Correlational research “is a research method that gives the researcher the opportunity to describe the relationship between two measured measure variables; whether two variables are correlated” (Sherri, 2011, p.148). It should be known that the two variables under discussion cannot be manipulated. As such, the researcher computes a correlation coefficient which documents the observed relationship in numerical terms (Cresewell, 2011). This enables the researcher to make predictions from one variable to another. If a researcher learns that the two variables are related, he or she can predict one variable to the other with a high degree of accuracy. As a non experimental study, correlational research has a number of advantages. First, it is particularly significant when it is impossible to conduct experimental research due to ethical or safety issues (Cresewell, 2011). Secondly, it is useful when an investigator is interested in measuring many variables with the aim of finding if those variables are correlated (Cresewell, 2011). The degree of relationship or correlation between variables differs in magnitude. While some variables can be strongly related, others may have a moderate relationship. In addition, some variables may not be related at all. Explanatory design and prediction design models are widely used in correlational research. According to Pandita (2012 p.1), “explanatory design examines the correlation of two or more variables while in predictive design, the capability of the prediction is the main aim of the research”. Generally, predictive design has two fundamental principles: predictor variable and the criterion variable. A predictor variable refers to a variable which the researcher uses to gauge the value of the other variable (criterion variable); criterion variable refers to the variable whose value is determined by a predictor variable. In this case, the investigation normally has two variables: predictor and criterion. Thus, the researcher uses the predictor variable to determine the value of the criterion variable. Explanatory design has a number of characteristics. First, its main aim is to elucidate the correlation between two or more variables (Rashid, 2012). Secondly, a correlational research that employs explanatory design requires that data collection be done at one time (Rashid, 2012). Thirdly, such a design concentrates on a single group (Rashid, 2012). Fourth, this approach calls for the computation of at least two scores (Rashid, 2012). Fifth, the researcher has to determine correlational statistical test strength and direction of correlation (Rashid, 2012). Sixth, this type of design demands that the researcher has to make conclusions basing on the statistics alone (Rashid, 2012). Explanatory design focuses on answering why two or more things are related. Social researchers argue that it is one thing to describe high unemployment rates in a given country, to examine trends over time and compare the current rates with other countries (Rashid, 2012). It is “quite a different thing however, to explain why there is a high unemployment rate in that particular country and why unemployment rates are significantly high in some countries and extremely low in other