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(1984).Analysis of ordinal categorical data. New York: John Wiley & Sons.\nAllison, P. D., & Liker, J. D. (1982). Analyzing sequential categorical data on dyadic interactions.Psychological Bulletin, 91, 393–403.\nArabie, P. (1984). Validation of sociometric structure by data on individuals' attributes.Social Networks, 6, 373–403.\nArabie, P., & Carroll, J. D. (1980). MAPCLUS: A mathematical programming approach to fitting the ADCLUS model.Psychometrika, 45, 211–236.\nBaker, R. J., & Nelder, J. A. (1978).The GLIM system, Release 3: Generalized linear interactive modeling. Oxford: The Numerical Algorithms Group.\nBerkowitz, S. D. (1982).An introduction to structural analysis: The network approach to social research. Toronto: Butterworths.\nBernard, H. R., & Killworth, P. D. (1979). Deterministic models of social networks. In P. W. Holland & S. Leinhardt (Eds.),Perspectives on social network research (pp. 165–186). New York: Academic Press.\nBishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (1975).Discrete multivariate analysis: Theory and practice. Cambridge, MA: The MIT Press.\nBudescu, D. V. (1984). Tests of lagged dominance in sequential dyadic interaction.Psychological Bulletin, 96, 402–414.\nBurt, R. S. (1980). Models of network structure.Annual Review of Sociology, 6, 79–141.\nFienberg, S. E. (1980).The Analysis of cross-classified, categorical data (2nd ed.). Cambridge, MA: The MIT Press.\nFienberg, S. E. (1985). Multivariate directed graphs in statistics. In S. Kotz & N. L. Johnson (Ed.),Encyclopedia of statistical sciences (Vol. 6, pp. 40–43). New York: John Wiley & Sons.\nFienberg, S. E., Meyer, M. M., & Wasserman, S. (1985). Statistical analysis of multiple sociometric relations.Journal of the American Statistical Association, 80, 51–67.\nFienberg, S. E., & Wasserman, S. (1981). Categorical data analysis of single sociometric relations. In S. Leinhardt (Ed.),Sociological methodology 1981 (pp. 156–192). San Francisco: Jossey-Bass.\nGottman, J. M. (1979).Marital interactions: Experimental investigations. New York: Academic Press.\nGottman, J. M., & Ringland, J. T. (1981). The analysis of dominance and bidirectionality in social development.Child Development, 52, 393–412.\nHaberman, S. J. (1978).Analysis of qualitative data, (Vol. 1). New York: Academic Press.\nHaberman, S. J. (1979).Analysis of qualitative data (Vol. 2). New York: Academic Press.\nHage, P., & Harary, F. (1983).Structural models in anthropology. Cambridge, England: Cambridge University Press.\nHolland, P. W., & Leinhardt, S. (1977). A dynamic model for social networks.Journal of Mathematical Sociology, 5, 5–20.\nHolland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs.Journal of the American Statistical Association, 76, 33–50.\nHubert, L. J. (1978). Generalized proximity function comparisons.British Journal of Mathematical and Statistical Psychology, 31, 179–192.\nHubert, L. J. (1979). Generalized concordance.Psychometrika, 44, 135–142.\nHubert, L. J., & Baker, F. B. (1978). Evaluating the conformity of sociometric measurements.Psychometrika, 43, 31–41.\nHubert, L. J., & Schultz, J. V. (1976). Quadratic assignment as a general data analysis strategy.British Journal of Mathematical and Statistical Psychology, 29, 190–241.\nIacobucci, D., & Wasserman, S. (1987). Dyadic social interactions.Psychological Bulletin, 102, 293–306.\nIacobucci, D., & Wasserman, S. (1988). A general framework for the statistical analysis of sequential dyadic interaction data.Psychological Bulletin, 103, 279–390.\nKatz, L., & Powell, J. H. (1953). A proposed index for the conformity of one sociometric measurement to another.Psychometrika, 18, 249–256.\nKatz, L., & Proctor, C. H. (1959). The concept of configuration of interpersonal relations in a group as a time-dependent stochastic process.Psychometrika, 24, 317–327.\nKnoke, D., & Kuklinski, J. H. (1982).Network Analysis. Beverly Hills, CA: Sage Publications.\nKoehler, K., & Larntz, K. (1980). An empirical investigation of goodness-of-fit statistics for sparse multinomials.Journal of the American Statistical Association, 75, 336–344.\nMeyer, M. M. (1982). Transforming contingency tables.Annals of Statistics, 10, 1172–1181.\nNoma, E., & Smith, D. R. (1985). Benchmark for the blocking of sociometric data.Psychological Bulletin, 97, 583–591.\nPayne, C. D. (1985).The GLIM system release 3.77: Generalized linear interactive modelling manual. Oxford: The Numerical Algorithms Group.\nRice, R. E., & Richards, W. D. Jr. (1985). An overview of network analysis methods and programs. In B. Cervin & M. J. Voigt (Eds.)Progress in communication sciences (Vol. VI, pp. 105–165). Norwood, NJ: Ablex.