes the program's principles and theoretical background and outlines ...","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Explanation Through Network Visualization","authors":"U. Brandes, P. Kenis, Jörg Raab","doi":"10.1027/1614-2241.2.1.16","DOIUrl":"https://doi.org/10.1027/1614-2241.2.1.16","url":null,"abstract":"Assessments of configurations, dynamics, and cause and effect are at the heart of our thinking and explanation. Although numerous methods for such assessments have been developed and are being used in daily scientific practice, visualization is usually not considered to be one of them. In this article we first argue that this is due to the common practice of visualizing data rather than the information contained in it; second, we address a number of principles for effective visualization; and third, we assess visualizations generated by the software tool visone in order to explain network outcomes using these principles.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1027/1614-2241.2.1.16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applying SIENA: An illustrative analysis of the co-evolution of adolescents’ friendship networks, taste in music, and alcohol consumption","authors":"C. Steglich, T. Snijders, P. West","doi":"10.1027/1614-2241.2.1.48","DOIUrl":"https://doi.org/10.1027/1614-2241.2.1.48","url":null,"abstract":"We give a non-technical introduction into recently developed methods for analyzing the co-evolution of social networks and behavior(s) of the network actors. This co-evolution is crucial for a variety of research topics that currently receive a lot of attention, such as the role of peer groups in adolescent development. A family of dynamic actor-driven models for the co-evolution process is sketched, and it is shown how the SIENA software can be used for estimating these models. We illustrate the method by analyzing the co-evolution of friendship networks, taste in music, and alcohol consumption of teenagers.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Joint Correspondence Analysis (JCA) by Maximum Likelihood","authors":"J. Vermunt, Carolyn J. Anderson","doi":"10.1027/1614-1881.1.1.18","DOIUrl":"https://doi.org/10.1027/1614-1881.1.1.18","url":null,"abstract":"Abstract. Parameter estimation in joint correspondence analysis (JCA) is typically performed by weighted least squares using the Burt matrix as the data matrix. In this paper, we show how to estimate the JCA model by means of maximum likelihood. For that purpose, JCA is defined as a model for the full K-way distribution by generalizing the correspondence analysis model for three-way tables proposed by Choulakian (1988a, 1988b). The advantage of placing JCA in a more formal statistical framework is that standard chi-squared tests can be applied to assess the goodness-of-fit of unrestricted and restricted models.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How to Describe the Difference between Factors and Corresponding Factor-Score Estimates","authors":"A. Beauducel","doi":"10.1027/1614-2241.1.4.143","DOIUrl":"https://doi.org/10.1027/1614-2241.1.4.143","url":null,"abstract":"Abstract. Because of factor score indeterminacy, there can be substantial shifts in the theoretical meaning of factors and their corresponding score estimates. Therefore, the original factor pattern should be compared with the regression-component loadings (Schonemann & Steiger, 1976) corresponding to the factor-score estimates in order to detect possible shifts in the theoretical meaning. Especially with large loading matrices the similarity of the original factor pattern and the regression components of the score estimates may be ascertained by means of congruency coefficients. It is shown that these congruencies contain information that is not already given by measures of factor-score indeterminacy. Two examples illustrate the use of regression-component analysis for different types of factor-score estimates. The analyses reveal that the Bartlett-score estimates are most appropriate when factor interpretation is based on the factor pattern, which is usually the case in confirmatory factor analysis.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}