Exploratory Data Analysis Using the dataset Chamorro-Premuzic. sav, exploratory statistical analysis was carried out on the variables in the dataset. Scatter plots were formulated t give a clear visual view of the data for Extroversion and Agreeableness. Descriptive statistics were also formulated for the variables. 2. Decision about the missing data 3. Correlation A correlation analysis was carried out on the data for Extroversion and Agreeableness.
4. Regression. A regression analysis was carried out on the data for Extroversion and Agreeableness to examines whether or not one can predict if a student wants a lecturer to be extroverted using the student’s extroversion score. The analysis was two-tailed since the answer sought to determine if the student wants a lecturer to be extroverted or not, and hence any deviation from the answer either positively or negatively would lead to rejection of the null hypothesis. (Triola, 2009) 5. Multiple Regression
A multiple regression analysis was carried out to determine whether age, gender, and student’s extroversion can predict if a student wants the lecturer to be extroverted. (Lewicki, 2007) Part B. Applying Analytical Strategies to an Area of Research Interest 1. Research area of interest. The research was aimed at examining the relationship between extroversion and Agreeableness by lecturer and students on what to consider for extroversion. a. Pearson Correlation A parametric correlation analysis that seeks to identify any relationship between two variables. b.
Spearman’s Correlation This is a correlation analysis that is non-parametric and aimed at identifying any relationship between two variables. c. Partial Correlation vs. Semi-Partial Correlation Partial correlation analysis is an analysis that seeks to identify the degree of a relationship between two variables when controlling factors has been introduced in the analysis. Semi-partial correlation analysis is an analysis that seeks to control the effect of a third variable in multiple regression and then finding the relationship between the remaining two variables.
The method however retains the variation caused by the third variable that is removed from the equation. d. Simple Regression e. Multiple Regression f. Logistic Regression Logistic regression is a regression that seeks to identify the probability of occurrence of an event in a logit function. The regression model is used for binomial regression models. References Hill, T. and Lewicki, P. (2007). Stataistics Methods and Applications. London: Croom Helm. Triola F. (2009). Elementary Statistics (11th Edition). New York, ACM.