Statistical Methods in Education Research

Grappling with selecting the most appropriate statistical methods? Fear not, for I bring you a comprehensive guide to help you navigate this intricate landscape. Below are a set of 4 tables that serve as a beacon for those seeking to unlock the secrets hidden within their data. Table 1: Parametric and Nonparametric Methods The table contrasts the parametric and nonparametric methods for various statistical analyses. From descriptive statistics to regression models, it covers a wide range of techniques, ensuring you have the right tools for the job, whether your data follows a normal distribution or not. Table 2: Comparing Proportions Dealing with categorical data? This table has got you covered. It outlines the statistical methods to compare proportions, unveiling the secrets of association between variables, changes in proportions, and comparisons between groups. Table 3 & 4: Semi-parametric and Non-parametric Methods From logistic regression to survival analysis, diagnostic accuracy, and agreement between diagnostic methods, it equips you with the knowledge to tackle even the most complex data scenarios. With these tables at your disposal, you'll be armed with the knowledge to make informed decisions, ensuring your statistical analyses are robust, reliable, and tailored to your specific research needs. The key to successful data analysis lies in understanding the assumptions and conditions of each statistical method. Embrace these tables as your guide, and let the power of statistical analysis illuminate your path to insightful discoveries. PS: What statistical methods have you found particularly valuable in your field or projects? Share in the comments below!

Not all tests are created equal The wrong one can distort your data, leading to: * flawed findings * endless reviewer questions * instant desk rejection Here’s a guide to common Statistical Tests: Uses, Assumptions, and Example Use Cases 1. t-Test: 🔹Use: Compare means of two groups 🔸Assumptions: Normally distributed data, equal variances ▪️Example Use Case: Compare test scores of two teaching methods 2. ANOVA (Analysis of Variance): 🔹Use: Compare means of more than two groups 🔸Assumptions: Normally distributed data, equal variances ▪️Example Use Case: Compare test scores among three teaching methods 3. Chi-Square Test: 🔹Use: Test independence between categorical variables 🔸Assumptions: Random sample, large enough sample size ▪️Example Use Case: Test if there's an association between gender and preference for tea or coffee 4. Pearson Correlation: 🔹Use: Measure linear association between two continuous variables 🔸Assumptions: Linearity, homoscedasticity ▪️Example Use Case: Examine the relationship between age and income 5. Regression Analysis 🔹Use: Predict a continuous dependent variable based on one or more independent variables 🔸Assumptions: Linearity, homoscedasticity, independence of errors ▪️Example Use Case: Predict house prices based on square footage, number of bedrooms, etc. 6. Mann–Whitney U Test 🔹Use: Compare distributions of two independent groups 🔸Assumptions: Independence of observations ▪️Example Use Case: Compare test scores between two different schools 7. Kruskal–Wallis Test 🔹Use: Compare distributions of more than two independent groups 🔸Assumptions: Independence of observations, similar distributions ▪️Example Use Case: Compare performance of different teaching methods across multiple schools 8. Wilcoxon Signed-Rank Test 🔹Use: Compare distributions of two related groups 🔸Assumptions: Dependent variable should be continuous or ordinal ▪️Example Use Case: Compare pre-test and post-test scores within a group 9. McNemar’s Test 🔹Use: Compare paired proportions or frequencies in a 2x2 contingency table 🔹Assumptions: Binary data, dependent groups ▪️Example Use Case: Compare the effectiveness of two treatments on a binary outcome 10. Fisher’s Exact Test 🔹Use: Compare proportions in a 2x2 contingency table 🔸Assumptions: Low sample size (fewer than 20), independence of observations ▪️Example Use Case: Assess the association between gender and smoking status in a small sample #PhD #Research ♻️ Find this useful? Like, Comment & Repost to help your network Follow Edidiong Ukpong(PhD Architecture) for more

In this article, I examine Regression Discontinuity Design (RDD), a powerful method for estimating causal effects, especially when randomization isn't possible. I break down how RDD works, with a practical example using a scholarship program. You'll learn how this method provides robust insights by comparing treatment and control groups at a cutoff point (think: eligibility thresholds). If you're looking to strengthen your causal inference toolkit, this is a must-read for both students and researchers alike! 💡 #CausalInference #DataScience #RDD #ResearchMethods #Education

*** The Statistical Method Most Underused *** ~ Multi-level (or Mixed-effects) Models are a regression model used to analyze data with hierarchical or nested structures. These models are beneficial when data points are not independent but are grouped at multiple levels, such as students within schools or repeated measurements within individuals. ~ Key Concepts: 1. Hierarchical Data: * Data can be organized into multiple levels or groups. For example, students can be nested within classrooms, and classrooms can be nested within schools. 2. Fixed Effects: * Fixed effects represent the average relationship between predictors and the outcome variable across all groups. These effects are constant across different levels of the hierarchy. 3. Random Effects: * Random effects account for the variability within groups. They allow the intercepts and slopes to vary between groups, capturing each group's unique characteristics. ~ Model Structure: See the post image. ~ Applications: 1. Education: * Modeling student performance with students nested within classrooms and classrooms nested within schools. 2. Healthcare: * Analyzing patient outcomes with repeated measurements nested within patients and patients nested within hospitals. 3. Social Sciences: * Investigating social behavior with individuals nested within communities. ~ Conclusion: Multi-level models are powerful tools for analyzing hierarchical data. They allow researchers to account for the complexity of nested structures and variability within groups. These models provide more accurate and nuanced insights into the relationships between predictors and outcomes by incorporating fixed and random effects. --- B. Noted