Methodology: European Journal of Research Methods for The Behavioral and Social Sciences最新文献

{"title":"Experimental Vignette Studies in Survey Research","authors":"C. Atzmüller, Peter M Steiner","doi":"10.1027/1614-2241/A000014","DOIUrl":"https://doi.org/10.1027/1614-2241/A000014","url":null,"abstract":"Vignette studies use short descriptions of situations or persons (vignettes) that are usually shown to respondents within surveys in order to elicit their judgments about these scenarios. By systematically varying the levels of theoretically important vignette characteristics a large population of different vignettes is typically available – too large to be presented to each respondent. Therefore, each respondent gets only a subset of vignettes. These subsets may either be randomly selected in following the tradition of the factorial survey or systematically selected according to an experimental design. We show that these strategies in selecting vignette sets have strong implications for the analysis and interpretation of vignette data. Random selection strategies result in a random confounding of effects and heavily rely on the assumption of no interaction effects. In contrast, experimental strategies systematically confound interaction effects with main or set effects, thereby preserving a meaningful in...","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2010-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292753","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":"Accentuating the negative?: A political efficacy question-wording- experiment","authors":"H. Clarke, A. Kornberg, T. Scotto","doi":"10.1027/1614-2241/A000012","DOIUrl":"https://doi.org/10.1027/1614-2241/A000012","url":null,"abstract":"Survey research on political efficacy is longstanding. In a number of countries efficacy has been measured using batteries of negatively worded “agree-disagree” statements. In this paper, we investigate the measurement properties of the Canadian variant of this traditional battery and compare its performance with an alternative, positively worded, battery. The research is based on data gathered by a random half-sample experiment administered in the 2004 Political Support in Canada national panel survey. Analyses of these data provide no evidence that negatively framing the statements designed to tap political efficacy is problematic. Rather, it appears that students of political efficacy would have been worse off if they had spent the past several decades conducting analyses employing positively worded variants of the traditional statements. Perhaps most important, scholars have not been misled by acquiescence bias depressing efficacious responses to the traditional battery. These experimental results ind...","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2010-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292714","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":"New Developments in Missing Data Analysis","authors":"L. A. van der Ark, Jeroen K. Vermunt","doi":"10.1027/1614-2241/A000001","DOIUrl":"https://doi.org/10.1027/1614-2241/A000001","url":null,"abstract":"In this special issue you will find four papers on handling missing data. All papers have been presented at the 2007 Fall Meeting of Social Science Division of the Dutch Statistical Society (VVS-OR) in Tilburg, The Netherlands. Together, these four papers give an excellent overview of state of the art in missing data analysis. To date, in virtually all fields of the social sciences, researchers are required to deal sophistically with missing data. Ignoring the problem, for example, by simply removing all observations that contain missing data or thoughtlessly applying software that makes the problem go away may lead to seriously biased statistical results and wrong conclusions, and is no longer an option. Instead the researcher must consider the reasons why some of the data are missing and act accordingly. Given that in the social sciences most data are obtained from respondents who responded to tests, questionnaires, surveys, or stimuli in an experimental setting, the first option that comes to mind is approaching those respondents with missing scores again, ask them the reason for their nonresponse, and ask them to respond yet. Unfortunately, this is usually not a realistic option and the researcher must rely on statistical solutions. One way of dealing with missing data is to incorporate the mechanism that caused the missingness into the statistical modeling of the data. In the context of educational measurement, Goegebeur, De Boeck, and Molenberghs (2010) discuss test speededness, which refers to the phenomenon that respondents do not respond to certain items in the test or examination due to a lack of time. They clearly explain how speededness can be incorporated into the statistical model. Using this model-based approach, they show how to identify respondents whose scores were affected by speededness. Advantage of this approach is that it allows the researcher to deal with data that are not missing at random. In some situations, it will not be possible to translate the researcher’s theories on the missingness mechanism into a statistical model because such theories are too complex or not available. Probably the best known strategy to deal with missing data is to assume that the missing scores are missing at random and conduct (multiple) imputation: Replacing the missing scores in the data by plausible values. Two papers discuss imputation methods. First, Van Ginkel, Sijtsma, Van der Ark, and Vermunt (2010) investigated the occurrence of missing data and current practices of handling nonresponse