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Journal of Vocational Rehabilitation 20 (2004) 143–150 143 IOS Press
Perspectives on Scientific Inquiry
Correlational designs in rehabilitation research
Shawn M. Fitzgerald∗, Phillip D. Rumrill, Jr. and Jason D. Schenker Kent State University, Department of Educational Foundations and Special Services, 413 White Hall, PO Box 5190, Kent, OH 44242-0001, USA Tel.: +1 330 672 00583; Fax: +1 330 672 2512; E-mail: smfitzge@kent.edu
Abstract. The article describes correlational research designs as a method for testing relationships between or among variables of interest in the lives of people with disabilities. The authors describe conceptual aspects of correlational research, discuss the methods by which researchers select variables for this type of inquiry, explain the primary purposes of correlational studies, and overview data analytic strategies. These discussions are illustrated with examples from the contemporary vocational rehabilitation literature.
Keywords: Correlational research, research design, data analysis
1. Introduction
Investigating relationships among variables in the lives of people with disabilities is one of the most ba- sic and important aspects of rehabilitation research [2]. In fact, gaining a deeper understanding of the connec- tions that exist among human phenomena is an abid- ing impetus for scientific inquiry in all of the social science disciplines, and that impetus transcends even the most polarized paradigmatic distinctions between various research methods (e.g., qualitative vs. quan- titative, descriptive vs. inferential, experimental vs. non-experimental).
Rather than attempting to infer causality by system- atically manipulating the independent variable (as is done in experimental research), correlational studies assess the strength of relationships as they occur or have occurred without experimental manipulation. Based on the observed relationships, statistical significance tests are then applied to determine the predictive or explanatory power of those relationships under study.
∗Corresponding author.
In this article, we describe issues related to using and interpreting data from correlational designs in contem- porary rehabilitation research. The purposes, assump- tions, and limitations that inhere to correlational re- search are presented, illustrated with examples from existing literature.
1.1. Purpose of correlational designs
Correlational designs are typically used by re- searchers for the purpose of exploring relationships among variables that are not manipulated or cannot be manipulated. For example, Boschen [5] used a cor- relational design to study the relationship between in- come and life satisfaction among people with disabili- ties. Capella [6] used a correlational design to study the relationships among age, case costs, and income within a sample of participants with visual impairments. Cor- relational designs were appropriate in these studies be- cause it is not possible to manipulate variables such as income, life satisfaction, age, and case costs. Although participants in these types of studies are assumed to possess the characteristics of interest prior to the study,
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144 S.M. Fitzgerald et al. / Correlational designs in rehabilitation research
Table 1 Typical data requirements for correlational designs and analysis
Subject Life-Satisfaction Income
1 85 45,000 2 66 32,000 3 42 48,000 4 78 42,000 5 25 22,000
and they are measured on those characteristics dur- ing the study, no attempt is made by the researcher to change them. In correlational research studies, it is important to note that researchers often use terms such as predictor and criterion instead of independent and dependent to discuss variables because this is the ap- propriate terminology to use when conducting studies that do not experimentally manipulate variables under investigation.
Because variables are not manipulated, causation is difficult to infer using correlational designs. Al- though variables may be chosen as predictors because they are theoretically expected to explain differences in the criterion variable, a significant statistical rela- tionship between these variables does not prove causal- ity. However, a statistically significant relationship be- tween variables is a precondition of causality. Research consumers may draw causal inferences based on the total evidence generated in a number of correlational studies. Theory-based hypotheses used in correlational studies propose the direction and/or temporal sequence of variables, which is another necessary but not suffi- cient precondition for establishing causality.
