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The Purpose of Descriptive Statistics in Human Growth and Development Research

The Purpose of Descriptive Statistics in Human Growth and Development Research
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  • 0:06 Once Data Is Collected
  • 1:12 Categorical Measurements
  • 3:05 Continuous Measurements
  • 4:59 Lesson Summary
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Lesson Transcript
Instructor: Lisa Roundy

Lisa has taught at all levels from kindergarten to college and has a master's degree in human relations.

How do you make sense of all the data you obtain during research? Knowing the role of descriptive statistics will help you understand how raw data is converted to usable information.

What Happens Once Data Is Collected

For a recent research project, you obtained survey information from 2,345 households. You are very pleased with the large response, but you are now left with 2,345 questionnaires worth of information to make sense of. You are feeling a bit overwhelmed. What do you do with all of this information?

Like the pile of surveys in the example above, researchers are often left with a large amount of scored material that needs to be analyzed when a research project is over. This is the raw data. Obviously, there is a need to reduce the raw data to more manageable proportions. This is the job of descriptive statistics. Descriptive statistics are used to summarize raw data in an understandable and meaningful way.

In order to summarize this data, you must first determine what type of measurement you have. There are two basic types of measurement: categorical and continuous. The type of measurement you have will determine the type of descriptive statistics you will be using.

Categorical Measurements

A categorical measurement refers to variables that have levels that are mutually exclusive. You may also hear this called 'qualitative data.' In other words, the variables are assigned to one specific category or another specific category. No quantitative values or levels are assigned to a variable.

An example of a categorical measurement would be political affiliation. You would either be assigned as a democrat, republican or independent. Another simple example would be gender. You are either assigned male or female.

When summarizing categorical results, the most common method is to produce a contingency table. A contingency table is used to record the relationship between observed variables by listing observed frequencies. Let's look at an example of a contingency table below. The independent variable, or the aspect that is manipulated during research, is listed on the top. The dependent variable, or the aspect that is not manipulated during research, is listed at the side.

Example of a contingency table
contingency table

Our independent variable will be gender, so we will have two categories at the top: male and female. Our dependent variable is stopping at a stop sign, so we will have two categories at the side: stop and no stop. Now let's assume you watched the first 200 men and the first 200 women who approached a stop sign at an intersection. You track if a person is male or female and assign them to the category of stop or no stop.

You would then fill in your contingency table with the frequencies of occurrence for each possible combination. The resulting information enables you to tell at a quick glance that more females than males stopped at the stop sign and that the majority of both genders stopped at the stop sign.

Continuous Measurements

A continuous measurement refers to variables that can take on any value within a range of the measurement. You can also hear this called 'quantitative data.' In other words, the raw data from the research has been measured on a scale where it can be compared to other measurements. An example of a continuous measurement would be how long it takes to complete a task. Another simple example would be test scores.

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