in test and questionnaire data in personality psychology. They found that in the large majority of published research reporting missing data, either the handling of missing data was not discussed, cases with missing values were deleted, or ad hoc procedures were used. In order to improve the use of appropriate methods they proposed using Method Two-Way for handling missing data in test and questionnaire data. Method Two-Way is a multiple imputation t","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2010-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1027/1614-2241/A000001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292586","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":"Incidence of Missing Item Scores in Personality Measurement, and Simple Item-Score Imputation","authors":"J. V. van Ginkel, K. Sijtsma, L. A. van der Ark, J. Vermunt","doi":"10.1027/1614-2241/A000003","DOIUrl":"https://doi.org/10.1027/1614-2241/A000003","url":null,"abstract":"The focus of this study was the incidence of different kinds of missing-data problems in personality research and the handling of these problems. Missing-data problems were reported in approximately half of more than 800 articles published in three leading personality journals. In these articles, unit nonresponse, attrition, and planned missingness were distinguished but missing item scores in trait measurement were reported most frequently. Listwise deletion was the most frequently used method for handling all missing-data problems. Listwise deletion is known to reduce the accuracy of parameter estimates and the power of statistical tests and often to produce biased statistical analysis results. This study proposes a simple alternative method for handling missing item scores, known as two-way imputation, which leaves the sample size intact and has been shown to produce almost unbiased results based on multi-item questionnaire data.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2010-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57293131","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":"Item Imputation Without Specifying Scale Structure","authors":"S. Buuren","doi":"10.1027/1614-2241/A000004","DOIUrl":"https://doi.org/10.1027/1614-2241/A000004","url":null,"abstract":"Imputation of incomplete questionnaire items should preserve the structure among items and the correlations between scales. This paper explores the use of fully conditional specification (FCS) to impute missing data in questionnaire items. FCS is particularly attractive for items because it does not require (1) a specification of the number of factors or classes, (2) a specification of which item belongs to which scale, and (3) assumptions about conditional independence among items. Imputation models can be specified using standard features of the R package MICE 1.16. A limited simulation shows that MICE outperforms two-way imputation with respect to Cronbach’s α and the correlations between scales. We conclude that FCS is a promising alternative for imputing incomplete questionnaire items.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2010-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292689","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":"Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators","authors":"S. Vansteelandt, J. Carpenter, M. Kenward","doi":"10.1027/1614-2241/A000005","DOIUrl":"https://doi.org/10.1027/1614-2241/A000005","url":null,"abstract":"This article reviews inverse probability weighting methods and doubly robust estimation methods for the analysis of incomplete data sets. We first consider methods for estimating a population mean when the outcome is missing at random, in the sense that measured covariates can explain whether or not the outcome is observed. We then sketch the rationale of these methods and elaborate on their usefulness in the presence of influential inverse weights. We finally outline how to apply these methods in a variety of settings, such as for fitting regression models with incomplete outcomes or covariates, emphasizing the use of standard software programs.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2010-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292700","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":"Obtaining Equations From the Proportional Odds Model to Set Multiple Cut Scores on a Test","authors":"R. Bersabé, Teresa Rivas, C. Berrocal","doi":"10.1027/1614-2241.5.4.123","DOIUrl":"https://doi.org/10.1027/1614-2241.5.4.123","url":null,"abstract":"From the proportional odds (PO) model, we obtain general equations to compute multiple cut scores on a test score. This analytical procedure is based on the relationship between a test score (X) and an ordinal outcome variable (Y) with more than two categories. Cut scores are established at the test scores corresponding to the intersection of adjacent category distributions. The application of this procedure is illustrated by an example with data from an actual study on eating disorders (EDs). In this example, two cut scores on the Eating Attitudes Test (EAT-26) are established in order to differentiate between three ordered categories: (1) asymptomatic, (2) symptomatic, and (3) eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalizes DSM-IV criteria for EDs. Alternatives to the PO model, when the PO assumption is rejected, are discussed.