Correlational Research: Types of Correlational Research Designs

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Correlational Research Types of Correlational Research Designs The first type of correlational design, explanatory design, is conducted when researchers want to explore “the extents to which two or more variables co-vary, that is, where changes in one variable are reflected in changes in the other” (Creswell, 2008, p. 358). When conducting an explanatory correlational study, researchers typically collect data at one time as their focus is not based on future or past performance of participants. Thus, when analyzing the findings of explanatory correlation research, researchers analyze participants as a single group rather than creating subcategories of participants. Finally, in this type of study researchers collect two scores from each participant as each score represents each variable being studied (Creswell, 2008). The second type of correlational design, prediction design, is used by researchers when the purpose of the study is to predict certain outcomes in one variable from another variable that serves as the predictor. Prediction designs involve two types of variables: a predictor variable and a criterion variable. While the predictor variable is utilized to make a forecast or prediction, the criterion variable is the anticipated outcome that is being predicted. Prediction studies can usually be identified rather easily by research consumers simply by taking note of the title of a published study as most published prediction studies include the word “prediction” in the article’s title. The time at which variables are measured also differs in prediction studies as the predictor variable is typically measured at one time while the criterion variable is usually measured at a later date. Prediction studies also include a forecast of anticipated future performance, as well as advanced statistical procedures including multiple regression. For further information about multiple regressions see (link to statistics portion of site) (Creswell, 2008). Characteristics of Correlational Research Any time a researcher has at least two scores, a graph called a scatterplot can be used to provide a visual representation of the data that has been collected. Each point on a scatterplot represents two scores provided by one person. Researchers must select the scores for one variable to be plotted on the x-axis (the horizontal axis of the graph) while scores for the second variable are plotted on the y-axis (the vertical axis of the graph). Scatterplots are vitally important to correlational research as they allow researchers, as well as research consumers, to determine the following by looking at patterns within the entire group of data points (Creswell, 2008; Lodico et al., 2006): • • • • • The form of the relationship The type of association The existence of extreme scores The direction of the relationship The degree of the relationship

Consider the following situation:

Mr. Thomas has noticed that it seems as though students who earn higher scores on their homework assignments typically also score higher on the Iowa Assessments. Mr. Thomas wonders if there is a relationship between the amount of time that the students spend on homework each night and their Iowa Assessment scores. Thus, Mr. Thomas asks his six grade students to report the amount of time (in minutes) that they spend each evening completing homework. Mr. Thomas then created the following table with each student’s name, Iowa Assessment National Standard Score and the amount of time each student reported spending on homework each night. Iowa Assessment National Standard Score 142 167 130 180 150 194 162 202 216 216 219 223 230 244 270 252 Average Time Spent on Homework Nightly 0 10 10 10 30 15 20 15 50 45 40 60 65 90 80 75

Student Matthew Jane Daniel Jose Armando Kelby Loren Samantha Andrew Brittney Kiedis Ethan Dakota Mia Damarcus Alejandro

Mr. Thomas then uses the above data to create a scatterplot, as Mr. Thomas realizes that scatterplots are necessary to...
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