Suppose that you want to study the effects of the decisions of Board of Education on various aspects of education, such as teacher morale, pupil achievement, relations between teachers and administrators, teacher clique formation, would your research be experimental or exposed back to? Why?

 Such a study would be classified as ex post facto research, not experimental. Here’s why:

1. Nature of the Study

You are interested in examining the effects of decisions already made by the Board of Education on variables like:

  • Teacher morale
  • Pupil achievement
  • Teacher–administrator relations
  • Teacher clique formation

These are naturally occurring outcomes that happened after the Board's decisions were implemented. You, as the researcher, are not manipulating the decisions or assigning schools to different policies — you are observing the consequences of past events.

2. Why It Is Ex Post Facto Research

Criterion

Explanation

Manipulation of Independent Variable

Not possible — the Board’s decisions have already occurred.

Random Assignment

Not feasible — schools or teachers cannot be randomly assigned to different Board decisions.

Nature of Variables

The study observes how existing or past decisions correlate with current outcomes.

Temporal Order

The "cause" (Board decisions) occurred in the past; effects are being studied afterward.

3. Why It Is Not Experimental

  • You cannot manipulate or assign educational boards to make certain decisions.
  • You cannot randomly assign teachers or students to policy environments.
  • An experiment would require controlled intervention, which is impractical in this policy-level context.

4. Appropriate Approach

  • Ex post facto (causal-comparative) or correlational design is most suitable.
  • You might use:
    • Surveys/interviews to assess teacher morale,
    • Academic records for pupil achievement,
    • Observations or ratings for administrative relationships or clique formation.
  • Statistical methods like regression analysis or ANCOVA can help control for confounding variables.

Conclusion

This research is ex post facto because:

  • It investigates the effects of existing decisions (independent variable) on naturally occurring educational outcomes (dependent variables),
  • And does so without experimental manipulation.

Let me know if you'd like a sample research design or framework for such a study.

Here is a sample research design framework for the ex post facto study titled:

Title:

“The Impact of Board of Education Decisions on Teacher Morale, Pupil Achievement, and Institutional Climate in Schools”

1. Research Design Type:

Ex Post Facto (Causal-Comparative) Research Design

Since the Board of Education’s decisions are already implemented, and the researcher cannot manipulate them, the study observes their effects retrospectively.

2. Objectives of the Study:

1.      To analyze the impact of Board decisions on teacher morale.

2.      To assess the relationship between Board decisions and student academic achievement.

3.      To study the influence of Board decisions on teacher–administrator relations.

4.      To investigate the relationship between Board decisions and teacher clique formation within schools.

3. Hypotheses:

·         H1: Schools affected by more inclusive Board decisions show significantly higher teacher morale.

·         H2: Student achievement is positively related to the quality of decision-making by the Board.

·         H3: Constructive Board policies lead to improved teacher–administrator relationships.

·         H4: Board decisions perceived as unfair or top-down lead to increased clique formation among teachers.

4. Variables:

Type

Variable

Independent Variable

Type/Nature of Board of Education Decisions (categorized by content, inclusivity, impact level)

Dependent Variables

- Teacher Morale- Pupil Achievement- Teacher–Administrator Relations- Clique Formation

5. Sampling Technique:

·         Population: Schools governed by different Boards of Education (within a district or state).

·         Sample: 20–30 schools purposively selected based on contrasting types of recent Board decisions (e.g., policy changes, restructuring, budget cuts, teacher evaluation changes).

6. Data Collection Tools:

Construct

Tool/Instrument

Teacher Morale

Standardized Teacher Morale Scale or Likert-type questionnaire

Pupil Achievement

Standardized test scores or cumulative academic performance records

Teacher–Administrator Relations

Structured interviews or climate inventory scale

Teacher Clique Formation

Social network analysis survey or faculty observation checklist

Nature of Board Decisions

Policy document analysis + expert coding rubric

7. Data Analysis Techniques:

·         Descriptive statistics: Mean, standard deviation

·         Inferential statistics:

o    t-test/ANOVA to compare outcomes across decision types,

o    Multiple regression analysis to determine the impact of Board decisions on dependent variables,

o    Chi-square tests (if categorical data are used)

8. Ethical Considerations:

·         Informed consent from participants (teachers, administrators).

