Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students
Abstract
:1. Introduction
1.1. The Importance of Executive Function Development in School-Age Children
1.2. Child-Related Predictors of EF Development
1.3. Parent-Related Predictors of EF Development
1.4. Home-Related Predictors of EF Development
1.5. Developmental Changes in EF
- What are the characteristics and incidences of EFD longitudinal trajectory patterns?
- What are the characteristics of the predictors associated with EFD trajectory patterns?
2. Materials and Methods
2.1. Procedure
2.2. Participants
2.3. Measures
2.3.1. Children’s Executive Function Difficulty
2.3.2. Child-Related Predictors of EFD Developmental Changes
2.3.3. Parent-Related Predictors of EFD Developmental Changes
2.3.4. Home-Related Predictors of EFD Developmental Changes
2.4. Statistical Analyses
2.4.1. Latent Class Growth Analysis
2.4.2. One-Way ANOVA and Spearman’s Rank Correlation Analysis
2.4.3. Bayesian Network and xgBoosting Learning
2.4.4. Evaluating Learning Model
3. Results
3.1. Characteristics and Incidence of EFD Longitudinal Trajectory Pattern
3.2. Characteristics of Predictors Associated with EFD Trajectory Patterns
3.2.1. Differences of Predictors
3.2.2. Spearman’s Rank Correlation
3.3. Performance Evaluation of BNL
3.3.1. Comparison with xgBoosting
3.3.2. Bayesian Network Structural Analysis and Predictors
4. Discussion
4.1. The Usefulness of LCGA to Analyze EFD Longitudinal Trajectory Patterns
4.2. The Usefulness of BNL to Analyze Predictors of EFD Longitudinal Trajectories
4.3. Limitations and Suggestions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Time | Predictor Classification | Measured Variable |
---|---|---|
5th (3-year-olds) | child-related predictors | C_5TS1 = Activity as a subvariable of EAS; C_5TS2 = Emotionality as a subvariable of EAS; C_5TS3 = Sociality as a subvariable of EAS |
7th (5-year-olds) | parent-related predictors | M_7FLI = Family interaction recognized by the mother; F_7FLI = Family interaction recognized by the father; M_7EDU = Educational level of the mother; F_7EDU = Educational level of the mother |
home-related predictors | DH_7INC = Monthly income | |
8th (6-year-olds) | child-related predictors | C_8SEI = Self-esteem; C_8HPY = Happiness; C_8BC2 = Anxious/depressed; C_8BC3 = Somatic complaints; C_8BC6 = Attention problems; C_8BC7 = Aggressive behaviors; C_8BC8 = Other problems; C_8BC9 = DSM-affective problems; C_8BC10 = DSM anxiety problems; C_8BC12 = DSM attention-deficit/hyper activity problems; C_8BC13 = DSM oppositional defiant problems; C_8BC14 = Withdrawn/depressed; C_8BC15 = Rule-breaking behavior; C_8BC16 = Social problems; C_8BC17 = Thought problems; C_8BC18 = DSM somatic problems; C_8BC19 = DSM conduct problems; C_8BC20 = Obsessive–compulsive symptom; C_8BC21 = Post-traumatic stress problems; C_8BC22 = Sluggish cognitive tempo. |
