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 …show more content…
In addition, it conveys a story about the data presenting the number of cases and the associated percentages. Looking at the data it is easy to see how many cases are within a given rang (Field, 2013).
For example, the percent of data that is missing provide a visual therefore identifying that something is wrong in the data. However, looking at the data using boxplots we are able to identify various outliers. Therefore, signaling data randomly spread out from the target. It is important to identify the outliers; they have the potential to causes bias (Field, 2013) . For example, looking at the box plot it clear that the frequency is up to 75, but the outliers are displayed (beyond the range of data) 76 or more in this data signify a problem in the data (Triola, 2008) .
Descriptive Statistics Figure 2 Descriptive Statistics
Figure …show more content…
An analysis can assist in identifying a correlation or trends within customers buying habits. For example, a marketer may like to know how the organization can best meet customer demand as well as build on revenue. Therefore, investigating levels of elasticity and inelasticity or product decline as well as customer awareness will give information as to how to meet the challenge. The information can be gathered by survey when investigating the level of customer awareness concerning a product. In this analysis, the measure of scale will be multidimensional (Hayes & Cai, 2007; Triola, 2008).
However I will use an example of an analysis that seek to identify an association of ordinal data. In this analysis, the marketer would like to understand the activity levels concerning buying habits. The data used in the investigation is to be extracted from a population of male and female’s (Gender) participants within a targeted area. The ordinal data is cataloged and evaluated as to there likely hood of buying a certain product. Their response archived using various level of confidence e.g., Not Buy, Reasonable Chance, Definite Buy and
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 …show more content…
In addition, it conveys a story about the data presenting the number of cases and the associated percentages. Looking at the data it is easy to see how many cases are within a given rang (Field, 2013).
For example, the percent of data that is missing provide a visual therefore identifying that something is wrong in the data. However, looking at the data using boxplots we are able to identify various outliers. Therefore, signaling data randomly spread out from the target. It is important to identify the outliers; they have the potential to causes bias (Field, 2013) . For example, looking at the box plot it clear that the frequency is up to 75, but the outliers are displayed (beyond the range of data) 76 or more in this data signify a problem in the data (Triola, 2008) .
Descriptive Statistics Figure 2 Descriptive Statistics
Figure …show more content…
An analysis can assist in identifying a correlation or trends within customers buying habits. For example, a marketer may like to know how the organization can best meet customer demand as well as build on revenue. Therefore, investigating levels of elasticity and inelasticity or product decline as well as customer awareness will give information as to how to meet the challenge. The information can be gathered by survey when investigating the level of customer awareness concerning a product. In this analysis, the measure of scale will be multidimensional (Hayes & Cai, 2007; Triola, 2008).
However I will use an example of an analysis that seek to identify an association of ordinal data. In this analysis, the marketer would like to understand the activity levels concerning buying habits. The data used in the investigation is to be extracted from a population of male and female’s (Gender) participants within a targeted area. The ordinal data is cataloged and evaluated as to there likely hood of buying a certain product. Their response archived using various level of confidence e.g., Not Buy, Reasonable Chance, Definite Buy and