\nSampson, S. F. (1968).A Novitiate in a period of change: An experimental and case study of social relationships. Unpublished doctoral dissertation, Department of Sociology, Cornell University.\nShepard, R. N., & Arabie, P. (1979). Additive clustering: Representation of similarities as combinations of discrete overlapping properties.Psychological Review, 86, 87–123.\nWampold, B. E. (1984). Tests of dominance in sequential categorical data.Psychological Bulletin, 96, 424–429.\nWampold, B. E., & Margolin, G. (1982). Nonparametric strategies to test the independence of behavioral states in sequential data.Psychological Bulletin, 92, 755–765.\nWasserman, S. (1978). Models for binary directed graphs and their applications.Advances in Applied Probability, 10, 803–818.\nWasserman, S. (1980). Analyzing social networks as stochastic processes.Journal of the American Statistical Association, 75, 280–294.\nWasserman, S. (1987). Conformity of two sociometric relations.Psychometrika, 52, 3–18.\nWasserman, S., & Anderson, C. (1987). Stochastica posteriori blockmodels: Construction and assessment.Social Networks, 9, 1–36.\nWasserman, S., & Iacobucci, D. (1986). Statistical analysis of discrete relational data.British Journal of Mathematical and Statistical Psychology, 39, 41–64.",{"EN":142},"A new method is proposed for the statistical analysis of dyadic social interaction data measured over time. The data to be studied are assumed to be realizations of a social network of a fixed set of actors interacting on a single relation. The method is based on loglinear models for the probabilities for various dyad (or actor pair) states and generalizes the statistical methods proposed by Holland and Leinhardt (1981), Fienberg, Meyer, & Wasserman (1985), and Wasserman (1987) for social network data. Two statistical models are described: the first is an “associative” approach that allows for the study of how the network has changed over time; the second is a “predictive” approach that permits the researcher to model one time point as a function of previous time points. These approaches are briefly contrasted with earlier methods for the sequential analysis of social networks and are illustrated with an example of longitudinal sociometric data.",{"EN":144},"Sequential social network data",{"VOID":146},"10.1007\u002FBF02294137","PUBLICATION","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02294137",[150,166],{"id":151,"sortIndex":124,"researcher":19,"roles":152,"affiliations":154,"properties":163},"cec82f04-65fc-491f-ac89-2afca250150d",[153],"AUTHOR",[155],{"id":19,"sortIndex":20,"affiliation":156,"properties":19},{"id":157,"createTime":158,"updateTime":158,"relativeEntities":159,"slug":19,"properties":160,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"873b5f02-09d2-43e9-9932-b2c2b3b7b430","2024-02-05T23:59:46.426+00:00",[],{"title":161},{"VI":162},"Department of Marketing J. L. Kellogg Graduate School of Management, Northwestern University, USA",{"title":164},{"VI":165},"Dawn Iacobucci",{"id":167,"sortIndex":20,"researcher":19,"roles":168,"affiliations":169,"properties":178},"835372d5-46b9-41d7-bf45-67f7e2c3010c",[153],[170],{"id":19,"sortIndex":20,"affiliation":171,"properties":19},{"id":172,"createTime":173,"updateTime":173,"relativeEntities":174,"slug":19,"properties":175,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"dbca319c-b931-4333-9ec0-7eee22dd0f62","2024-02-05T23:59:46.413+00:00",[],{"title":176},{"VI":177},"Department of Psychology and Department of Statistics, University of Illinois, Champaign",{"title":179},{"VI":180},"Stanley Wasserman","ARTICLE",{"url":148,"publisher":183,"properties":218},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":184,"slug":10,"properties":185,"entityType":17,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"subjectFields":189,"manageAffiliations":190,"indexDatabases":191,"url":119,"thumbnailPath":19,"statistic":213,"gsStatistic":19,"type":129,"analyzePriority":19},[],{"issn":186,"eissn":187,"title":188},{"VOID":13},{"VOID":15},{"EN":10},[],[],[192,199,206],{"id":102,"indexDatabase":193,"url":98,"indexYears":19,"academicFieldIds":198,"indexDatabaseRanking":19},{"id":104,"createTime":105,"updateTime":106,"relativeEntities":194,"label":195,"description":196,"key":113,"publicationTags":197,"standard":19},[],{"EN":109,"VI":109},{"VI":111,"EN":112},[115,97],[117,118],{"id":63,"indexDatabase":200,"url":76,"indexYears":77,"academicFieldIds":205,"indexDatabaseRanking":81},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":201,"label":202,"description":203,"key":73,"publicationTags":204,"standard":19},[],{"EN":70,"VI":70},{"EN":70,"VI":72},[75],[79,80],{"id":83,"indexDatabase":207,"url":98,"indexYears":19,"academicFieldIds":212,"indexDatabaseRanking":19},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