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2009-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1027/1614-2241.5.4.123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292576","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":"The Multitrait-Multimethod Matrix at 50!","authors":"M. Eid, Fridtjof W. Nussbeck","doi":"10.1027/1614-2241.5.3.71","DOIUrl":"https://doi.org/10.1027/1614-2241.5.3.71","url":null,"abstract":"Fifty years ago, in 1959, Campbell and Fiske published one of the most influential papers in psychology. In their article Convergent and discriminant validation by the multitraitmultimethod matrix, they argued that it is not sufficient to consider one single operationalization of one construct for purposes of test validation but that multiple measures of multiple constructs are necessary. Campbell and Fiske recommended using at least two methods that are as different as possible for measuring the constructs. Moreover, Campbell and Fiske made clear that it is not possible to get a measure of a trait that is free of method-specific influences. Whenever, in science, we measure a construct (a trait) we have to use a specific measurement method. Therefore, it is the trait and the method that influence the observed score simultaneously. In order to separate methodfrom traitspecific influences, it is thus always necessary to consider more than one trait and more than one method in the validation process. Campbell and Fiske proposed the multitraitmultimethod (MTMM) matrix for analyzing the convergent and discriminant validity. The MTMM matrix consists of the correlations between all multiple measures representing the different traits measured by the different methods. These correlations can be evaluated by several criteria that have been developed by Campbell and Fiske. If the different measures of the same construct are highly correlated, this proves convergent validity. If the different measures of one construct are not correlated with the measures of another construct, this indicates discriminant validity. Campbell and Fiske’s article had and has an enormous influence on psychology (Eid & Diener, 2006). It is the most often cited paper that has ever been published in Psychological Bulletin (Sternberg, 1992). To date, it has been cited 4,735 times (Social Science Citation Index, February 27, 2009, 3:41 pm), and its citation rate is increasing. Their article does not only have an important impact on test validation studies but also has a strong impact on methodological research as many researchers have developed new approaches for analyzing MTMM data and tried to overcome some of the problems and limitations that are related to former approaches of analyzing MTMM matrices. This special issue is dedicated to honoring Campbell and Fiske’s influential work. It presents three different modern approaches for analyzing MTMM data. All contributors use the same data set illustrating their approaches. This enables readers to concentrate on the comparison of the different approaches with respect to the way convergent and discriminant validity can be analyzed as well as how traitand method-specific influences can be identified and quantified. The data consists of three personality traits (extraversion, neuroticism, and conscientiousness) assessed by three raters (one selfand two peer raters). Each scale consists of four items (adjectives such as talkative, conscie","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2009-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292510","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":"A CTC(M−1) Model for Different Types of Raters","authors":"Fridtjof W. Nussbeck, M. Eid, C. Geiser, D. Courvoisier, T. Lischetzke","doi":"10.1027/1614-2241.5.3.88","DOIUrl":"https://doi.org/10.1027/1614-2241.5.3.88","url":null,"abstract":"Many psychologists collect multitrait-multimethod (MTMM) data to assess the convergent and discriminant validity of psychological measures. In order to choose the most appropriate model, the types of methods applied have to be considered. It is shown how the combination of interchangeable and structurally different raters can be analyzed with an extension of the correlated trait-correlated method minus one [CTC(M−1)] model. This extension allows for disentangling individual rater biases (unique method effects) from shared rater biases (common method effects). The basic ideas of this model are presented and illustrated by an empirical example.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2009-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292564","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":"Three-Mode Models for Multitrait-Multimethod Data","authors":"F. Oort","doi":"10.1027/1614-2241.5.3.78","DOIUrl":"https://doi.org/10.1027/1614-2241.5.3.78","url":null,"abstract":"Multitrait-multimethod (MTMM) data are characterized by three modes: traits, methods, and subjects. Considering subjects as random, and traits and methods as fixed, stochastic three-mode models can be used to analyze MTMM covariance data. Stochastic three-mode models can be written as linear latent variable models with direct product (DP) restrictions on the parameter matrices (Oort, 1999), yielding three-mode factor models (Bentler & Lee, 1979) and composite direct product models (Browne, 1984) as special cases. DP restrictions on factor loadings and factor correlations facilitate interpretation of the results and enable easy evaluation of the validity requirements of MTMM correlations (Campbell & Fiske, 1959). As an illustrative example, a series of stochastic three-mode models has been fitted to data of three personality traits of 482 students, measured with 12 items, through three methods.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2009-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57292524","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}