To understand when to use different statistical test for inferential data analysis.

Choosing a statistical test.....

There are several factors that must be considered before a statistical test can be chosen. Firstly the research design, aim and level of measurement must be identified.

STEP 1:

What is the Research Design?

Data can be either related or unrelated. Related data is produced from repeated measures and matched pairs designs. Unrelated data is produced from independent groups designs.

STEP 2:

What is the Research aim?

Is the aim of the research to investigate a significant difference or a significant association? If, for example, there are 2 groups of participants, each in a different condition of the independent variable (e.g. in an experiment), then the aim is to test for a significant difference. If the aim is to test for a correlation between two variables, then the aim is to test for a significant association.

**Inferential statistics doesn't have to be a mission.....**

Lesson outcomes:

Knowing types of data

Research design

Purpose of the analysis

Repeated measures

In a repeated measures design, each participant is assigned to more than one condition of the independent variable. The experimental groups consist of exactly the same participants repeating the same task but under a different condition.

Matched pairs

In a matched pairs design, there are two equal groups of participants and each participant is matched with a similar participant in the other group (e.g. age, IQ, occupation). Both groups then take part in different conditions of the independent variable, as with independent groups.

Independent groups

In an independent groups design, each participant is only assigned to one condition of the independent variable. There can be several groups of participants, but each group only takes part in one condition of the IV and does not repeat anything.

Data can be produced at nominal, ordinal and interval levels.

STEP 3:

What are the levels of Measurement?

Nominal data

is the most basic level of measurement. An example is a frequency count of a distinct category, such as the number of aggressive and non-aggressive acts in an observation.

Ordinal data

consists of a list of data that can be ranked in order, but not data that would fit to an interval scale. An example is the subjective rating of happiness (on a scale form 1 to 10) that participants may score themselves as on a questionnaire. A happiness rating of 10 is higher than 5, but it is not twice as happy as 5 or 5 times as happy as 2.

Interval data

is measured on a scale in which each interval is exactly the same size. Time is interval data because each second is the same duration, and 10 seconds are twice as long as 5 seconds.

Step 4:

Which test to choose!

Once the design, aim and level of measurement have been identified, the correct inferential test can be chosen.

Spearman’s rho is a test for significant association, and produces a correlation coefficient.

The level of measurement must be either ordinal or interval.

The research design can be either related or unrelated.

Spearman’s Rho

Wilcoxon Test

Wilcoxon is a test of significant difference for related data.

The research design must produce related data (e.g. repeated measures or matched pairs).

The level of measurement can be either ordinal or interval.

Mann-Whitney U is a test of significant difference for unrelated data.

The research design must produce unrelated data (e.g. independent measures).

The level of measurement can be either ordinal or interval.

Mann-Whitney U

Chi-square tests for difference when the data is nominal and unrelated.

The research design must produce unrelated data (e.g. independent measures).

The level of measurement must be nominal (e.g. categories).

Chi-Square

Correlation coefficients

The strength of a correlation is described as a correlation coefficient. Coefficients range from -1.0 to +1.0, with a coefficient of less than zero describing a negative correlation and a coefficient above zero describing a positive correlation. A good rule of thumb is to consider of 0.0 to 0.3 as weak, 0.3 to 0.7 as moderate, and above 0.7 as strong.