":208,"label":209,"description":210,"key":94,"publicationTags":211,"standard":19},[],{"EN":90,"VI":90},{"VI":92,"EN":93},[96,97],[100],{"impactFactor":20,"impactFactorByYear":214,"i10Index":20,"i10IndexLast5Year":20,"totalPublication":122,"totalPublicationByYear":215,"totalCitation":20,"totalCitationByYear":216,"totalCitationPerPublication":20,"totalCitationPerPublicationByYear":217,"hindexLast5Year":20,"hindex":20},{},{"1940":124,"1943":124,"1953":124,"1957":124,"1961":125,"1963":124,"1964":124,"1967":124,"1978":124,"1980":124,"1981":124,"1982":125,"1985":124,"1986":124,"1990":124,"1992":124,"1993":125,"1995":124,"2001":124,"2003":124,"2010":126,"2015":124,"2016":125,"2017":124,"2023":124},{},{},{"volume":219,"pages":221},{"VOID":220},"53",{"VOID":222},"261-282","1988-06-01",1988,false,{"id":227,"createTime":228,"updateTime":229,"relativeEntities":230,"slug":231,"properties":232,"entityType":147,"verifyStatus":241,"verifyTime":229,"verifyNote":242,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":124,"primaryUrl":243,"fullTextUrl":19,"authors":244,"publicationType":181,"publisherRelationship":272,"citationCount":19,"citationInfo":19,"publishDate":313,"publishYear":314,"citationAnalyzeStatus":18,"lastCitationAnalyze":19,"indexDatabases":19,"openAccess":19,"references":19,"isForceReanalyzing":225},"f0f18235-3849-4e7d-9e97-88b6cc973476","2023-12-31T14:09:50.600+00:00","2025-01-19T23:58:10.472+00:00",[],"On-methods-in-the-analysis-of-profile-data",{"references":233,"abstract":235,"title":237,"doi":239},{"VOID":234},"Anderson, T. W.Introduction to multivariate statistical analysis. New York: Wiley, 1958.\nBlock, J., Levine, L., and McNemar, Q. Testing for the existence of psychometric patterns.J. abnorm. soc. Psychol., 1951,46, 356–359.\nBox, G. E. P. A general distribution theory for a class of likelihood criteria.Biometrika, 1949,36, 317–346.\nBox, G. E. P. Problems in the analysis of growth and wear curves.Biometrics, 1950,6, 362–389.\nBox, G. E. P. Some theorems on quadratic forms applied in the study of analysis of variance problems: I. Effect of inequality of variance in the one-way classification.Ann. math. Statist., 1954,25, 290–302.\nBox, G. E. P. Some theorems on quadratic forms applied in the study of analysis of variance problems: II. Effects of inequality of variance and of correlation between errors in the two-way classification.Ann. math. Statist., 1954,25, 484–498.\nEisenhart, C. The assumptions underlying the analysis of variance.Biometrics, 1947,3, 1–21.\nGeisser, S. and Greenhouse, S. W. An extension of Box's results on the use of theF distribution in multivariate analysis.Ann. math. Statist., 1958,29, 885–891.\nHeck, D. L. Some uses of the distribution of the largest root in multivariate analysis. Inst. Statist. Univ. North Carolina, Mimeo. Ser. No. 194, 1958.\nHotelling, H. A generalizedT test and measure of multivariate dispersion.Proceedings of the second Berkeley symposium on mathematical statistics and probability. Berkeley: Univ. Calif. Press, 1951, 23–42.\nKullback, S. An application of information theory to multivariate analysis, II.Ann. math. Statist., 1956,27, 122–146.\nRao, C. R.Advanced statistical methods in biometric research. New York: Wiley, 1952.\nRoy, S. N. On a heuristic method of test construction and its use in multivariate analysis.Ann. math. Statist., 1953,24, 220–238.\nScheffé, H. A “mixed model” for the analysis of variance.Ann. math. Statist., 1956,27, 23–36.\nWelch, B. L. Note on Mrs. Aspin's Tables and on certain approximations to the tabled functions.Biometrika, 1949,36, 293–296.\nWilk, M. B. and Kempthorne, O. Fixed, mixed, and random models.J. Amer. statist. Ass., 1955,50, 1144–1167.\nWilks, S. S. Certain generalizations in the analysis of variance.Biometrika, 1932,24, 471–494.",{"EN":236},"This paper is concerned with methods for analyzing quantitative, non-categorical profile data, e.g., a battery of tests given to individuals in one or more groups. It is assumed that the variables have a multinormal distribution with an arbitrary variance-covariance matrix. Approximate procedures based on classical analysis of variance are presented, including an adjustment to the degrees of freedom resulting in conservativeF tests. These can be applied to the case where the variance-covariance matrices differ from group to group. In addition, exact generalized multivariate analysis methods are discussed. Examples are given illustrating both techniques.",{"EN":238},"On methods in the analysis of profile