Near East University Department of English Language Teaching Advanced Research Techniques Correlational Studies Abdalmonam H. Elkorbow

Topics to discuss -Definition, purposes, types of correlational study -Relationship study -Prediction study -The process -Correlation coefficient -Conducting of Relationship Studies -Conducting of prediction Studies - Independent and dependent variables

Definitions of correlational studies A correlational study is a type of study in which two (or more) are measured and compared in a large group of individuals. - The results of a correlational study allow us to determine whether or not the two variables “go together” — that is, to determine the degree to which they change together, on average. If two variables change together in the same direction, such as height and weight (taller people tend to be heavier, on average, and vice versa), we say that the variables are positively correlated. If two variables change together in the opposite direction, such as alcohol intake and driving ability (the more alcohol one drinks, the less one is able to drive well, on average, and vice versa), we say that the variables are negatively correlated. The major strength of correlational studies is that they allow us to quickly discover general relationships among variables (or, at least, more quickly than if we compared a large number of case studies).

purpose correlational studies : Two basic purposes 1- Help explain important human behaviors -(Relationship Studies) 2-Predict likely outcomes -(Prediction Studies)

Relationship STUDIES - Researchers often investigate a number of variables they believe are related to a more complex variable. - Unrelated variables dropped from further consideration - Most researchers most probably trying to gain some ideas about cause and effect -However it does not establish cause and effect

PREDICTION STUDIES -Predict a score on one variable if a score on the other variable is known -Determine the predictive validity of measuring instruments -Predictor Variable; variable that is used to make the prediction -Criterion Variable; variable about which the prediction is made

The process 1-select the problem - Variables to be correlated are selected on the basis of some rationale - Increases the ability to meaningfully interpret results - Inefficiency and difficulty interpreting the results from a shotgun approach 2- select participants and instrument - Participant and instrument selection * Minimum of 30 subjects * Instruments must be valid and reliable * Higher validity and reliability requires smaller samples * Lower validity and reliability requires larger samples 3- Design and procedures Collect data on two or more variables for each subject. two or more scores are obtained for each member of the sample, one score for each variable of interest, and the paired scores are then correlated …the result is expressed as a correlation coefficient.

The process 4- Data analysis Compute the appropriate correlation coefficient. …the two or more scores are obtained for each member of the sample, one score for each variable of interest, and the paired scores are then correlated …the correlation coefficient indicates the degree of relationship between the variables of interest.

correlation coefficients A correlation coefficient identifies the size and direction of a relationship - Size /Ranges from 0.00 – Directions *Positive or negative -Interpreting the size of correlations -General rule * Less than.35 is a low correlation * Between.36 and.65 is a moderate correlation *Above.66 is a high correlation

Correlational coefficient - Predictions * Between.60 and.70 are adequate for group predictions * Above.80 is adequate for individual predictions - Interpreting the size of correlations Criterion-related validity * Above.60 for affective scales is adequate * Above.80 for tests is minimally acceptable - Inter-rater reliability * Above.90 is very good * Between.80 and.89 is acceptable * Between.70 and.79 is minimally acceptable * Lower than.69 is problematic

Correlation coefficient Interpreting the direction of correlations Direction Positive High scores on the predictor are associated with high scores on the criterion Low scores on the predictor are associated with low scores on the criterion Negative High scores on the predictor are associated with low scores on the criterion Low scores on the predictor are associated with high scores on the criterion Positive or negative does not mean good or bad

Correlation coefficient - Interpreting the size and direction of correlations using the general rule * +.95 is a strong positive correlation * +.50 is a moderate positive correlation * +.20 is a low positive correlation * -.26 is a low negative correlation * -.49 is a moderate negative correlation * -.95 is a strong negative correlation

Conducting Relationship Studies - Identify a set of variables 1- Limit to those variables logically related to the criterion 2- Avoid the shotgun approach * Possibility of erroneous relationships * Issues related to determining statistical significance - Identify a population and select a sample - Identify appropriate instruments for measuring each variable - Collect data for each instrument from each subject - Compute the appropriate correlation coefficient

Conducting a predictions studies - Identify a set of variables *Limit to those variables logically related to the criterion - Identify a population and select a sample -Identify appropriate instruments for measuring each variable - Ensure appropriate levels of validity and reliability - Collect data for each instrument from each subject * Typically data is collected at different points in time - Compute the results

INDEPENDENT AND DEPENDENT VARIABLES: * As I said before A variable is an object, event, idea, feeling, time period, or any other type of category you are trying to measure. Independent variable: It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable. Other factors (such as what they eat, how much they go to school, how much television they watch) aren't going to change a person's age. In fact, when you are looking for some kind of relationship between variables you are trying to see if the independent variable causes some kind of change in the other variables, or dependent variables

INDEPENDENT AND DEPENDENT VARIABLES: * dependent variable: means something that depends on other factors. For example, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it. Usually when you are looking for a relationship between two things you are trying to find out what makes the dependent variable change the way it does.

INDEPENDENT AND DEPENDENT VARIABLES: Many people have trouble remembering which is the independent variable and which is the dependent variable. An easy way to remember is to insert the names of the two variables you are using in this sentence in they way that makes the most sense. Then you can figure out which is the independent variable and which is the dependent variable: (Independent variable) causes a change in (Dependent Variable) and it isn't possible that (Dependent Variable) could cause a change in (Independent Variable).

INDEPENDENT AND DEPENDENT VARIABLES: For example: (Time Spent Studying) causes a change in (Test Score) and it isn't possible that (Test Score) could cause a change in (Time Spent Studying).

References Kendall, M. G. (1955) "Rank Correlation Methods", Charles Griffin & Co Székely, G. J. Rizzo, M. L. and Bakirov, N. K. (2007). "Measuring and testing independence by correlation ofdistances“,. doi: / doi /