We meet again!!!! As mentioned earlier by Dr Noraziah, the quiz will be on the 6th March and follow by the joint-seminar on 13th March. Wow..what a news for all of us. So, this week, Dr taught us on research design. RESEARCH DESIGN.....it gives me idea on what design I want to use for my research. But until today I am still blur on what to do. Have the idea in my head but still don't know how to do it.For the next seminar, we have to work in group.
yea yea yea..group work.. Munirah, Nadia and I are in one group. Our group has chosen Correlational design as our topic for this coming seminar. Hmmmmmm... blur..n blurrrrr.....and the topic is on Social Networking, and the first thing on my mind is Facebook. hehehehe.. Munirah has come out with a great idea on our topic and we discussed the matter right after our class.Online discussion for sure...
Kuala Terengganu..here I come..home sweet home...missing everything at home..hehhehe
Something about correlational design from the net.
Correlational Research
by Janet Waters
Research Design
In general, a correlational study is a quantitative method of research in which you have 2 or more quantitative variables from the same group of subjects, & you are trying to determine if there is a relationship (or covariation) between the 2 variables (a similarity between them, not a difference between their means). Theoretically, any 2 quantitative variables can be correlated (for example, midterm scores & number of body piercings!) as long as you have scores on these variables from the same participants; however, it is probably a waste of time to collect & analyze data when there is little reason to think these two variables would be related to each other.
Try to have 30 or more participants; this is important to increase the validity of the research.
Your hypothesis might be that there is a positive correlation (for example, the number of hours of study & your midterm exam scores), or a negative correlation (for example, your levels of stress & your exam scores). A perfect correlation would be an r = +1.0 & -1.0, while no correlation would be r = 0. Perfect correlations would almost never occur; expect to see correlations much less than + or - 1.0. Although correlation can't prove a causal relationship, it can be used for prediction, to support a theory, to measure test-retest reliability, etc.
Data collection:
You may collect your data through testing (e.g. scores on a knowledge test (an exam or math test, etc.), or psychological tests, numerical responses on surveys & questionnaires, etc. Even archival data can be used (e.g. Kindergarten grades) as long as it is in a numerical form.
Data Analysis:
With the use of the Excel program, calculating correlations is probably the easiest data to analyze. In Excel, set up three columns: Subject #, Variable 1 (e.g. hours of study), & Variable 2 (e.g. exam scores). Then enter your data in these columns. Select a cell for the correlation to appear in & label it. Click "fx" on the toolbar at the top, then "statistical", then "Pearson". When asked, highlight in turn each of the two columns of data, click "Finish", & your correlation will appear. Charts in any statistics textbook can tell you if the correlation is significant, considering the number of participants.
You can also do graphs & scatter plots with Excel, if you would like to depict your data that way (See Chart wizard).
Presentation of your results in a Research Report:
Use the standard APA style lab report. In the Introduction, briefly review past research & theory in your topic question (e.g. summarize current research on stress & academic achievement). Use APA referencing style to cite your sources. Then in the Method section, present a general description of the group of participants (their number, mean age, gender, etc.) in the Participants section, any materials you may have used (e.g. tests, surveys, etc.) in the Materials section, & in the Procedure section, note that your general research strategy was a correlational study, & describe your methods of data collection (e.g. survey, test, etc.).
In the Results section of the report, present your correlation statistic in both a table & in words, & note whether or not it is significant. If you have more than 2 variables to correlate, present a correlational matrix, showing the correlation between each of the variables. In the following example, 4 variables were correlated in one study. The correlation between Exam scores & hours of study, for example, is r = +.67, p <.01. This indicates a significant positive relationship between the number of hours of study & subsequent exam scores.
| Hours of study | +.67* | - | - |
|---|---|---|---|
| Stress level | - .45* | -.10 | - |
| # of Piercings | -.15 | -.2 | +.18 |
| Exam Scores | Hrs of Study | Stress level |
* p < .01
In the Discussion section, relate your results to past or current research & theory you had cited & described in the Introduction. Do note the statistical significance of your findings, & limits to their generalizability. Remember that even if you did not obtain the significant differences you had hoped to, your results are still interesting, & must be explained, with reference to other research & theory.