data",{"VOID":240},"10.1007\u002FBF02289823","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02289823",[245,260],{"id":246,"sortIndex":124,"researcher":19,"roles":247,"affiliations":248,"properties":257},"060113ee-d044-4859-9957-782aceb4bfd1",[153],[249],{"id":19,"sortIndex":20,"affiliation":250,"properties":19},{"id":251,"createTime":252,"updateTime":252,"relativeEntities":253,"slug":19,"properties":254,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"633a7216-9b7c-4496-979a-71cda7b0d7f4","2024-01-13T21:02:56.336+00:00",[],{"title":255},{"VI":256},"National Institute of Mental Health, USA",{"title":258},{"VI":259},"Seymour Geisser",{"id":261,"sortIndex":20,"researcher":19,"roles":262,"affiliations":263,"properties":269},"60f4a78e-27b8-4697-b529-e7e36722c92f",[153],[264],{"id":19,"sortIndex":20,"affiliation":265,"properties":19},{"id":251,"createTime":252,"updateTime":252,"relativeEntities":266,"slug":19,"properties":267,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},[],{"title":268},{"VI":256},{"title":270},{"VI":271},"Samuel W. 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J.Alternative inference models based on matching for a weighted index of nominal scale response agreement. Unpublished manuscript, 1978.\nAbe, O. A central limit theorem for the number of edges in the random intersection of two graphs.Annals of Mathematical Statistics, 1969,40, 144–151.\nBishop, Y. M. M., Feinberg, S. E., & Holland, P. W.Discrete multivariate analysis: Theory and practice. Cambridge, Mass.: The M.I.T. Press, 1975.\nBrennan, R. L. & Light, R. J. Measuring agreement when two observers classify people into categories not defined in advance.The British Journal of Mathematical and Statistical Psychology, 1974,27, 154–163.\nBrown, M. B. The asymptotic standard errors of some estimates of uncertainty in the two-way contingency table.Psychometrika, 1975,40, 291–296.\nCamilli, G. & Hopkins, K. D. Applicability of chi-square to 2 × 2 contingency tables with small expected cell frequencies.Psychological Bulletin, 1978,85, 163–167.\nCohen, J. A coefficient of agreement for nominal scales.Educational and Psychological Measurement, 1960,20, 37–46.\nCohen, J. Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit.Psychological Bulletin, 1968,70, 213–220.\nCohen, J. Weighted chi square: An extension of the kappa method.Educational and Psychological Measurement, 1972,32, 61–74.\nDaniels, H. E. The relation between measures of correlation in the universe of sample permutations.Biometrika, 1944,33, 129–135.\nDavid, F. N. & Barton, D. E.Combinatorial chance. New York: Hafner, 1962.\nEdgington, E. S.Statistical inference: The distribution-free approach. New York: McGraw-Hill, 1969.\nFleiss, J. L., Cohen, J., & Everitt, B. S. Large sample standard errors of kappa and weighted kappa.Psychological Bulletin, 1969,72, 323–327.\nGoodman, L. A. & Kruskal, W. H. Measures of association for cross classifications, IV: Simplification of asymptotic variances.Journal of the American Statistical Association, 1972,67, 415–421.\nGraves, G. W. & Whinston, A. B. An algorithm for the quadratic assignment problem.Management Science, 1970,17, 453–471.\nHartigan, J. A.Clustering algorithms. New York: Wiley, 1975.\nHawkes, R. K. The multivariate analysis of ordinal measures.American Journal of Sociology, 1971,76, 908–926.\nHays, W. L.Statistics for social scientists. New York: Holt, Rinehart and Winston, 1973.\nHildebrand, D. K., Laing, J. D., & Rosenthal, H.Prediction analysis of cross-classifications. New York: Wiley, 1977.\nHoeffding, W. A combinatorial central limit theorem.Annals of Mathematical Statistics, 1951,22, 558–566.\nHope, A. C. A. A simplified Monte Carlo significance test procedure.Journal of the Royal Statistical Society, SeriesB, 1968,30, 582–598.\nHotelling, H. The selection of variates for use in prediction with some comments on the general problem of nuisance parameters.The Annals of Mathematical Statistics, 1940,11, 271–283.\nHubert, L. J. A relationship between the assignment problem and some simple statistical techniques.Quality and Quantity, 1976,10, 341–348.\nHubert, L. J. Kappa revisited.Psychological Bulletin, 1977,84, 289–297. (a)\nHubert, L. J. Nominal scale response agreement as a generalized correlation.The British Journal of Mathematical and Statistical Psychology, 1977,30, 98–103. (b)\nHubert, L. J. A general formula for the variance of Cohen's weighted kappa.Psychological Bulletin, 1978,85, 183–184.\nHubert, L. J. & Baker, F. B. Analyzing distinctive features.Journal of Educational Statistics, 1977,2, 79–98.\nHubert, L. J. & Baker, F. B. Applications of combinatorial programming to data analysis: The traveling salesman and related problems.Psychometrika, 1978,43, 81–91. (a)\nHubert, L. J. & Baker, F. B. Evaluating the conformity of sociometric measurements.Psychometrika, 1978,43, 31–41. (b)\nHubert, L. J. & Levin, J. R. Inference models for categorical clustering.Psychological Bulletin, 1977,84, 878–887.