STATISTICAL TECHNIQUES FOR research ROMMEL S. DE GRACIA ROMMEL S. DE GRACIA SEPS for PLANNING & RESEARCH SEPS for PLANNING & RESEARCH

What options you have? BE LAME – “Usual Thinking Syndrome” – soothed with what you have and “think no more”; BE RIDICULOUS - Do things badly; or BE STRATEGIC – be an agent of positive change; Do the right things right.

Considerations for Choosing a Statistical Technique  THE RESEARCH QUESTION. What is it that we want to know? That is, do we want to test differences, to explore relationships between variables, or to simply describe our data?  THE NATURE AND LEVEL OF MEASUREMENT OF THE DATA COLLECTED. That is, is our data numerical or categorical? Is our data of the nominal, ordinal, or interval/ratio level?  NUMBER OF VARIABLES INVOLVED IN THE INVESTIGATION. That is, do we need to use

Scale of Measurement Nominal – qualitative and categorical (for identification) Ordinal – qualitative and ranked data – (converted numerically) Interval-Ratio – quantitative with meaning

Guide for Choosing Statistical Techniques  Univariate techniques are used to investigate one variable at a time.  Bivariate techniques are used to investigate two variables at time.  Multivariate techniques are used to analyze more than two variables simultaneously.

Some Examples: Example of Research Questions Independent Variable Dependent Variable Essential Feature Descriptive Statistics What is the compliance in the implementation of the Grade 2 English curriculum relative to a. Percent of LC’s implemented b. Number of teaching-learning delivered c.Percent of learning competencies mastered Mean % Median Standard Deviation Level/ Extent of Compliance a. % of LCs implemented. b. No of teaching- learning delivered c. % of learning competence Descriptive

Guide for Choosing Statistical Techniques  Techniques for Exploring Differences between Groups - Testing whether there is a significant difference among a number of groups. Most of these analyses involve comparing the mean/median scores of groups on one or more dependent variables.  Techniques for Exploring Relationships among Variables - Testing if the relationships (causal and non-causal) among variables are statistically significant. The following are considered: 1)relationship between two variables (e.g., Pearson r) and 2) causal relationships of variable/s on another variable (e.g., linear regression analysis).

A Comparison  Nonparametric tests are distribution free and do not require testing for assumptions.  When assumptions are met, parametric tests are more powerful.

YES NO YES

Some Examples: Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a change in the participant’s level of understanding action research before and after the seminar? Time (Before and After) Categorical IV: Two Levels Level of Understanding Same people/ participants on two different occasions Paired- Sample t-Test ( Dependent Samples) Wilcoxon Signed- Rank Test

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a significant difference in the Spelling Test Scores between males and females? Some Examples: GENDER (Male and Female) Categorical IV: Two Levels Spelling Test Scores Two Groups: Different people in each group T-Test for Independent Samples Mann Whitney test Wilcoxon Rank sum test

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a significant difference in the Spelling Test Scores for the three-different curricula (RBEC, STEM, SPA)? Some Examples:

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a significant difference in the Spelling Test Scores for the three-different curricula (RBEC, STEM, SPA)? Curricula (RBEC, STEM, SPA) Categorical IV: Three Levels/Groups Spelling Test Scores More than two groups: Different people in each group One-Way ANOVA using F-Test Kruskal Wallis ANOVA by ranks Some Examples:

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a significant difference in the performance in English 2 when grouped according to the strategies of teaching (traditional and non-traditional) used? Some Examples:

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a significant difference in the performance in English 2 when grouped according to the strategies of teaching (traditional and non-traditional) used? Strategies of teaching (Traditional and non- traditional) Categorical IV: Two levels Performance in English 2 Two groups of different samples T-Test for Independent Samples Mann Whitney test Wilcoxon Rank sum test Some Examples:

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a relationship between educational attainment and percent of LCs implemented?

Example of Research Questions Independent Variable Dependent Variable Essential Feature Parametric Statistics Non-Parametric (Alternative) Is there a relationship between educational attainment and percent of LCs implemented? Educational attainment Ordinal Data Percent of implemented LCs Ordinal data versus Interval Ratio Spearman- Rho Some Examples:

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