Parent-related predictors | M_8DPR = Depression recognized by the mother; M_8PRS = Parenting stress recognized by the mother; M_8MRC = Marital conflict recognized by the mother; M_8CR1 = Parent–child interaction of mother; M_8CR2 = Controlled parenting of mother; M_8CR3 = Warm parenting recognized by the mother; M_8CR4 = Coparenting integrity recognized by the mother; M_8CR5 = Coparenting reprimand recognized by the mother; M_8CR6 = Coparenting disparagement recognized by the mother; M_8CR7 = Coparenting conflict recognized by the mother; F_8DPR = Depression recognized by the father; F_8PRS = Parenting stress recognized by the father; F_8MRC = Marital conflict recognized by the father; F_8CR1 = Parent–child interaction recognized by the father; F_8CR2 = Controlled parenting recognized by the father; F_8CR3 = Warm parenting recognized by the father; F_8CR4 = Coparenting integrity recognized by the father; F_8CR5 = Coparenting reprimand recognized by the father; F_8CR6 = Coparenting disparagement recognized by the father; F_8CR7 = Coparenting conflict recognized by the father | |
home-related predictors | MH_8SES = Subjective SES; MH_8EN1 = Responsivity; MH_8EN2 = Encouragement of maturity; MH_8EN3 = Emotional climate; MH_8EN4 = Learning materials and opportunities; MH_8EN5 = Enrichment; MH_8EN6 = Family companionship; MH_8EN7 = Family integration; MH_8EN8 = Physical environment. | |
Response variable | EFDs = executive function difficulties | |
9th (7-year-olds) | response variable | EFDs = executive function difficulties |
10th (8-year-olds) | response variable | EFDs = executive function difficulties |
11th (9-year-olds) | response variable | EFDs = executive function difficulties |
Number of Classes | loglik | AIC | BIC | SABIC | Entropy | Class 1 (%) | Class 2 (%) | Class 3 (%) | Class 4 (%) |
---|---|---|---|---|---|---|---|---|---|
1 | −1059.60 | 2129.20 | 2153.77 | 2137.89 | 1.00 | 100.00 | |||
2 | −186.55 | 393.10 | 442.24 | 410.48 | .86 | 65.21 | 34.79 | ||
3 | 236.97 | −443.94 | −370.23 | −417.87 | .89 | 50.69 | 40.76 | 8.65 | |
4 | 338.75 | −637.51 | −539.23 | −602.75 | .83 | 42.45 | 32.01 | 19.68 | 5.86 |
Wave | Total (n = 1006) | Class 1 (n = 509) | Class 2 (n = 410) | Class 3 (n = 87) | F | p |
---|---|---|---|---|---|---|
m ± sd | m ± sd | m ± sd | m ± sd | |||
w8 | 1.44 ± 0.30 | 1.22 ± 0.15 | 1.59 ± 0.21 | 1.97 ± 0.27 | 796.83 | <.001 |
w9 | 1.47 ± 0.31 | 1.24 ± 0.14 | 1.63 ± 0.19 | 2.04 ± 0.23 | 1129.95 | <.001 |
w10 | 1.48 ± 0.33 | 1.25 ± 0.16 | 1.62 ± 0.19 | 2.14 ± 0.26 | 1091.39 | <.001 |
w11 | 1.43 ± 0.32 | 1.22 ± 0.16 | 1.57 ± 0.21 | 2.07 ± 0.23 | 906.85 | <.001 |
Model | 0:1 | Threshold | Accuracy | Sensitivity | Specificity | AUC | |
---|---|---|---|---|---|---|---|
Bayesian network modeling | 1 | class 1: class 2+3 | .50 | .80 (.75–.84) | .86 (.79–.91) | .85 (.78–.91) | .86 (.82–.90) |
2 | class 1: class 2 | .50 | .79 (.73–.84) | .74 (.65–.82) | .83 (.76–.89) | .87 (.82–.91) | |
3 | class 1: class 3 | .50 | .92 (.87–.96) | .96 (.79–1.00) | .91 (.85–.95) | .97 (.95–.99) | |
