Education Advanced Quantitative Modeling

Education Advanced Quantitative Modeling

Goal

Select, specify, run, and report advanced quantitative models that match education research questions and data structures.

Use After

Use after:

• education-quantitative-data-cleaning
• education-descriptive-statistics
• education-inferential-statistics
• education-validity-reliability-analysis
• education-quantitative-study-design
• education-learning-analytics-design
• education-program-evaluation

Do not expose the skill name to users. Present it as "高级量化模型分析".

Inputs

• Research questions/hypotheses
• Cleaned dataset
• Variable roles and levels
• Sample size and clustering structure
• Timepoints if longitudinal/pre-post/panel
• Measurement model if latent variables are used
• Intervention/control group design if applicable

Model Selection Guide

Workflow

1. Confirm whether an advanced model is justified.
2. Identify unit and level:
   • item
   • timepoint
   • student
   • class
   • teacher
   • school
   • region
3. Check sample size/model complexity.
4. Select model family.
5. Specify variables and equations/path diagram.
6. Check assumptions and diagnostics.
7. Run baseline/simple model first.
8. Add complexity stepwise.
9. Interpret parameters with educational meaning.
10. Produce tables, diagrams, and reporting text.

Tool Calls

R Packages

install.packages(c(
  "tidyverse", "lme4", "lmerTest", "performance", "broom.mixed",
  "lavaan", "semTools", "mediation", "interactions", "mirt",
  "TAM", "MatchIt", "WeightIt", "fixest", "did", "marginaleffects"
))

Multilevel model:

library(lme4)
library(lmerTest)
fit <- lmer(score_post ~ group + score_pre + (1 | class_id), data = data)
summary(fit)
performance::check_model(fit)

Random slope model:

fit <- lmer(score_post ~ group + score_pre + (1 + group | class_id), data = data)

SEM:

library(lavaan)
model <- '
  motivation =~ m1 + m2 + m3 + m4
  engagement =~ e1 + e2 + e3 + e4
  achievement ~ motivation + engagement
  engagement ~ motivation
'
fit <- sem(model, data = data, estimator = "MLR", missing = "fiml")
summary(fit, fit.measures = TRUE, standardized = TRUE)

Mediation:

model <- '
  mediator ~ a * intervention
  outcome ~ b * mediator + cprime * intervention
  indirect := a * b
  total := cprime + (a * b)
'
fit <- sem(model, data = data, se = "bootstrap", bootstrap = 5000)
summary(fit, standardized = TRUE, ci = TRUE)

IRT:

library(mirt)
fit <- mirt(item_data, 1, itemtype = "2PL")
summary(fit)
coef(fit, IRTpars = TRUE)

DID:

library(fixest)
fit <- feols(outcome ~ treatment * post + covariates | school_id + time, data = data)
summary(fit)

Propensity score matching:

library(MatchIt)
m <- matchit(treatment ~ pre_score + gender + grade + ses, data = data, method = "nearest")
matched <- match.data(m)

Python Packages

pip install pandas numpy statsmodels linearmodels semopy pyirt scikit-learn causalinference

Mixed model:

import statsmodels.formula.api as smf
fit = smf.mixedlm("score_post ~ group + score_pre", data=df, groups=df["class_id"]).fit()
print(fit.summary())

DID:

import statsmodels.formula.api as smf
fit = smf.ols("outcome ~ treatment * post + covariates", data=df).fit(cov_type="cluster", cov_kwds={"groups": df["school_id"]})
print(fit.summary())

Specialized Tools

Mplus: https://www.statmodel.com/
HLM: https://ssicentral.com/index.php/products/hlm-general/
Stata: https://www.stata.com/
AMOS: https://www.ibm.com/products/structural-equation-modeling-sem
Winsteps: https://www.winsteps.com/
jamovi SEMLj module: https://semlj.github.io/

Output Format

1. Model Choice Table

2. Variable-Level Table

Levels:

• item
• timepoint
• student
• class
• teacher
• school
• region

3. Model Specification

4. Model Results Table

5. Diagnostics / Fit

Mermaid Templates

Mediation

flowchart LR
  X["Intervention"] --> M["Mediator"]
  M --> Y["Outcome"]
  X --> Y

Multilevel Data

flowchart TD
  S["School"] --> C1["Class"]
  C1 --> ST1["Student"]
  C1 --> ST2["Student"]

Education-Specific Guidance

• Education data are often nested; inspect ICC before ignoring class/school effects.
• SEM requires theory-driven measurement and structural models.
• DID requires credible parallel trends; do not use it just because data are pre/post.
• IRT requires item-level response data and enough examinees/items.
• Mediation with cross-sectional data should be interpreted cautiously.
• Program evaluation with nonrandom assignment may require baseline controls or propensity methods.

Quality Rules

• Start with the simplest defensible model.
• Do not use advanced models to disguise weak design.
• Report model assumptions and diagnostics.
• Avoid causal language without causal identification.
• Keep model complexity proportional to sample size.
• For nested education data, cluster-robust SEs or multilevel models may be needed.

User-Facing Closure

End by choosing the next modeling step:

根据你的数据结构，最合适的高级模型是 [模型]，原因是 [简短原因]。接下来我可以先帮你写模型设定和变量层级表，或者直接生成 R/SPSS/Stata/Mplus 分析脚本。