LEADER 03562oam 2200637I 450 001 9910823157003321 005 20240131145609.0 010 $a1-136-26632-1 010 $a0-203-10855-8 010 $a1-299-27868-X 010 $a1-136-26633-X 024 7 $a10.4324/9780203108550 035 $a(CKB)2560000000099183 035 $a(EBL)1143716 035 $a(OCoLC)830161412 035 $a(SSID)ssj0000832667 035 $a(PQKBManifestationID)12410564 035 $a(PQKBTitleCode)TC0000832667 035 $a(PQKBWorkID)10899963 035 $a(PQKB)11034474 035 $a(MiAaPQ)EBC1143716 035 $a(Au-PeEL)EBL1143716 035 $a(CaPaEBR)ebr10672711 035 $a(CaONFJC)MIL459118 035 $a(FINmELB)ELB133718 035 $a(EXLCZ)992560000000099183 100 $a20180706d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplied quantitative analysis in education and the social sciences /$fedited by Yaacov Petscher, Christopher Schatschneider, Donald L. Compton 210 1$aNew York :$cRoutledge,$d2013. 215 $a1 online resource (389 p.) 300 $aDescription based upon print version of record. 311 $a0-415-89349-6 311 $a0-415-89348-8 320 $aIncludes bibliographical references and index. 327 $aExtending conditional means modeling: an introduction to quantile regression / Yaacov Petscher, Jessica A.R. Logan, and Chengfu Zhou -- Using dominance analysis to estimate predictor importance in multiple regression / Razia Azen -- I am ROC curves (and so can you)! / Christopher Schatschneider -- Multilevel modeling: practical examples to illustrate a special case of SEM / Lee Branum-Martin -- Linear and quadratic growth models for continuous and dichotomous outcomes / Ann A. O'Connell, Jessica A. R. Logan, Jill Pentimonti, and D. Betsy McCoach -- Exploratory and confirmatory factor analysis / Rex Kline -- Factor analysis with categorical indicators: demonstration of item response theory / R.J. de Ayala -- Introduction to structural equation modeling / Richard Lomax -- Latent growth curve modeling using structural equation modeling / Ryan Bowles and Janelle J. Montroy -- Latent class/profile analysis / Karen Samuelsen and Katherine Raczynski -- n-level structural equation modeling / Paras Mehta. 330 $aTo say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model 606 $aRegression analysis 606 $aMathematical statistics 615 0$aRegression analysis. 615 0$aMathematical statistics. 676 $a519.5/36 701 $aCompton$b Donald L.$f1960-$01659645 701 $aPetscher$b Yaacov M$01659646 701 $aSchatschneider$b Christopher$01659647 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823157003321 996 $aApplied quantitative analysis in education and the social sciences$94014399 997 $aUNINA