Data Processing and Analysis

• Data processing implies editing, coding, classification and tabulation of collected data so that they are amenabel to analysis (Kothari, 2004, p. 122).
• Analysis, particularly in case of survery or experimental data, involves estimating the values of unknown parameters of the population and testing of hypothesis for drawing inferences. If the analysis deals with a single variable then it is called descriptive analysis but if it deals with more than one variable then it is called inferential analysis. The latter is also known as statistical analysis.
• There are different types or elements of inferential data analysis. Normally, research have more than one variables to be analysed, the data of such research need multivariate analysis. Usually, the following analyses are involved when we make a reference of multivariate analysis: multiple regression analysis, multiple discriminant analysis, multivariate analysis of variance (multi ANOVA) and canonical analysis.
• Statistics has an important role in designing research, analysisng its data and drawing conlusions. There are two major areas in statistics: descriptive statistics and inferential statistics.
• Descriptive statistics concern the developmetn of certain idices from the raw data, whereas inferential statistics concern with the process of generalization. The latter is also known as sampling statistics and are mainly concerned with two types of problems: the estimation of population parameters and the testing of statistical hypotheses.
• The important statistical measures to summarize the survey/research data are: measures of central tendency or statistical averages, measures of dispersion, measures of asymmetry (skewness), measures of relationship and other measures.
• Mean is the simplest measurement of central tendency and is widely used measure (Kothari). Its chief use consists is summarizing the essential features of a series and in enabling data to be compared.
• Standard deviation is most wiely used measure of dispersion of a series. It defined as the square-root of the average of squares of deviations, when such deviations for the values of individual items in a series are obtained from the arithematic average.
• Skewness is an important tool to measure asymmetry of the data. It shows the manner in which the items are clustered around the average. A normal curve shows normal distribution of the data and it indicates there is no skewness. If the curve is distorted on the right side, we have positive skewness but when it is distorted on the left side it is called negative skewness.
• There are different methods of determining the relationship between two variables. Mainly there are two questions to be answered:

1. Does there exist association or correlation between the two or more variable? If yes, of what degree?
2. Is there any cause and effect relationship between the two variables? If yes, of what degree and in which direction?

• The first question is answered by the use of correlation technique and the second by regression technique.
• Karl Pearson's coefficient of correlation (or simple correlation) is the most widely used method of measuring the degree of relationship between two variables. The value of 'r' lies between +1 and -1 showing the perfect positive correlation and perfect negative correlation respectively.