View in EDS

Analyzing Transitions in Sequential Data with Marginal Models

Saved in:
Bibliographic Details
Title: Analyzing Transitions in Sequential Data with Marginal Models
Language: English
Authors: Jeffrey Matayoshi (ORCID 0000-0003-1321-8159), Shamya Karumbaiah
Source: Journal of Educational Data Mining. 2024 16(1):197-232.
Availability: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Peer Reviewed: Y
Page Count: 36
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Models, Statistical Bias, Data Analysis, Simulation, Evaluation Research, Research Design, Replication (Evaluation), Theory Practice Relationship, Error of Measurement
ISSN: 2157-2100
Abstract: Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have uncovered issues with the metrics and procedures commonly used to analyze these transitions. As such, our goal in this work is to address these issues by outlining an alternative procedure that is based on the use of marginal models. We begin by looking at the specific mechanisms responsible for a recently discovered statistical bias with several metrics used in sequential data analysis. After giving a theoretical explanation for the issue, we show that the marginal model procedure appears to adjust for this bias. Next, a related problem is that the common practice of removing transitions to repeated states has been shown to have unintended side-effects--to account for this issue, we develop a method for extending the marginal model procedure to this specific type of analysis. Finally, in a recent study evaluating the problem of multiple comparisons and sequential data analysis, the Benjamini-Hochberg (BH) procedure, a commonly used approach to control for false discoveries, did not perform as expected. By applying a technique from the biostatistics and epidemiology literature, we show that the performance of the BH procedure, when used with the marginal model method, can be brought back to its expected level. In all of our analyses, we evaluate the proposed method by both running simulations and using actual student data. The results indicate that the marginal model procedure seemingly compensates for the problems observed with other transition metrics, thus resulting in more accurate estimates of the importance of transitions between states.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1430535
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1430535
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: EJ1430535
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Analyzing Transitions in Sequential Data with Marginal Models
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jeffrey+Matayoshi%22">Jeffrey Matayoshi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1321-8159">0000-0003-1321-8159</externalLink>)<br /><searchLink fieldCode="AR" term="%22Shamya+Karumbaiah%22">Shamya Karumbaiah</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Data+Mining%22"><i>Journal of Educational Data Mining</i></searchLink>. 2024 16(1):197-232.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 36
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Bias%22">Statistical Bias</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Research%22">Evaluation Research</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Design%22">Research Design</searchLink><br /><searchLink fieldCode="DE" term="%22Replication+%28Evaluation%29%22">Replication (Evaluation)</searchLink><br /><searchLink fieldCode="DE" term="%22Theory+Practice+Relationship%22">Theory Practice Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Error+of+Measurement%22">Error of Measurement</searchLink>
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2157-2100
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have uncovered issues with the metrics and procedures commonly used to analyze these transitions. As such, our goal in this work is to address these issues by outlining an alternative procedure that is based on the use of marginal models. We begin by looking at the specific mechanisms responsible for a recently discovered statistical bias with several metrics used in sequential data analysis. After giving a theoretical explanation for the issue, we show that the marginal model procedure appears to adjust for this bias. Next, a related problem is that the common practice of removing transitions to repeated states has been shown to have unintended side-effects--to account for this issue, we develop a method for extending the marginal model procedure to this specific type of analysis. Finally, in a recent study evaluating the problem of multiple comparisons and sequential data analysis, the Benjamini-Hochberg (BH) procedure, a commonly used approach to control for false discoveries, did not perform as expected. By applying a technique from the biostatistics and epidemiology literature, we show that the performance of the BH procedure, when used with the marginal model method, can be brought back to its expected level. In all of our analyses, we evaluate the proposed method by both running simulations and using actual student data. The results indicate that the marginal model procedure seemingly compensates for the problems observed with other transition metrics, thus resulting in more accurate estimates of the importance of transitions between states.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1430535
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1430535
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 36
        StartPage: 197
    Subjects:
      – SubjectFull: Models
        Type: general
      – SubjectFull: Statistical Bias
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Simulation
        Type: general
      – SubjectFull: Evaluation Research
        Type: general
      – SubjectFull: Research Design
        Type: general
      – SubjectFull: Replication (Evaluation)
        Type: general
      – SubjectFull: Theory Practice Relationship
        Type: general
      – SubjectFull: Error of Measurement
        Type: general
    Titles:
      – TitleFull: Analyzing Transitions in Sequential Data with Marginal Models
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jeffrey Matayoshi
      – PersonEntity:
          Name:
            NameFull: Shamya Karumbaiah
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2157-2100
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Educational Data Mining
              Type: main
ResultId 1