\nHubert, L. J. & Schultz, J. V. Quadratic assignment as a general data analysis strategy.The British Journal of Mathematical and Statistical Psychology, 1976,29, 190–241.\nKendall, M. G.Rank correlation methods (4th edition). New York: Hafner, 1970.\nKlauber, M. R. Two-sample randomization tests for space-time clustering.Biometrics, 1971,27, 129–142.\nKrippendorff, K. Bivariate agreement coefficients for reliability of data. In E. F. Borgatta (Ed.),Sociological methodology 1970. San Francisco: Jossey-Bass, 1970.\nLancaster, H. O.The chi-squared distribution. New York: Wiley, 1969.\nLehmann, E. L.Nonparametrics. San Francisco: Holden-Day, 1975.\nLight, R. J. & Margolin, B. H. An analysis of variance for categorical data.The Journal of the American Statistical Association, 1971,66, 534–544.\nMargolin, B. H. & Light, R. J. An analysis of variance for categorical data, II: Small sample comparisons with chi-square and other computations.The Journal of the American Statistical Association, 1974,69, 755–764.\nMotoo, M. On the Hoeffding's combinatorial central unit theorem.Annals of the Institute of Statistical Mathematics, 1957,8, 145–154.\nPuri, M. L. & Sen, P. K.Nonparametric methods in multivariate analysis. New York: Wiley, 1971.\nRand, W. M. Objective criteria in the evaluation of clustering methods.The Journal of the American Statistical Association, 1971,66, 846–850.\nRao, C. R.Linear statistical inference and its applications. New York: Wiley, 1973.\nScott, W. A. Reliability of content analysis: The case of nominal scale coding.Public Opinion Quarterly, 1955,19, 321–325.",{"EN":325},"Inference models motivated by the combinatorial chance literature and the concept of object matching may be used in the analysis of a contingency table if the conditional assumption of fixed row and column totals is imposed. More specifically, by developing a matching reinterpretation for several problems of interest in the prediction analysis of cross-classifications—as defined by Hildebrand, Laing and Rosenthal, appropriate significance tests can be given that may differ from those justified by the more common multinomial models. In the course of the paper the distinction between a degree-1 statistic (based on the relationship between single objects) and a degree-2 statistic (based on the relationship between object pairs) is reviewed in some detail. Also, several specializations are presented to topics of current methodological importance in psychology; for instance, a number of references are made to the measurement of nominal scale response agreement between two raters.",{"EN":327},"Matching models in the analysis of cross-classifications",{"VOID":329},"10.1007\u002FBF02293782","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02293782",[332],{"id":333,"sortIndex":20,"researcher":19,"roles":334,"affiliations":335,"properties":344},"ba5b4668-1931-4ab9-a2a3-42191a383fb5",[153],[336],{"id":19,"sortIndex":20,"affiliation":337,"properties":19},{"id":338,"createTime":339,"updateTime":339,"relativeEntities":340,"slug":19,"properties":341,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"3d0349d4-1be9-43f9-b9cd-ff8937020f5e","2024-01-05T21:20:01.976+00:00",[],{"title":342},{"VI":343},"Department of Education, The University of California, Santa Barbara",{"title":345},{"VI":346},"Lawrence J. 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E. An approach to the problem of differential prediction.Psychometrika, 1946,11, 139–154.\nDwyer, P. S. Solution of the personnel classification problem with the method of optimal regions.Psychometrika, 1954,19, 11–26.\nLord, F. M. Notes on a problem of multiple classification.Psychometrika, 1952,17, 297–304.\nRao, C. R. Advanced statistical methods in biometric research. New York: Wiley, 1952.\nVotaw, D. F. Methods of solving some personnel classification problems.Psychometrika, 1952,17, 255–266.",{"EN":398},"A simple algebraic proof of a theorem defining the optimal solution to the personnel classification problem is given. If a set of constants, one for each job, are known, the theorem indicates that each individual should be classified by adding the constants to the estimates of the individual's productivity in the several jobs and selecting the job for which the resulting sum is highest.",{"EN":400},"A simple proof of a personnel classification theorem",{"VOID":402},"10.1007\u002FBF02289185","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02289185",[405],{"id":406,"sortIndex":20,"researcher":19,"roles":407,"affiliations":408,"properties":417},"71a072c0-48c2-47ba-819f-664591d5172c",[153],[409],{"id":19,"sortIndex":20,"affiliation":410,"properties":19},{"id":411,"createTime":412,"updateTime":412,"relativeEntities":413,"slug":19,"properties":414,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"37537b98-cbd1-45ad-af61-2bf6beebd271","2024-02-08T23:57:31.948+00:00",[],{"title":415},{"VI":416},"Department of the Army, Personnel Research Branch, Ago, USA",{"title":418},{"VI":419},"Hubert E. 