1.2. Interpreting relationships in correlational designs
To understand the nature of various relationships that could be examined in conducting correlational studies, consider the data presented in Table 1. Note that with correlational designs at least two points of data related to variables of interest must be collected for each in- dividual. In this example, every individual has pro- vided data on income level and life satisfaction. To understand how variables co-vary (i.e., are related) re- searchers use scatterplots, which require data from one variable to be plotted against data from another variable for each individual in the study. Scores for one variable are plotted on a horizontal axis, referred to as the x axis, and scores from the other variable are plotted on a vertical axis, called the y axis. To plot a data point on the scatterplot for an individual, a researcher would locate scores on each axis for each variable and then
Table 2 Guidelines for interpreting correlation coefficients
Range of values Interpretation
+0.75 to+1.00 Strong positive relationship +0.50 to+0.75 Moderate to strong positive relationship +0.25 to+0.50 Weak to moderate positive relationship
0.00 to+0.25 Zero to weak positive relationship 0.00 to−0.25 Zero to weak negative relationship
−0.25 to−0.50 Weak to moderate negative relationship −0.50 to−0.75 Moderate to strong negative relationship −0.75 to−1.00 Strong negative relationship
mark a spot on the graph where these two scores would meet. Figures 1, 2, and 3 present scatterplots of three types of relationships that might exist among variables. If there is a positive relationship among two variables, higher scores on one variable would tend to be associ- ated with higher scores on another variable. This type of relationship is illustrated in Fig. 1. If a negative re- lationship exists between two variables, higher scores on one variable would tend to be associated with lower scores on another variable. This type of relationship is illustrated in Fig. 2. If there is no relationship between variables a pattern of scores similar to those illustrated in Fig. 3 would be observed.
Scatterplots are not only useful for understanding the direction of a relationship between two variables; they are also useful for understanding the magnitude or strength of the relationship between two variables. To estimate the strength of a relationship, a researcher would consider the closeness of data points plotted on the scatterplot. Points that cluster closely together indicate strong relationships, such as those illustrated in Figs 1 and 2, whereas points that are not tightly clustered indicate weak or no relationships. Figure 3 presents data representing a weak relationship between two variables.
The calculations for determining correlational statis- tics result in both positive and negative values that range from −1 to +1. Negative values are associated with negative relationships between variables and positive values are associated with positive relationships. The closer the correlational statistic (also known as a coef- ficient) is to−1 or +1, the stronger the relationship. Correlational statistics close to 0 indicate weak rela- tionships. If there were no relationship at all between two variables, a value of 0 would be reported. Al- though there are no binding rules for determining what constitutes a strong, moderate, or weak relationship, Table 2 provides a guide for interpretating corelational statistics.
S.M. Fitzgerald et al. / Correlational designs in rehabilitation research 145
Variable A (X axis)
4.03.53.02.52.0
V ar
ia b
le B
( Y
a xi
s) 700
600
500
400
Fig. 1. Scatterplot of a positive relationship between two variables.
Variable A (X axis)
6050403020
V ar
ia b
le B
( Y
a xi
s)
10
8
6
4
2
0
Fig. 2. Scatterplot of a negative relationship between two variables.
1.3. Variables in correlational designs
Correlational designs are prevalent in the social sci- ences and rehabilitation research primarily because they can be used for any research study in which it is
not necessary (or possible) to manipulate the indepen- dent variable of interest. The versatility of this type of research design is borne in the multitude of correla- tional analyses that exist for investigating relationships between or among variables.
146 S.M. Fitzgerald et al. / Correlational designs in rehabilitation research
Variable A (X axis)
2.722.702.682.662.642.622.602.582.562.54
V ar
ia b
le B
( Y
a xi
s) 510
500
490
480
470
Fig. 3. Scatterplot of no relationship between two variables.
Table 3 A summary of the hierarchy of measurement scales used in the social sciences
Properties Scale Examples
One category is different from another Nominal Gender, race Categories are different and ranked in order Ordinal Supervisor rankings, letter grades Categories are different and ranked in order plus differences between points are equal
Interval Standardized tests
Categories are different, ranked in order, differ- ences between points are equal and a true zero
Ratio Height, weight
As with all statistical analyses, deciding on the ap- propriate correlational analysis is dependent on the measurement properties of the variables under consid- eration [2]. In general, measurement refers to the pro- cess…
