MSc

The research entitled “Development of software for statistical methods in social science research” was conducted during the period 2020-22 in KAU, COA Vellayani. The objective of the study was to develop an open-source software for social science research with special focus on survey data analysis in agriculture. The methods covered included canonical correlation analysis (CCA), linear regression, binary logistic regression, chi-square test, index construction, test for scale reliability, and construction of one-way frequency tables for quantitative data.
Scales are measurement tools used by social scientists to measure phenomena of abstract nature. They are collections of questions, the responses for which are used to measure the construct under consideration. A developed scale should be tested for its reliability before it can be put to use. To facilitate this, an application was developed for testing the temporal stability (test-retest reliability) and internal consistency (Spearman-Brown prophecy formula and Cronbach’s alpha) which returns the test statistics and their significance.
Indices are constructed so as to condense complex multidimensional phenomena into a simpler form to make evaluation easier. Indicators are unidimensional data extracted from the sample. They are combined using various method to form indices. An application was developed for the construction of indices after standardizing the dimensions and aggregating with equal weights.
Frequency tables are very popular analysis tools for data collected from a sample survey. In one-way tables, respondents are classified into different categories based on different levels of a single factor. The percentages are also calculated for describing the nature of the sample. An application was developed that calculates and displays the frequencies and percentages based on a prescribed criterion for classification of quantitative data.
Chi-square statistics can be used for analyzing categorical data. The goodness of fit test employs the chi-square statistic to conclude if the observed frequencies are on par with theoretical frequencies whereas test for independence of attributes is employed to check whether two attributes are distributed independently of each other in a sample. An application was for conducting the two tests mentioned. After
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uploading the data in the prescribed format, the test statistic along with p-value is returned.
Regression involves studying the functional relationship between a single dependent variable and one or more independent variables. Two types of regression is considered based on the nature of the dependent variable, whether they are quantitative or qualitative. Separate applications are developed for both types. The regression model developed in either case is assessed based on the value of coefficients.
In the case of linear regression, the dependent variable is qualitative and the model is linear with respect to parameters. Ordinary least square method is used to estimate the parameters of the model and their significance is tested using t-test. The goodness of fit is assessed using R-squared and adjusted R-squared values which represent the proportion of the variance in y explained by x variables.
When the dependent variable is qualitative it is called logistic regression. When the regressand has only two possibilities it is called binary logistic regression. In this case also the coefficients of each independent variable and their significance in terms of p-values are provided.
Canonical correlation analysis (CCA) is a dimension reduction technique in which the relationship between two multivariate datasets can be summarized into a few significant dimensions. The datasets are linearly combined to obtain pairs of canonical variables. An application was developed for the CCA. The results include the correlation between the pairs of canonical variates called canonical correlations. There are as many correlations as there are number of variables in the smaller group. The significance of dimensions/ correlations can be assessed using Wilks’ lambda criteria and associated p-value. The application also returns raw and standardized coefficients of canonical variates in each dimension and also loadings and cross loadings.
The open-source language R and its associated integrated development environment RStudio was used along with the web development platform RShiny was used for the development of the application. The results were demonstrated using example datasets.

The software covers statistical methods that is frequently used in researches in social sciences.