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R.Introduction to matrix analysis. New York: McGraw-Hill, 1960.\nLawley, D. N. A note on Karl Pearson's selection formulae.Proceedings of the Royal Society of Edinburgh (Section A), 1943–44,62, 28–30.\nMeredith, W. Notes on factorial invariance.Psychometrika, 1964,29, 177–185.\nThurstone, L. L.Multiple factor analysis. Chicago: University of Chicago Press, 1947.\nTucker, L. R. Implications of factor analysis of three-way matrices for measurement of change. In C. W. Harris (Ed.),Problems in measuring change. Madison: University of Wisconsin Press, 1963, 122–137.\nTucker, L. R. Some mathematical notes on three-mode factor analysis.Psychometrika, 1966,31, 279–311.",{"EN":473},"Previous results of the application of Lawley's selection theorem to the common factor analysis model are extended to a revision of Tucker's three-mode principal components model. If the regression of the three-mode manifest variates on variates used to select subpopulations is both linear and homoscedastic, the two factor pattern matrices, the core matrix, and the residual variance-covariance matrix in the three-mode model can all be assumed to be invariant across subpopulations. The implication of this finding for simple structure is discussed.",{"EN":475},"A note on invariance in three-mode factor analysis",{"VOID":477},"10.1007\u002FBF02289329","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02289329",[480],{"id":481,"sortIndex":20,"researcher":19,"roles":482,"affiliations":483,"properties":494},"41f5b401-ce7c-4347-a286-37f6da930f17",[153],[484],{"id":19,"sortIndex":20,"affiliation":485,"properties":19},{"id":486,"createTime":487,"updateTime":488,"relativeEntities":489,"slug":490,"properties":491,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"20860779-3bce-4b7c-82dd-5c4687bced65","2024-01-15T00:37:55.768+00:00","2024-09-02T07:39:17.182+00:00",[],"Educational-Testing-Service-USA",{"title":492},{"VI":493},"Educational Testing Service USA",{"title":495},{"VI":496},"Bruce 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H. (1980).K-sample pattern hypotheses on correlation matrices by the method of generalized least squares. University of British Columbia, Institute of Applied Mathematics and Statistics Research Bulletin 80-2.\nSteiger, J. H. (June 2, 1982). A robust large-sample procedure for comparing dependent correlations. Paper presented at the annual Spring Meeting of the Psychometric Society.\nBrowne, M. W. (1977). The analysis of patterned correlation matrices by generalized least squares.British Journal of Mathematical and Statistical Psychology, 30, 113–124.\nBrowne, M. W. (1982). Covariance structures. In D. M. Hawkins (Ed.),Topics in applied multivariate analysis. Cambridge: Cambridge University Press.\nDevlin, S. J., Gnanadesikan, R., & Kettenring, J. R. (1976). Some multivariate applications of elliptical distributions. In S. Ideka (Ed.),Essays in probability and statistics. Tokyo: Shinko Tsusho.\nDuncan, G. T., & Layard, M. W. J. (1973). A Monte Carlo study of asymptotically robust tests for correlation coefficients.Biometrika, 60, 551–558.\nHsu, P. L. (1949). The limiting distribution of functions of sample means and application to testing hypotheses.Proceedings of the First Berkely Symposium on Mathematical Statistics and Probability, 359–402.\nIsserlis, L. (1916). On certain probable errors and correlation coefficients of multiple frequency distributions with skew regression.Biometrika, 11, 185–190.\nKnuth, D. E. (1969).The art of computer programming. Vol. 2. Reading, Mass.: Addison-Wesley.\nJöreskog, K. G. (1967). Some contributions to maximum likelihood factor analysis.Psychometrika, 32, 443–482.\nLord, F. M. (1975). Automated hypothesis tests and standard errors for nonstandard problems.The American Statistician, 29, 56–59.\nMardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications.Biometrika, 57, 519–530.\nMardia, K. V. (1974). 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On the probable error of frequency constants and on the influence of random selection of variation and correlation.Philosophical Transactions of the Royal Society of London, Series, A, 191, 229–311.\nRao, C. R. (1973).Linear statistical inference and its applications (2nd Ed.). New York: Wiley.\nRoss, G. J. S. (1970). The efficient use of function minimisation in nonlinear maximum likelihood estimation.Applied Statistics, 19, 205–221.