descriptive statistics
To prepare for this Discussion:
- Review this week’s Learning Resources and the Descriptive Statistics media program.
- For additional support, review the Skill Builder: Visual Displays for Categorical Variables and the Skill Builder: Visual Displays for Continuous Variables, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.
- Review the Chapter 4 of the Wagner text and the examples in the SPSS software related to central tendency and variability.
- From the General Social Survey dataset found in this week’s Learning Resources, use the SPSS software and choose one continuous and one categorical variable Note: this dataset will be different from your Assignment dataset).
- As you review, consider the implications for positive social change based on the results of your data.
By Day 3
Post, present, and report a descriptive analysis for your variables, specifically noting the following:
For your continuous variable:
- Report the mean, median, and mode.
- What might be the better measure for central tendency? (i.e., mean, median, or mode) and why?
- Report the standard deviation.
- How variable are the data?
- How would you describe this data?
- What sort of research question would this variable help answer that might inform social change?
Post the following information for your categorical variable:
- A frequency distribution.
- An appropriate measure of variation.
- How variable are the data?
- How would you describe this data?
- What sort of research question would this variable help answer that might inform social change?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
In social scientific research, measurement is the process of assigning numbers or words to observations made in the real world. It is the foundation of the research process because it results in data that can be examined using mathematical operations. Because different things have different properties, what is being measured (what type of variable) determines how it is measured—or more specifically, the level at which it can be measured. That’s what the term ratio
interval ordinal nominal The level at which something will be or was measured affects the values recorded for the units of analysis and the mathematical operations that can be performed on the data collected: In general, the higher the level of measurement the less restricted researchers are in the types of mathematical operations they can perform on the data. Researchers, therefore, want to collect data at the highest level possible.
The graphic below shows the four levels of measurement in order from highest (least restricted/most informative) to lowest (most restricted/least informative).
A researcher asks students how they perceived their body weight. They might respond with overweight, underweight, or just about right, in which case each student is a unit of analysis, the answer options represent categories of responses, each answer option is a value, and all of the students’ responses comprises a data set. One of the first steps in analyzing a sample of data such as this one is to examine what is referred to as the distribution of values for the data set’s variables.
A researcher conducted a study in which she observed students’ scores on an examination. One of the first steps in analyzing a sample of data is to examine the distribution of values for variables in the data set. The distribution of the data tells her about the frequency with which various values are observed. Distributions can be examined in
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Identifying Categorical Variables
are those that have a small number of possible values. Usually, categorical variables involve or levels of measurement. For example, political party affiliation is an example of a nominal, categorical variable. This variable places individuals into one of just a few categories (e.g. Democrat, Republican, or Independent). An example of an ordinal, categorical variable is highest grade completed, with categories of less than high school, high school diploma, and more than high school. Again, this variable has just a small number of possible values. You will typically use categorical methods of displaying data, such as a bar chart or a pie chart, when the number of categories is less than 10 or 12. If there are too many categories, the displays become messy and difficult to read. Also keep in mind that pie charts and bar charts are not typically used for non-categorical variables. An example of a non-categorical variable would be students’ percentile ranking on a standardized math test; this variable has a large range of values and students aren’t simply placed into one of a limited number of categories