CORRELATIONAL RESEARCH

DefinitionCorrelational research assesses relationships among naturally occurring variables with the goal of identifying predictive relationships.Predictive relationships found in correlational research have implications for decisions such as:

ContinuedDiagnosing psychological disordersSelecting job applicantsAdmission into colleges and universitiesSuccess in collegeStudents who are likely to be at risk academically in the 1st year of college.

Nature of CorrelationsA Correlation exists when two different measures of the same people, events, or things vary together that is, when scores on one variable covary with scores on another variable.Example Smoking and lung diseaseThe more an individual smokes, the more likely of contracting lung disease.

ContinuedBased on this correlation we can make predictions about lung disease.If we know how long an individual has smoked, we can predict ( to some degree) his/her likelihood of developing lung disease.Thus, the nature of our predictions will depend on the direction and strength of correlation.

Correlation CoefficientA Correlation Coefficient expresses the relationship between two variables in terms of both the direction and the magnitude of that relationship.

Direction of a Correlation Coefficient The direction of CC can be:PositiveNegativeZero

Positive CorrelationA positive correlation indicates that as the values for one measure increase, the values for other measure also increase.

Positive CorrelationYLowHighLowHighGPASATX

Negative CorrelationIn a negative correlation, as the value of one measure increases, the value of the other measure decreases.

Negative CorrelationYLowHighLowHighAchievement testHrs of TV watchingX

Magnitude/Strength of Correlation CoefficientThe magnitude of CC can range in absolute values from 0.0 to 1.00The value of 0 indicates there is no correlation and there is no basis for making predictions.A value of +1.00 indicates a perfect positive correlation.A value of -1.00 indicates a perfect negative correlation. (In both cases we can make predictions with absolute confidence).The formula for calculating CC is the Pearson r.

Correlation and CausalityWhen two variables are related (correlated), we can make predictions for the variables; however, we cannot make inferences about the cause of the relationship.Correlation does not imply causation.

ContinuedFor example Correlation between being outgoing (socially active) and being satisfied with ones life.The causal relationship could go either way.It is impossible to determine the correct causal direction simply by knowing the correlation between the two variables.

Spurious RelationshipIt is possible for a third variable to exist in this correlation.Having more friends, leads people to be more outgoing and more satisfied with their lives.This third variable is called Spurious Relationship.This is not to say that the original positive correlation does not exist, it just means that there are other variables that may explain why the relationship exists.

Path AnalysisPath analysis is a statistical technique used to help researchers understand potential causes of correlational relationships.One of the technique involves the identification of mediator and moderator variables.

Mediator VariableIt is a variable that is used to explain the correlation between two variables.

Moderator VariableIt is a variable that affects the direction or strength of the correlation between two variables.Example Parental support and students adjustment to college.

Approach copingParentalsupportChanges inPsychologicaladjustmentPath bPath cPath a

Study on Parental Support and Psychological AdjustmentValentiner and others (1994) carried out a correlational study to examine the relationship between parental support and students adjustment to college.Page 131-134

Measures in Correlational ResearchQuestionnaires-is a powerful scientific instrument for measuring different variables. It should be well constructed.

ContinuedSelf-Report Scales-are commonly used to measure peoples judgments about items presented on the scale and to determine differences among people on some dimension presented on the scale.For example-life events and amount of stress

ContinuedRespondents may be asked to rate different life events according to how stressful they perceive the events to be.The researcher then may develop a list of life events that vary on the dimension of stressfulness.

ContinuedThis type of scale focuses on differences among the items on the scale.To measure individual differences, respondents may be asked to report how often during the past year they experienced different stressful life events listed on a scale.A total stress score can be obtained from each individual by summing responses to the items on the scale.

ContinuedThe area of psychological measurement concerned with scaling items or stimuli is known as psychophysics, and the area of measurement concerned with individual differences is referred to as psychometrics.

Individual Differences ScalesLikert Method-A number of items are written to reflect construct or domain of interest along a 5-point scale ranging from strongly agree to strongly disagree. Each individuals responses are summed to derive a total score.

ContinuedItems are then selected from the whole set of items to best represent the domain. Items are discarded if they do not discriminate individuals who are high on the dimension from individuals who are low based on their total score.Each selected item should correlate well with the total scale score.Once the best items are chosen, an individuals scale score is obtained by summing up his/her responses to these items. Checking for reliability is important.

2. Factor AnalysisIt is similar to Likert Method.It is designed to select the best items to represent the construct or domain of interest.An advantage is that a measure can be developed that assesses more than one construct.Questionnaires can be designed to represent each factor on the scale.