43-wentorai-research-plugins-skills-domains-education-educational-research-methods

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SKILL.md

Educational Research Methods

A comprehensive skill for conducting rigorous educational research using both quantitative and qualitative methodologies. Covers study design, data collection instruments, analysis techniques, and reporting standards specific to education scholarship.

Study Design Frameworks

Quantitative Designs

Educational quantitative research typically follows one of these designs:

Design	Purpose	Example
Randomized controlled trial (RCT)	Causal inference	Random assignment to instruction methods
Quasi-experimental	Causal inference without randomization	Pre-post comparison with matched control
Correlational	Relationship exploration	Survey linking self-efficacy to GPA
Longitudinal panel	Change over time	Tracking cohort achievement K-12
Cross-sectional survey	Snapshot description	National teacher satisfaction survey

Qualitative Designs

Common qualitative traditions in education:

Ethnography: Extended immersion in a classroom or school culture to produce thick description
Case study: In-depth examination of a bounded system (a program, a school, a student)
Grounded theory: Iterative coding to build theory from interview and observation data
Phenomenology: Exploring the lived experience of participants (e.g., first-generation college students)
Action research: Practitioners systematically studying their own practice to improve it

Mixed Methods

Sequential and concurrent mixed-methods designs are increasingly common in education research:

Sequential Explanatory:
  Phase 1: Quantitative survey (n=500) --> identify patterns
  Phase 2: Qualitative interviews (n=20) --> explain patterns

Concurrent Triangulation:
  QUAN data collection + QUAL data collection (simultaneous)
  --> merge and compare findings at interpretation stage

Embedded Design:
  Primary: RCT measuring learning outcomes
  Secondary: Classroom observations embedded within treatment arm

import pandas as pd
from scipy import stats

# Reliability analysis for a Likert-scale instrument
def cronbach_alpha(df: pd.DataFrame) -> float:
    """
    Compute Cronbach's alpha for internal consistency reliability.
    df: DataFrame where each column is an item, each row a respondent.
    Acceptable threshold: alpha >= 0.70 for research purposes.
    """
    n_items = df.shape[1]
    item_vars = df.var(axis=0, ddof=1)
    total_var = df.sum(axis=1).var(ddof=1)
    alpha = (n_items / (n_items - 1)) * (1 - item_vars.sum() / total_var)
    return round(alpha, 4)

# Example usage with a 6-item motivation scale
data = pd.DataFrame({
    'item1': [4, 5, 3, 4, 5, 3, 4, 5],
    'item2': [3, 4, 3, 4, 5, 2, 4, 4],
    'item3': [4, 5, 4, 5, 4, 3, 5, 5],
    'item4': [3, 4, 2, 3, 5, 2, 3, 4],
    'item5': [4, 5, 3, 4, 5, 3, 4, 5],
    'item6': [3, 4, 3, 4, 4, 3, 4, 4],
})

alpha = cronbach_alpha(data)
print(f"Cronbach's alpha: {alpha}")
# alpha >= 0.70 indicates acceptable internal consistency

import statsmodels.api as sm
from statsmodels.formula.api import mixedlm

# Hierarchical Linear Model (HLM) -- essential for nested
# education data (students within classrooms within schools)
# Example: predicting math achievement from student SES
# and classroom teaching quality

model = mixedlm(
    "math_score ~ student_ses + teaching_quality",
    data=df,
    groups=df["school_id"],
    re_formula="~teaching_quality"
)
result = model.fit()
print(result.summary())

# Effect size calculation (Cohen's d)
def cohens_d(group1, group2):
    n1, n2 = len(group1), len(group2)
    var1, var2 = group1.var(), group2.var()
    pooled_std = ((( n1 - 1) * var1 + (n2 - 1) * var2) / (n1 + n2 - 2)) ** 0.5
    return (group1.mean() - group2.mean()) / pooled_std

43-wentorai-research-plugins-skills-domains-education-educational-research-methods

Summary

SKILL.md

Educational Research Methods

Study Design Frameworks

Quantitative Designs

Qualitative Designs

Mixed Methods

Data Collection Instruments

Survey and Questionnaire Design

Observation Protocols

Interview Protocols

Analysis Techniques

Quantitative Analysis for Education Data

Qualitative Coding

Reporting Standards

APA and AERA Guidelines

Key Journals

Ethical Considerations

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