QUANTITATIVE RESEARCH

Flowchart nodes

• Measures of frequency

• Measures of central tendency

• MODE

• MEDIAN

• Is the score at the center of the distribution

• It is the mist frequent score obtained by a particullar group of learners

• MEAN

• It is the arithmetic average

• Measures of dispercion

• STANDARD DERIVATION

• It is a number that shows how scores are spread around the mean

• DESCRIPTIVE STATISTICS

• QUANTITATIVE RESEARCH

• NORMAL DISTRIBUTION

• Describes the clusterings of scores/ behaviours, also known as a bell curve

• STANDARDS SCORES

• There are times when we want to compare an individual's performance on different tests.

• PROBABILITY

• It provides confidence in the claims that are being made about the analysis of the data.

• INFERENTIAL STATISTICS

• the goal is to generalize beyond the results.

• Prerequisites

• two—standard error of the mean and standard error of the difference between sample means

• Degrees of Freedom

• is the number of scores that are not fixed.

• Critical Values

• This is the value that we can use as a confidence measure to determine whether our hypothesis can be substantiated.

• One-Tailed Versus Two-Tailed Hypotheses

• The former (those that predict a difference in one direction) are known as one-tailed hypotheses and require a different critical value than do the "neutral" or two-tailed hypotheses.

• Parametric Versus Nonparametric Statistics

• Parametric Statistics

• t-tests

• Analysis of Variance (ANOVA)

• Two-way ANOVA

• Analysis of Covariance (ANCOVA).

• Multivariate Analysis of Variance (MANOVA)

• Repeated Measures ANOVA

• Nonparametric Tests

• Chi Square (X2)

• Mann-Whitney U/Wilcoxon Rank Sums

• Kruskal-Wallis/Friedman

• STATISTICAL TABLES

• tables that can be consulted to determine if your test results are significant

• STRENGTH OF ASSOCIATION

• There are times when we might want to determine how much of the variation is actually due to the independent variable in question

• ETA2 AND OMEGA2

• goes beyond the fact that there is a significant difference and gives us an indication of how much of the variability is due to our independent variable

• EFFECT SIZE

• It is a measure that gives an indication of the strength of one's findings.

• META-ANALYSES

• To make a meaningful comparison, effect sizes become the main comparative tool.

• ORRELATION

• Correlational research attempts to determine the relationship between or among variables; it does not determine causation.