4 | class 1+2: class 3 | .50 | .82 (.77–.87) | .86 (.64–.97) | .82 (.77–.87) | .93 (.90–.97) | |
5 | class 2: class 3 | .50 | .72 (.64–.80) | .78 (.56–.93) | .71 (.62–.79) | .84 (.77–.92) | |
1 | class 1: class 2+3 | .85 | .69 (.63–.74) | .68 (.60–.76) | .69 (.61–.77) | .79 (.74–.84) | |
2 | class 1: class 2 | .85 | .80 (.75–.85) | .74 (.65–.82) | .85 (.78–.91) | .86 (.82–.91) | |
3 | class 1: class 3 | .85 | .88 (.82–.92) | .88 (.68–.97) | .88 (.81–.93) | .96 (.93–.99) | |
4 | class 1+2: class 3 | .85 | .81 (.76–.86) | .86 (.64–.97) | .81 (.76–.86) | .91 (.86–.96) | |
5 | class 2: class 3 | .85 | .59 (.50–.67) | .70 (.47–.87) | .57 (.47–.66) | .69 (.58–.80) | |
xgBoosting | 1 | class 1: class 2+3 | .87 (.83–.91) | .91 (.85–.95) | .83 (.75–.89) | .96 (.93–.98) | |
2 | class 1: class 2 | .90 (.86–.94) | .94 (.88–.98) | .87 (.80–.92) | .96 (.93–.98) | ||
3 | class 1: class 3 | .95 (.91–.98) | .96 (.79–1.00) | .95 (.90–.98) | .98 (.97–1.00) | ||
4 | class 1+2: class 3 | .92 (.89–.95) | .90 (.70–.99) | .93 (.89–.95) | .98 (.96–1.00) | ||
5 | class 2: class 3 | .93 (.87–.96) | .87 (.66–.97) | .94 (.87–.99) | .95 (.87–1.00) |
Model | 0 : 1 | Threshold | Effect Node (C_EFPT)’s Parent Node |
---|---|---|---|
1 | class 1: class 2+3 | .50 | C_GEND, C_5TS2, C_8BC9, C_8BC17, M_8PRS |
2 | class 1: class 2 | .50 | C_GEND, C_5TS2, C_8HPY, C_8BC9, M_8PRS, MH_8EN2 |
3 | class 1: class 3 | .50 | C_GEND, C_5TS2, C_8SEI, C_8HPY, C_8BC6, C_8BC9, C_8BC10, C_8BC17, C_8BC19, C_8BC20, M_8DPR, M_8CR5, M_8CR7, F_8PRS, F_8CR4, F_8CR6, MH_8SES, MH_8EN2, MH_8EN4, MH_8EN8 |
4 | class 1+2: class 3 | .50 | C_GEND, C_5TS2, C_8HPY, C_8BC6, C_8BC9, C_8BC10, C_8BC16, C_8BC22, M_8DPR, M_8CR7, F_8CR3, F_8CR4, MH_8EN2, MH_8EN4 |
5 | class 2: class 3 | .50 | C_GEND, C_5TS2, C_8BC9, C_8BC10, M_8DPR, M_8CR5, M_8CR7, F_7FLI, F_8CR4, MH_8SES |
1 | class 1: class 2+3 | .85 | C_GEND, C_5TS2, M_8PRS |
2 | class 1: class 2 | .85 | C_GEND, C_5TS2, C_8BC9, M_8PRS |
3 | class 1: class 3 | .85 | C_GEND, C_5TS2, C_8HPY, C_8BC6, C_8BC17, C_8BC19, M_8DPR, F_8PRS, MH_8EN2, MH_8EN4 |
4 | class 1+2: class 3 | .85 | C_GEND, C_5TS2, C_8HPY, C_8BC10, M_8DPR, M_8CR7 |
5 | class 2: class 3 | .85 | C_GEND, C_5TS2, M_8CR7, F_8CR4 |
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Goh, E.-K.; Jeon, H.-J. Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students. J. Intell. 2022, 10, 74. https://doi.org/10.3390/jintelligence10040074
Goh E-K, Jeon H-J. Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students. Journal of Intelligence. 2022; 10(4):74. https://doi.org/10.3390/jintelligence10040074
Chicago/Turabian StyleGoh, Eun-Kyoung, and Hyo-Jeong Jeon. 2022. "Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students" Journal of Intelligence 10, no. 4: 74. https://doi.org/10.3390/jintelligence10040074
APA StyleGoh, E. -K., & Jeon, H. -J. (2022). Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students. Journal of Intelligence, 10(4), 74. https://doi.org/10.3390/jintelligence10040074