Part 1
Continuous Variables
Continuous variables can be measured along a continuum. The continuous variables in this study will be two. These include participant’s age and the development of compensatory abilities. Age will be assessed in years, by asking participants to indicate their age in years. The development of compensatory skills will be assessed from 1 to 10.
Categorical Variables
Categorical variables are discrete in nature may further be grouped into nominal, ordinal, or dichotomous. In the proposed study, the following will be the categorical variables:
Gender: This categorical variable will be considered as dichotomous in this study. Participants will be categorized as either male or female. That means that the variables will have two categories.
Type of social medium used. This is a nominal variable. The type of medium will be categorized as either technological communication or face-to-face communication.
Effect of compensatory abilities survival. This will be an ordinal variable. This variable will be used to assess the degree to which compensatory abilities promote survival.
Effectiveness of face-to-face communication. This will be an ordinal variable, measured on a seven-point Likert-type scale.
Effectiveness of technological communication. This will be an ordinal variable, measured on a seven-point Likert-type scale. The assessment will revolve around phone text messages, Facebook, e-mail, Twitter, Instagram, Myspace, Digg, YouTube, WhatsApp and Stumble Upon.
Most useful Thing Learned from SPSS
The most important thing that I learned is that in SPSS, frequencies might be used to obtain most of the descriptive information about the data. This descriptive information includes measures of central tendency (median, mode, and mean with standard deviation), measures of variability (range, minimum, and maximum), and measures of the shape of the distribution (kurtosis, skewness and standard error of skewness). In addition to learning the procedures to follow in SPSS to obtain frequencies, I am also able to interpret the measures of central tendency and variability, and understand what they say about the data.
What I did not Understand
The aspect that I did not understand relates to the measures of the shape of the distribution. I had difficulties understanding the difference between kurtosis and skewness. Although I understand that the two may be used to determine whether the distribution is normal or not, I do not understand how to use them. It is also not clear to me whether both kurtosis and skewness are used, or whether one of them can be used without the other. In addition, I do not understand how to interpret the numerical values for kurtosis and skewness. In other words, I am not sure when I can say the data is normally distributed or non-normally distributed, using the kurtosis and skewness values.
Part 2
Question 5
One of my colleagues indicated that they had difficulties understanding the term “kurtosis”. They stated that they had not heard of this term before. Since they had expressed the desire to understand this term, more information is provided here. Kurtosis is a measure of the “peakedness” or flatness of the data distribution (Illinois State University, n d.). In other words, kurtosis measures how heavy the tails of a data distribution are. If the value of kurtosis is close to 0, it means that the shape is almost normal. A positive kurtosis value suggests that that the distribution is flatter than normal while a negative kurtosis value suggests that the distribution is more peaked than normal. Extremely nonnormal distributions tend to have extremely positive or negative values. In a positive kurtosis, the tails are heavier than they would be in a normal distribution while in negative kurtosis, the tails are lighter than they would be in a normal distribution. The heaviness or how positive/negative kurtosis gives information about where values are located. For example, an extremely positive kurtosis is an indication that more values are concentrated in the tails than around the mean (Illinois State University, n d.).
Reference
Illinois State University. (n. d.). SPSS: Descriptive statistics. Retrieved from http://psychology.illinoisstate.edu/jccutti/138web/spss/spss3.html
