國立虎尾科技大學 |

Multiple Imputation Methods for Latent Profile Analysis in Education and the Behavioral Sciences.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multiple Imputation Methods for Latent Profile Analysis in Education and the Behavioral Sciences./
作者:	Waldman, Marcus R.
面頁冊數:	1 online resource (226 pages)
附註:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
標題:	Educational psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9798678150264

Multiple Imputation Methods for Latent Profile Analysis in Education and the Behavioral Sciences.
Waldman, Marcus R.

Multiple Imputation Methods for Latent Profile Analysis in Education and the Behavioral Sciences. - 1 online resource (226 pages)

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ed.D.)--Harvard University, 2020.

Includes bibliographical references

Researchers in education and the behavioral sciences are increasingly conducting latent profile analysis by focusing investigations on subpopulations of individuals to describe individual differences with greater nuance. At the same time, missing data is practically inevitable, and even modest missingness rates can threaten the validity of inferences. Multiple imputation is a powerful strategy to treat missing data, but it suffers from key limitations in both the imputation and pooling phases when conducting latent profile analysis.In this dissertation, I conduct three studies to address these gaps. In the first study (Chapter 2), I evaluate whether recursive partitioning imputation algorithms better mitigate nonresponse bias than alternative missing data approaches that are common in practice. I find that recursive partitioning imputation algorithms perform well when sample sizes are large (N = 1,200), but not when sample sizes are small (N = 300) or when class separation is weak (i.e., entropy ≈ .74). In response, I propose a hybrid imputation procedure in the second study (Chapter 3); the proposed method embeds a finite mixture model to generate imputations using a joint modeling framework within a larger chained equations procedure. I demonstrate the hybrid imputation procedure using real-world data.In the final study (Chapter 4), I scrutinize current practices for conducting finite mixture model selection with missing data. I am not aware of any studies evaluating whether model selection decisions are sensitive to real-world missing data problems. I fill this gap in research by studying whether selection decisions are sensitive to missing data if either a full information maximum likelihood (FIML) or a multiple imputation strategy is employed. Two findings emerge. First, with regards to FIML, the BIC under extracts the true number of classes relative to the complete data condition in the presence of small sample sizes and small classes. Second, with regards to multiple imputation, current practices for pooling information criteria result in model selection decisions that poorly replicate the decisions that would have been made had the data been complete. I propose two remedial procedures for future practice, and, using simulations, I show that these two procedures outperform current practices.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798678150264Subjects--Topical Terms:

555103
Educational psychology.
Subjects--Index Terms:

Multiple imputationIndex Terms--Genre/Form:

554714
Electronic books.

Multiple Imputation Methods for Latent Profile Analysis in Education and the Behavioral Sciences.
LDR:03750ntm a22003977 4500 001 1144592
005 20240611104313.5
006 m o d
007 cr mn ---uuuuu
008 250605s2020 xx obm 000 0 eng d
020 $a 9798678150264
035 $a (MiAaPQ)AAI28221034
035 $a (MiAaPQ)0084vireoharvard254Waldman
035 $a AAI28221034
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Waldman, Marcus R. $3 1469667
245 1 0 $a Multiple Imputation Methods for Latent Profile Analysis in Education and the Behavioral Sciences.
264 0 $c 2020
300 $a 1 online resource (226 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
500 $a Advisor: Ho, Andrew.
502 $a Thesis (Ed.D.)--Harvard University, 2020.
504 $a Includes bibliographical references
520 $a Researchers in education and the behavioral sciences are increasingly conducting latent profile analysis by focusing investigations on subpopulations of individuals to describe individual differences with greater nuance. At the same time, missing data is practically inevitable, and even modest missingness rates can threaten the validity of inferences. Multiple imputation is a powerful strategy to treat missing data, but it suffers from key limitations in both the imputation and pooling phases when conducting latent profile analysis.In this dissertation, I conduct three studies to address these gaps. In the first study (Chapter 2), I evaluate whether recursive partitioning imputation algorithms better mitigate nonresponse bias than alternative missing data approaches that are common in practice. I find that recursive partitioning imputation algorithms perform well when sample sizes are large (N = 1,200), but not when sample sizes are small (N = 300) or when class separation is weak (i.e., entropy ≈ .74). In response, I propose a hybrid imputation procedure in the second study (Chapter 3); the proposed method embeds a finite mixture model to generate imputations using a joint modeling framework within a larger chained equations procedure. I demonstrate the hybrid imputation procedure using real-world data.In the final study (Chapter 4), I scrutinize current practices for conducting finite mixture model selection with missing data. I am not aware of any studies evaluating whether model selection decisions are sensitive to real-world missing data problems. I fill this gap in research by studying whether selection decisions are sensitive to missing data if either a full information maximum likelihood (FIML) or a multiple imputation strategy is employed. Two findings emerge. First, with regards to FIML, the BIC under extracts the true number of classes relative to the complete data condition in the presence of small sample sizes and small classes. Second, with regards to multiple imputation, current practices for pooling information criteria result in model selection decisions that poorly replicate the decisions that would have been made had the data been complete. I propose two remedial procedures for future practice, and, using simulations, I show that these two procedures outperform current practices.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Educational psychology. $3 555103
650 4 $a Statistics. $3 556824
653 $a Multiple imputation
653 $a Latent profile analysis
653 $a FIML
653 $a Recursive partitioning
653 $a CART
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0525
710 2 $a Harvard University. $b Quantitative Policy Analysis in Education. $3 1187528
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 82-05B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28221034 $z click for full text (PQDT)