·         Anonymity and confidentiality of school and personal data.

·         Approval from Institutional Review Board or equivalent.

9. Limitations:

·         No random assignment; potential confounding variables may affect internal validity.

·         Subjective interpretation of Board decisions could introduce bias.

·         Retrospective design relies on participant memory and documentation accuracy.

10. Potential Contributions:

·         Insights for Boards of Education on how their policy decisions impact real educational outcomes.

·         Recommendations for policy design that fosters positive school environments.

·         Basis for future quasi-experimental or longitudinal research.

Comments

Post a Comment

Popular posts from this blog

Two-Step System GMM (Generalized Method of Moments)

 The Two-Step System GMM is a dynamic panel data estimation technique designed to address: Endogeneity of explanatory variables Unobserved heterogeneity (fixed effects) Autocorrelation and heteroskedasticity It improves upon simpler estimators (like OLS or FE) and is particularly useful when the model includes lagged dependent variables . ⚙️ System GMM: The Basics Developed by Arellano & Bover (1995) and Blundell & Bond (1998) , System GMM combines: Difference GMM (Arellano & Bond, 1991): First-differences the model to remove fixed effects and uses lagged levels as instruments . Level GMM (Blundell & Bond) : Uses lagged differences as instruments for the level equation to improve efficiency. 🧮 The Dynamic Panel Model A typical dynamic panel data model is: y i t = α y i t − 1 + β X i t + μ i + ε i t y_{it} = \alpha y_{it-1} + \beta X_{it} + \mu_i + \varepsilon_{it} y i t ​ = α y i t − 1 ​ + β X i t ​ + μ i ​ + ε i t ​ Where: y...
Read more

Shodhganaga vs Shodhgangotri

  Shodhganga and Shodhgangotri are both initiatives by the INFLIBNET Centre (Information and Library Network Centre) in India. They aim to promote research and knowledge sharing in the academic community but serve different purposes. Here’s a comparison: Feature Shodhganga Shodhgangotri Purpose Repository for completed theses and dissertations. Repository for research proposals and synopses. Content Full-text doctoral theses and dissertations submitted by universities. Approved research proposals and synopses of doctoral students. Focus Stage of Research Final stage – after the research is completed. Initial stage – before the research begins. Target Audience Researchers looking for completed, peer-reviewed research work. Scholars looking for examples or ideas for research topics and methodologies. Utility ...
Read more

Panel Stationarity Tests: CADF and CIPS Explained

 When dealing with panel data , testing for stationarity (i.e., whether variables have a constant mean and variance over time) is crucial before estimation—especially in macroeconomic and financial studies. Two advanced second-generation panel unit root tests used to check for stationarity while accounting for cross-sectional dependence are: 🔹 1. CADF (Cross-sectionally Augmented Dickey-Fuller) Test 📌 Developed by: Pesaran (2007) CADF is an extension of the Augmented Dickey-Fuller (ADF) test that augments each ADF regression with cross-sectional averages to account for cross-sectional dependence . 🔍 Model Form: For each unit i i i , the CADF regression is: Δ y i t = a i + b i y i , t − 1 + c i y ˉ t − 1 + d i Δ y ˉ t + ∑ j = 1 p ϕ i j Δ y i , t − j + ε i t \Delta y_{it} = a_i + b_i y_{i,t-1} + c_i \bar{y}_{t-1} + d_i \Delta \bar{y}_{t} + \sum_{j=1}^{p} \phi_{ij} \Delta y_{i,t-j} + \varepsilon_{it} Δ y i t ​ = a i ​ + b i ​ y i , t − 1 ​ + c i ​ y ˉ ​ t − 1 ​ + d i ...
Read more