1.5 Choice of Statistical Techniques

As mentioned earlier, the choice of a statistical technique is a complex issue, which should not be reduced to a cookbook approach. With this caveat, we have suggested appropriate techniques for different data analysis situations in Tables 1 and 2. They are meant to be a framework, and are intended as general guidelines. They should not be applied rigidly in all situations. It would be desirable to try more than one way of analyzing the data, whenever possible.

Table 1: Appropriate techniques for problems without distinction between independent and dependent variables
No. of Variables		Measurement Level	Analysis Method
NON-METRIC
One		Nominal	Frequencies, Proportions
One		Ordinal	Median, Mode
One		Preferences	Rank Consensus among evaluators
METRIC
One	Interval or ratio scale		Mean, Median, Mode, Variance, Skewness, Kurtosis
NON-METRIC
Two	Dichotomous		Cross-tabulation Chi-square
Two	Nominal		Cross tabulation, Chi-square, Correspondence Analysis
Two	Ordinal		Kendall's Tau,Spearman's Rho, Gamma
METRIC
Two	Interval-scale		Scatter plot, Pearson's Correlation Coefficient
More than two	Interval-scale		Principal Components Analysis, Factor Analysis, Cluster Analysis Multidimensional Scaling

Table 2: Appropriate techniques for problems with distinction between independent and dependent variables
No. of Variables		Measurement Level		Analysis Method
Dependent	Independent	Dependent	Independent
One	One	Nominal	Nominal	Non-parametric tests, Chi-square
One	One	Nominal (dichotomous)	Nominal	Multiple Classification Analysis
One	One	Nominal	Nominal (Dichotomous)	Wilcoxon's two sample test, Chi-square, Kolmogorov-Smirnov Test
One	One	Interval-scale	Nominal (Dichotomous)	t-test, Analysis of Variance
One	One	Interval-scale	Interval-scale	Regression Analysis
One	One	Interval-scale	Nominal	Analysis of Variance
One	More	Nominal	Interval-scale	Discriminant Analysis
One	More	Interval-scale	Nominal	Analysis of Variance, Multiple Regression Analysis, Multiple Classification Analysis
One	More	Interval scale	Dummy	Analysis of Variance, Multiple Regression Analysis, Multiple Classification Analysis
One	More	Interval-scale	Interval-scale	Multiple Regression Analysis