© Janet Waters
Correlational Research
The basic question for descriptive research is - "What are the values of a number of variables for a given sample of subjects.
The basic research question for correlation research is - What is the relationship between two or more variables for a given set of subjects. Notice that we said relationship between variables and not the effect of one variable on another variable.
In descriptive research we are just describing our subjects in terms of one or more variables, while in correlational research we are looking at the relationship between the variables.
In future lessons we will look at research in which we are looking at the effect of one variable (the independent variable) on another variable (the dependent variable). This will be the case for causal-comparative reseaerch (lesson 12) and for experimental research (lesson 13).
The important thing to remember is that for correlational research we are just looking at the degree of relationship between the variables and not the effect of one variable on another variable.
An example of a correlational research study:
One important type of correlational research are studies conducted to provide information about the validity and reliability of tests.
Reliability studies are conducted to demonstrate the consistency with which tests perform their measurement function. In one type of reliability study the same group of subjects is given a test and then at a somewhat later date are given the test again. We thus have two scores for each subject (the test score and the retest score) and the correlation coefficient between the two sets of scores can be calculated.
this kind of correlation coefficient is referred to as a reliability coefficient. The reliability coefficient can also be calculated by using equivalent forms of the test. Many tests used in education, for example, standardized achievement tests, have more than one form. To determine the reliability coefficent, a group of subjects are given both forms of a test (e.g. Form A and form B) thus two scores are obtained for each subject and the correlation coefficient is calculated for the two sets of scores.
The first type of correlation study we referred to is a test-retest reliability study and the second is an equivalent forms reliability study.
To demonstrate the validity of a test we want to show that scores on the test correlate highly with some external measure of what the test purportedly measures. This external measure is referred to as the criterion. To conduct a validity correlational study. We obtain scores for students on some test and also record their scores on the criterion measure. Thus we have two scores for each subject and can calculate the correlation coefficient of the sets of scores. This correlation coefficient is referred to as a validity coefficient.
In the follow table are listed some example tests and criteria that might be used in validity studies.
| Test | Criterion |
| Reading Comprehension | Teachers Rating on Reading |
| Jiffy IQ Test | Wechsler Intelligence |
| Clinical Depression Scale | Psychiatrist's Rating on |
| Algebra Prediction Test | Final Grades for |
Note that in the first three examples the test and the criteria occurred at about the same time. This type of validity is referred to as concurrent validity. In the last example note that the criteria occurs at some time after the test. This is referred to as predictive validity. In this case we want to show the effectiveness of scores on the test in predicting the subjects' standing on some criteria. Some of the same consideratins for predictive validity studies are true for predition studies in general, which we will discuss later.
The Nature of Correlation
Correlational research studies almost always use the correlation coefficient to indicate the degree of relationship bwtween two variaables. The correlation coefficient is a number ranging from 1 (a perfect positive correlation) through 0 (no relationship between the variables) to -1 (a perfect negative correlation).
It is tempting to think of a correlation coefficient as indicating the proportaion of sameness between the two variables. But this is not true. A correlation of .90 does not mean that the two variables are 90% the same. In fact, the proportion of common variance (an indication of sameness) for a correlation of .90 is .81 or 81%. The proportion of commmon variance is the square of the correlation coefficient. (.90 x .90 = .8100).
If we have a correlation of .50 between two variables the proportion of common variance is only .25 or 25% (.50 x .50 = .2500). We could say that the two variables demonstrate 25% sameness but 75% (100 - 25) differentness (if there is such a word and we can use it in the present context).
The Design of Correlational Research Studies
Prediction Studies
In predictive correlational studies we are using the degree of relationship that exists between two variables to predict one variable from the other. For example if reading and spelling are correlated, then we can use the information to predict a student's score on the spelling test if the student has only taken the reading test. Converseley we could predict the student's score on the reading test given the student's score on the spelling test.
Prediction studies are widely used to predict student academic success in college based on such measures as high school grades, teacher grades, and apptitude test scores. In fact, many such criteria may be used in a multiple correlation prediction study.
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