\nSchuenemeyer, J. H., & Bargmann, R. E. (1978). Maximum eccentricity as a union-intersection test statistic in multivariate analysis.Journal of Multivariate Analysis, 8, 268–273.\nShapiro, A. (1983). Asymptotic distribution theory in the analysis of covariance structures (a unified approach).South African Statistical Journal, 17, 33–81.\nSteiger, J. H. (1979). MULTICORR: A computer program for fast, accurate, small-sample testing of correlational pattern hypotheses.Educational and Psychological Measurement, 39, 677–680.\nSteiger, J. H. (1980a). Tests for comparing elements of a correlation matrix.Psychological Bulletin, 87, 245–251.\nSteiger, J. H. (1980b). Testing pattern hypotheses on correlation matrices: Alternative statistics and some empirical results.Multivariate Behavioral Research, 15, 335–352.\nSteiger, J. H., & Hakstian, A. R. (1982). The asymptotic distribution of elements of a correlation matrix: Theory and application.British Journal of Mathematical and Statistical Psychology, 35, 208–215.\nSteiger, J. H., & Hakstian, A. R. (1983). A historical note on the asymptotic distribution of correlations.British Journal of Mathematical and Statistical Psychology, 36, 157.\nVenables, W. (1976). Some implications of the union-intersection principle for tests of sphericity.Journal of Multivariate Analysis, 6, 175–190.",{"EN":684},"A general procedure is provided for comparing correlation coefficients between optimal linear composites. 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R. and Mosteller, F. A mathematical model for simple learning.Psychol. Rev., 1951,58, 313–323.\nEstes, W. K. and Burke, C. J. A theory of stimulus variability in learning.Psychol. Rev., 1953,60, 276–286.\nGulliksen, H. A generalization of Thurstone's learning function.Psychometrika, 1953,18, 297–307.\nHull, C. L. Principles of behavior. New York: Appleton-Century-Crofts, 1943.\nRestle, F. A theory of discrimination learning.Psychol. Rev., 1955,62, 11–19.\nSchoeffler, M. O. Probability of response to compounds of discriminated stimuli.J. exp. Psychol., 1954,48, 323–329.\nSpence, K. W. The nature of discrimination learning in animals.Psychol. Rev., 1936,43, 427–449.\nThurstone, L. L. The learning function.J. gen. Psychol., 1930,3, 469–493.",{"EN":851},"A model is proposed to predict the performance on a compound stimulus as a function of the performance on the component stimuli in a two-choice situation. Data from a learning task are used to evaluate the model.",{"EN":853},"A model for response tendency combination",{"VOID":855},"10.1007\u002FBF02288970","Author affiliation is blank","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02288970",[859],{"id":860,"sortIndex":20,"researcher":19,"roles":861,"affiliations":862,"properties":863},"835a62a7-5c6c-40e7-b6f4-1a17adbe2e9e",[153],[],{"title":864},{"VI":865},"David Birch",{"url":857,"publisher":867,"properties":902},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":868,"slug":10,"properties":869,"entityType":17,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"subjectFields":873,"manageAffiliations":874,"indexDatabases":875,"url":119,"thumbnailPath":19,"statistic":897,"gsStatistic":19,"type":129,"analyzePriority":19},[],{"issn":870,"eissn":871,"title":872},{"VOID":13},{"VOID":15},{"EN":10},[],[],[876,883,890],{"id":102,"indexDatabase":877,"url":98,"indexYears":19,"academicFieldIds":882,"indexDatabaseRanking":19},{"id":104,"createTime":105,"updateTime":106,"relativeEntities":878,"label":879,"description":880,"key":113,"publicationTags":881,"standard":19},[],{"EN":109,"VI":109},{"VI":111,"EN":112},[115,97],[117,118],{"id":63,"indexDatabase":884,"url":76,"indexYears":77,"academicFieldIds":889,"indexDatabaseRanking":81},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":885,"label":886,"description":887,"key":73,"publicationTags":888,"standard":19},[],{"EN":70,"VI":70},{"EN":70,"VI":72},[75],[79,80],{"id":83,"indexDatabase":891,"url":98,"indexYears":19,"academicFieldIds":896,"indexDatabaseRanking":19},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":892,"label":893,"description":894,"key":94,"publicationTags":895,"standard":19},[],{"EN":90,"VI":90},{"VI":92,"EN":93},[96,97],[100],{"impactFactor":20,"impactFactorByYear":898,"i10Index":20,"i10IndexLast5Year":20,"totalPublication":122,"totalPublicationByYear":899,"totalCitation":20,"totalCitationByYear":900,"totalCitationPerPublication":20,"totalCitationPerPublicationByYear":901,"hindexLast5Year":20,"hindex":20},{},{"1940":124,"1943":124,"1953":124,"1957":124,"1961":125,"1963":124,"1964":124,"1967":124,"1978":124,"1980":124,"1981":124,"1982":125,"1985":124,"1986":124,"1990":124,"1992":124,"1993":125,"1995":124,"2001":124,"2003":124,"2010":126,"2015":124,"2016":125,"2017":124,"2023":124},{},{},{"volume":903,"pages":905},{"VOID":904},"22",{"VOID":906},"373-380","1957-12-01",1957,{"id":910,"createTime":911,"updateTime":911,"relativeEntities":912,"slug":19,"properties":913,"entityType":147,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"primaryUrl":922,"fullTextUrl":19,"authors":923,"publicationType":181,"publisherRelationship":941,"citationCount":19,"citationInfo":19,"publishDate":982,"publishYear":983,"citationAnalyzeStatus":18,"lastCitationAnalyze":19,"indexDatabases":19,"openAccess":19,"references":19,"isForceReanalyzing":225},"a106859d-8d5d-49a9-bcea-a48b9fa40ca1","2024-01-15T23:52:59.600+00:00",[],{"references":914,"abstract":916,"title":918,"doi":920},{"VOID":915},"Anderson, M. 