# Case Study: Explore Correlation And Regression

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Explore Correlation and Regression
Using the explore function within SPSS gives a clearer view of how the data is represented. The explore function helps to assess the variables within the investigation by visually identifying various aspects of the data, therefor revealing the true nature of the data (Field, 2013). For example, the data in the Chamorro-Premuzic.sav file can be evaluated in a number of ways, for example analyzing the data based on the gender grouping allow one to visualize the data by manipulating the data using gender as a factor, then placing the remainder of the values as dependent variables. Evaluating the data in this way gives the investigator a better understanding of how the data is either normally distributed or perhaps skewed (Triola, 2008) .
As identified in the case summery below, this is just one table of information available from SPSS explorer function. As seen here it helps to identify number of valid or missing case data as well as the percent as it concern each case. In addition, it provides information such as
Addressing the situation, there are a couple of option one can use e.g., data removal or transform the data (Field, 2013). In the event you remove the data, it is simply extracting the data from the analysis. However, this should only be used upon verifying the data isn’t from the population in question (Field, 2013; Hayes & Cai, 2007; Triola, 2008).
Moreover, transforming the data maybe a more popular technique if the data is believed to be normally distributed (Field, 2013). In addition, changing the Z score may benefit the process more so if the sample is unrepresentative and present a level of biases (Field, 2013). Still precaution should be taken due to implications in the data that would supersede the benefits of traditional methods (Field, 2013).

Figure 7 Test of

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