choosing appropriate statistics

8/3/2019 Choosing Appropriate Statistics

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Choosing Appropriate

Statistics

C. L. Varte

Department of Psychology,Mizoram University.


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What are the independent and

dependent variables? Identify the independent and dependent variables and

determine the scale of measurement.

Our dependent variable is always the phenomenon or

behavior that we want to explain or predict. Theindependent variable represents a predictor or causal

variable in the study.


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What is the scale of measurement

of the study variables?

* Nonparametric statisticsare used when our data aremeasured on a nominal orordinal scale ofmeasurement.

* Parametric statistics areused when our data aremeasured on approximatelyinterval, interval, or ratioscales of measurement.


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How many samples/groups are

in the design? With the scale of measurement of the dependent variable

identified:

1. To use Z test; t test; Pearson and Spearman

correlations; chi-square goodness-of-fit only one set or"sample" of data is applicable.

2. There must be at least two sets of scores or two

"samples" to examines differences between groupswith t test for dependent means; t test for independentmeans; one-way ANOVA; Friedman ANOVA; and chi-square test of independence.


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in the design?

With single samples and one dependent variable,the one-sample Z test, the one-sample t test, andthe chi-square goodness-of-fit test are the onlystatistics that can be used.

When we have a single sample and independentand dependent variables measured on all subjects,

we typically are testing a hypothesis about the

association between two variables we can use: chi-square test of independence, Spearman's andPearson's correlation coefficient, bivariateregression and multiple regression.


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in the design?

Repeated measurements or pairs of subjects typicallycollect at least two sets of scores:

1. Studies that are limited to two groups use either the

chi-square statistic, Mann-Whitney U, Wilcoxon T,independent means t test, or the dependent means t test.

2. With three or more groups in the design, the chi-square statistic, Kruskal-Wallis H Test, Friedman ANOVA

for ranks, One-way Between-Groups ANOVA, SimpleRepeated Measures ANOVA, Factorial ANOVA, andMixed Factorial ANOVA depending on the nature of therelationship between groups.


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in the design? Repeated measures, linked selection, or matching leads to

some type of association or link in the research designbetween sets of scores. We can use:

1. McNemar Test (two samples or times of

measurement),2. Wilcoxon t Test (two samples),

3. Dependent Means t Test (two samples),

4. Friedman ANOVA for Ranks (three or more samples),

5. Simple Repeated Measures ANOVA (three or moresamples), and 6. Mixed Factorial ANOVA (at leastone factor is linked/correlated).


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in the design? When there is no subject overlap across groups, we define

the groups as independent. Example genderdifferences.We can use:

1. Chi-square test of independence (two or moregroups),

2. Mann-Whitney U Test (two groups),

3. Independent Means t test (two groups),

4. One-Way Between-Groups ANOVA (three or moregroups), and

5. Factorial ANOVA (two or more independentvariables).


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in the design? When there is no subject overlap across groups, we define

the groups as independent. Example genderdifferences.We can use:

1. Chi-square test of independence (two or moregroups),

2. Mann-Whitney U Test (two groups),

3. Independent Means t test (two groups),

4. One-Way Between-Groups ANOVA (three or moregroups), and

5. Factorial ANOVA (two or more independentvariables).


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Have I met the assumptions of

the statistical test selected? If we violate test assumptions, the statistic chosen cannot

be applied.

In this circumstance we have two options:

1. We can use a data transformation.

2. We can choose a nonparametric statistic.


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the statistical test selected? All parametric tests assume that the populations

from which samples are drawn have specificcharacteristics and that samples are drawn undercertain conditions.

These characteristics and conditions are expressedin the assumptions of the tests.

Parametric tests make no distinction betweenapproximately interval, interval, or ratio data.

These are all considered "scale" data.


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the statistical test selected? Nonparametric tests make assumptions about

sampling (random) and the independence or

dependence of samples (varies by test) but make

no assumptions about the population.

Within nonparametric statistics, tests are further

divided into those that are appropriate for nominal

data and those that are applied to ordinal/rank

data.


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Nominal Data


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Ordinal Data


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Scale Data


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Summary

When choosing statistical tests we should

consider:

Scale of measurement

Number of samples/groups

Nature of the relationship between groups

Number of variables Assumptions of statistical tests

choosing appropriate statistics

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