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Applied Psychological Measurement, 14, 73–81.",{"EN":917},"Effort has been devoted to account for heteroscedasticity with respect to observed or latent moderator variables in item or test scores. For instance, in the multi-group generalized linear latent trait model, it could be tested whether the observed (polychoric) covariance matrix differs across the levels of an observed moderator variable. In the case that heteroscedasticity arises across the latent trait itself, existing models commonly distinguish between heteroscedastic residuals and a skewed trait distribution. These models have valuable applications in intelligence, personality and psychopathology research. However, existing approaches are only limited to continuous and polytomous data, while dichotomous data are common in intelligence and psychopathology research. Therefore, in present paper, a heteroscedastic latent trait model is presented for dichotomous data. The model is studied in a simulation study, and applied to data pertaining alcohol use and cognitive ability.",{"EN":919},"Heteroscedastic Latent Trait Models for Dichotomous Data",{"VOID":921},"10.1007\u002Fs11336-014-9406-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11336-014-9406-0",[924],{"id":925,"sortIndex":20,"researcher":19,"roles":926,"affiliations":927,"properties":938},"438e7def-8140-4e04-95bb-def8fd1e1565",[153],[928],{"id":19,"sortIndex":20,"affiliation":929,"properties":19},{"id":930,"createTime":931,"updateTime":932,"relativeEntities":933,"slug":934,"properties":935,"entityType":48,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20},"f2d64196-fbd9-46be-a533-8960c786ae6e","2024-01-29T15:27:07.290+00:00","2024-08-31T08:43:49.056+00:00",[],"Psychological-Methods-Department-of-Psychology-University-of-Amsterdam-Amsterdam-The-Netherlands",{"title":936},{"VI":937},"Psychological Methods, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands",{"title":939},{"VI":940},"Dylan Molenaar",{"url":922,"publisher":942,"properties":977},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":943,"slug":10,"properties":944,"entityType":17,"verifyStatus":18,"verifyTime":19,"verifyNote":19,"syncStatus":18,"languages":19,"translateLanguages":19,"viewCount":20,"subjectFields":948,"manageAffiliations":949,"indexDatabases":950,"url":119,"thumbnailPath":19,"statistic":972,"gsStatistic":19,"type":129,"analyzePriority":19},[],{"issn":945,"eissn":946,"title":947},{"VOID":13},{"VOID":15},{"EN":10},[],[],[951,958,965],{"id":102,"indexDatabase":952,"url":98,"indexYears":19,"academicFieldIds":957,"indexDatabaseRanking":19},{"id":104,"createTime":105,"updateTime":106,"relativeEntities":953,"label":954,"description":955,"key":113,"publicationTags":956,"standard":19},[],{"EN":109,"VI":109},{"VI":111,"EN":112},[115,97],[117,118],{"id":63,"indexDatabase":959,"url":76,"indexYears":77,"academicFieldIds":964,"indexDatabaseRanking":81},{"id":65,"createTime":66,"updateTime":67,"relativeEntities":960,"label":961,"description":962,"key":73,"publicationTags":963,"standard":19},[],{"EN":70,"VI":70},{"EN":70,"VI":72},[75],[79,80],{"id":83,"indexDatabase":966,"url":98,"indexYears":19,"academicFieldIds":971,"indexDatabaseRanking":19},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":967,"label":968,"description":969,"key":94,"publicationTags":970,"standard":19},[],{"EN":90,"VI":90},{"VI":92,"EN":93},[96,97],[100],{"impactFactor":20,"impactFactorByYear":973,"i10Index":20,"i10IndexLast5Year":20,"totalPublication":122,"totalPublicationByYear":974,"totalCitation":20,"totalCitationByYear":975,"totalCitationPerPublication":20,"totalCitationPerPublicationByYear":976,"hindexLast5Year":20,"hindex":20},{},{"1940":124,"1943":124,"1953":124,"1957":124,"1961":125,"1963":124,"1964":124,"1967":124,"1978":124,"1980":124,"1981":124,"1982":125,"1985":124,"1986":124,"1990":124,"1992":124,"1993":125,"1995":124,"2001":124,"2003":124,"2010":126,"2015":124,"2016":125,"2017":124,"2023":124},{},{},{"volume":978,"pages":980},{"VOID":979},"80",{"VOID":981},"625-644","2014-08-01",2014]