Practical research 2

2nd quarter

Descriptive Statistics

Describe and summarize data.

Inferential Statistics

Make inferences and draw conclusions about a population based on sample data.

Goal of Inferential Statistics

Generalize findings, make predictions, test hypotheses, evaluate relationships, and support decision-making.

Data Analysis

Analyzes and interprets the characteristics of a dataset.

Presentation of Data

Displaying analyzed data using figures, graphs, and tables.

Discussion of Data

Interpreting findings, relating to previous studies, and discussing implications.

Analysis of Data

Breaking down data to prove or disprove a theory or claim.

Interpretation of Data

Comparing and contrasting data results, noting unexpected findings.

Nominal Level

Data can only be categorized; labels or names with no numeric order.

Ordinal Level

can be categorized and ranked

Interval Level

ranked and evenly spaced

Ratio Level

can be categorized, ranked, evenly spaced, and has a true zero point.

Examples of Ratio Data

Hours studied, absences, test scores, weight, income.

Parametric Tests

Require assumptions about the population (normal distribution).

Example Tests for Parametric Tests

t-test, ANOVA, Pearson r.

Nonparametric Tests

Do not assume a normal distribution; for ordinal or nominal data.

When to Use Nonparametric Tests

When data are not normal, sample size is small, or data are ordinal/nominal.

Example Tests for Nonparametric Tests

Chi-square, Mann-Whitney.

Null Hypothesis (H₀)

States there is no significant difference.

Alternative Hypothesis (H₁)

States there is a significant difference.

T-test

Compares means of two groups to see if the difference is significant.

One-Sample T-test

Compares one group’s mean against a known value.

Independent Samples T-test

Compares means of two independent groups.

Paired Samples T-test

Compares two related measurements from the same participants.

ANOVA (Analysis of Variance)

Compares the means of three or more groups.

Factor (in ANOVA)

The independent variable being tested.

Levels (in ANOVA)

The categories or groups of the factor.

Dependent Variable (in ANOVA)

The outcome being measured.

F-ratio

Computed value used to check if group means differ.

Significant Difference

At least one group mean is different (p < 0.05).

One-way ANOVA

Compares 3 or more independent groups.

Chi-Square Test

Tests categorical data for differences between observed and expected frequencies.

Chi-square Goodness of Fit Test

Checks if observed distribution matches expectations.

Chi-square Test of Independence

Tests if two categorical variables are related.

Pearson r (Correlation)

Measures strength and direction of a linear relationship between two variables.

Pearson Product-Moment Correlation

Parametric version of Pearson r.

Spearman’s Rank Order Correlation

Measures relationship between two ranked variables.

Spearman’s rho

Nonparametric version of Pearson r.

Negative Correlation

As X increases, Y decreases.

Positive Correlation

As X increases, Y also increases.

Simple Linear Regression

Predicts the dependent variable (Y) using one independent variable (X).

Independent Variable (X)

Predictor or cause.

Dependent Variable (Y)

Outcome or result.

Mann-Whitney U-Test

Nonparametric alternative to independent t-test; compares two independent groups.

Wilcoxon Signed-Rank Test

Nonparametric alternative to paired t-test; compares two related groups.

Kruskal-Wallis H-Test

Nonparametric equivalent of one-way ANOVA for 3+ groups.

Kruskal-Wallis H

Same as Kruskal-Wallis H-Test; compares 3+ independent samples.

Graph

Visual representation showing relationships, comparisons, or distributions in data.

Internal Validity

Results are due to the variables tested, not other factors.

External Validity

Results can be generalized to other populations or settings.