### 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