A lot of research in statistics has taken place in

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A lot of research in statistics has taken place in the 20 th century and new research is still going on in different areas of application. Research in statistics has two dimensions: theoretical and applied. All the statistical methods used by a
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243 researcher have a strong mathematical basis. Using the foundations of probability theory, statistics has evolved as ‘a science in search of truth’. The problems posed by researchers in various branches of science like biology, agricultural research, clinical trials, anthropological studies as well as problems in economics leading to planning, forecasting, etc., have made theoretical statisticians work on suitable methods to handle quantitative data. This has given rise to a large number of analytical tools, which are popularly used in applied research. In a way, many theoretical results have also evolved out of a practical need to answer a researcher’s questions. A statistician is therefore like a server, examining, understanding, participating and serving all the statistical needs of researchers from different fields. However, the quantum of statistics in contemporary research still seems to be lagging behind. One reason for this appears to be the failure of the researcher in studying the problem from a quantitative angle. The gap between theory and application is still wide and should be reduced. The most powerful medium to bri dge this gap is a ‘computer software’. It is therefore high time for researchers to understand the computer-oriented aspects of handling statistical data and to perform routine investigations on their own. 24.3.2. COMMON STATISTICAL ISSUES IN RESEARCH There are different types of statistical issues faced by a researcher. One may broadly classify them into the following groups according to the stage of research: Level 1: Data collection and recording stage Sampling scheme of a survey Layout of an experimentq Data coding, scoring and recording Tabulation and presentation of data Handling missing data Level 2: Computing basic statistics Proportion and percentages Measures of location (averages), variation and shape Statistical charts Measures of consistency of data Frequency distributions and histograms Cross tabulation Level 3: Statistical tests of hypotheses Comparison of means of independent groups Comparison of means of paired values Comparison of proportions Comparison of variances Level 4: Associations and relationships Tests of independence between attributes (count data)
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244 Contingency and association measures Non-parametric methods Correlation and regression Level 5: Multivariate methods Factor analysis Cluster analysis Discriminant analysis Probit and logit analysis Path analysis Profile analysis Multivariate ANOVA Analysis of factorial experiments Each of the above aspects and tools requires a fundamental understanding of its statistical origin and purpose. In the following section, we examine some aspects
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