This part describes how we should deal with the data, which is quantitative or qualitative, collected from previous step. SPSS (The Statistical Package for the Social Sciences) is introduced to show how to analyze quantitative data. Before analyzing data each response for question in the questionnaire should be translated into appropriate code then enter these codes in the SPSS. If there is missing data ninety nine or -1 are frequently used instead 0. Data analysis is commonly divided into two broad types such as exploratory and confirmatory. While exploratory analysis explores the data, trying to find out what they tell you confirmatory analysis seeks to establish whether you have actually got what you expected to find. Particularly confirmatory data analysis (CDA) is the mainstream approach in statistical analysis. The main aspects of SPSS are follows.

 -       Significance test : Quantitative analysis is virtually synonymous with significance testing.   What a p value actually tells you is how likely it would be that you would get the difference you did. If the p value is small rather than large, this makes it less likely that the result is due to chance variation rather than to a real difference. In addition the chance of obtaining a statistically significant result increases as the sample size increases.

-       Measures of central tendency : The most commonly used are Mean, Median, and Mode.

-       Measures of variability : Some commonly used measures are Range, Inter-quartile range, Variance, Standard deviation, and Standard error.

-       Deviation : deviation = (individual score) – (mean score)

-       Standardized Score : standardized score = (individual score – mean score)/(standard deviation)

-       The normal distribution: The shape of normal distribution is completely determined once the mean and standard deviation are known. Here median and mode coincide with the mean.

-       Correlation coefficients : This gives an indication of both the strength and the direction of the relationship between the variables.

-       Multiple regression: This is multiple in the sense that it involves a single dependent variable and two or more independent variables.  The regression equation is Y= a + b1x1 + b2x2 where y is the dependent variable, x1 and x2 are the two independent variables, a is the intercept, and b1 and b2 the regression coefficients for the two independent variables.

-       R-squared : This is the multiple coefficient of determination, measure of the proportion of the variance in the dependent variable which is explained by the independent variables in the equation.

-       Factor analysis : This is an approach to making sense of a large number of correlations between variables.  

The author also explains how to analyze qualitative data in chapter 14. Tesch (1990) lists forty-six labels that qualitative researchers have used to describe their approach and reduces these to four basic groupings. These are the characteristics of language, the discovery of regularities, the comprehension of the meaning of text or action, and reflection. Crabtree and Miller (1992) produce a different typology, more closely linked to the method of data analysis used. These are quasi-statistical methods, template approaches, editing approaches, and immersion approaches. Author empathize that the central requirement in qualitative analysis is clear thinking on the part of the analyst. For the qualitative analysis NUD*IST is introduced and author describes how document system and index system can be used, how to make link the document and index system, and how to display data.