UGC NET Questions (Paper – 1)

Regression analysis models the relationship between a dependent variable and one or more independent variables to predict or explain outcomes. In simple regression, a straight line is fitted to data points, whereas multiple regression uses several predictors. Regression coefficients indicate the direction and magnitude of each predictor’s effect. Therefore, the technique used to predict a dependent variable from predictors is correctly termed regression analysis.

Option A:
Correlation measures the strength and direction of association between variables but does not provide an explicit predictive equation specifying expected values of the dependent variable given specific independent variable scores.

Option B:
Regression offers both prediction and partial explanation by quantifying how changes in predictor variables are associated with changes in the outcome, holding other variables constant. This predictive focus corresponds precisely to the stem, making this option correct.

Option C:
Factor analysis identifies underlying latent dimensions that account for correlations among observed variables and is not primarily used for predicting one variable from others.

Option D:
Cluster analysis groups cases into relatively homogeneous clusters based on similarity but does not predict a single dependent variable from predictors.

Multiple correlation refers to the degree of association between a single criterion variable and several predictor variables considered simultaneously. It is summarised by the multiple correlation coefficient R, which indicates how well the combined predictors explain variance in the criterion. This is different from simple correlation, which involves only two variables. Because the stem mentions correlation between one variable and a combination of two or more predictors, multiple correlation is the correct term.

Option A:
Partial correlation measures the relationship between two variables while statistically controlling for the effect of one or more additional variables. It does not directly express the combined predictive power of several variables on one criterion as multiple correlation does. Thus, partial is not the appropriate completion.

Option B:
Simple correlation, often denoted by r, captures the linear relationship between two variables at a time. It does not account for the simultaneous effects of multiple predictors on a single outcome. Since the stem explicitly refers to a combination of predictors, simple correlation is not correct.

Option C:
Multiple correlation summarises how well a set of predictors, such as intelligence, study hours and motivation, explain a criterion like academic achievement. A high R value indicates strong combined prediction. This conception aligns closely with the stem’s description, confirming multiple as the right answer.

Option D:
Serial correlation is often used to describe correlations between observations ordered in time, such as successive scores in a time series, and is not directly about combining predictors for a single criterion. Hence, serial is not suitable here.