A summary of day 1 of the SPSS workshop : Padmini Krishna 12152

The first class of the SPSS workshop exposed us to the very basics of SPSS; its usage, origins and some of its applications.

Firstly, we were familiarized with the SPSS software in terms of its usage and learnt the terminology associated with it like the variables, labels, missing values etc.

We were then taken through an example which encapsulated the statistical concepts of frequency and cross tabulation.

Frequency:  is the number of occurrences of a particular event in an experiment.  Frequency analysis is thus a univariate method of analysis.

Cross tabulation: is a method of analysis which establishes an interdependent relationship between two tables of data. Since it involves two data sets, it is a bivariate method of analysis. The general procedure for running a cross tabulation is:

- Establish a null hypothesis- we begin by saying that “there is no relationship between X and Y”

- Run the cross tabulation analysis- analyse the relationship between X and Y

- Run the Chi-square analysis- establish if there is in fact a relationship between X and Y.

This method of analysis finds many applications in Market Research. For example, if a supermarket wants to understand the purchase pattern of a certain segment of its customers, it can cross tabulate the income levels of its customers against the frequency of purchase. 

Another example is that a clothing brand can cross tabulate the gender of the customers against the colours they pick. This will give the company an insight into how the colour preferences vary according to the gender.

We might be tempted to immediately draw conclusions based on the results of the cross tabulation, however, we were told to desist till we get the result of the Chi-Square analysis. The Chi-square analysis is a test of independence. It is that which ultimately proves the existence (or not) of a relationship between two variables. It is that which separates fact from conjecture.  The result which is in terms of a number up to two or three decimal places , signifies the level of confidence in the hypothesis. Thus a result of 0.05 signifies a 95% level of confidence. 0.05 is a popularly accepted benchmark although it can vary depending on the criticality of the decision. A result of less than 0.05 says that our null hypothesis is bunk or in more polite words, “wrong”. A result of over 0.05 allows us to pat ourselves on the back for our accurate hypothesis.  

Thus, we learnt that SPSS, a tool that was originally intended to be of use in the arena of social sciences is now an indispensible tool for market researchers, health researches and government agencies to name a few. When combined with pragmatic marketing sense, it can help glean meaningful insights into consumer behaviour which can translate into monetary benefits and growth of the company.