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# Correlational research design

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A procedure in which subjects’ scores on two variables are simply measured, without manipulation of any variables, to determine whether there is a relationship.

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### Transcript of "Correlational research design"

1. 1. Correlational Design SGDP6013 Research Methods in EducationPrepared for: Prepared by:Dr. Ruzlan Md. Ali Abdullah Al-Mahmood (805016) Md. Abdur Rashid (805026) Md. Shamsul Islam (805028)
2. 2. Correlational Research Design Weighing One Variable Against Another Chapter 12
3. 3. Topics to Be Discussed Defines Correlational Research When we use Correlational Research Types of Correlational Design The key characteristics of Correlational Design The steps in conducting a Correlational Study How do we evaluate a Correlational Study 4
4. 4. Correlational Research A procedure in which subjects’ scores on two variables are simply measured, without manipulation of any variables, to determine whether there is a relationship Correlational research examines the relationship between two or more non manipulated variables. 5
5. 5. Correlational Research What is the relationship between: 1. Height and weight? 2. Birth order and years of education? 3. Cigarettes smoked per day and health care costs? 4. How close to the front you sit in a classroom and your grade in a class? 6
6. 6. What can correlational research tell us? Imagine that researchers find an association between sitting in the front of the classroom and receiving good grades You promptly move to the front of the classroom, and expect your grade will improve Don’t bet money on it… 7
7. 7. Correlation and Causality With correlational research designs, causality cannot be inferred Example: Researchers want to investigate the link between religious affiliation and alcohol consumption*  They measure the number of bars and churches in randomly-selected towns # of Bars # of Churches ?* Example by Professor Kristi Lemm of Western Washington University 8
8. 8. Pitfalls of correlational research designs The researchers find that towns with more bars also have more churches Therefore, religious persons tend to drink more, or perhaps alcohol consumption is a reason people attend church …what is wrong with these conclusions? 9
9. 9. We cannot infer causation! Larger towns tend to have more bars and more churches. Therefore, a third (and more likely) explanation: # of Bars Town Population # of Churches 10
10. 10. Correlational Research Operational Definition: A statistical analysis of covariant data to determine a pre-existing relationship. Researcher makes no attempt to manipulate an independent variable. Purpose: This research technique is used to relate two or more variables and allow predictions of outcomes based on causative relationships between the variables 11
11. 11. Correlational ResearchHistorical Perspective: Karl Pearson introduced modern correlation techniques in 1895 at a Royal Society meeting in London where her illustrated his statistical model using Darwin’s evolution and Galton’s heredity. Improvements were slow coming until the arrival of microcomputers when complex regressional analysis of multiple variables was possible 12
12. 12. Correlational ResearchExample Situation: We, as teachers, practice correlation research often in the forms of pre-tests, quizzes, dip-sticking, etc., where we correlate (based on years of experience) the outcome of these assessments with anticipated final test results. We will often modify our teaching in response to the data to modify the outcome. 13
13. 13. Correlational ResearchDesign Models (Types)Explanatory Design: Research looks for simple associations between variables and investigates the extent to which the variables are relatedPrediction Design: Research designed to identify variables that will positively predict outcomes 14
14. 14. Explanatory Design ModelKey Characteristics of ERD• Correlation of two or more variables• Data collected at one time• Single group• At least two scores recorded• Correlation Statistical Test- Strength and Direction of correlation determined• Researcher draws conclusions from statistics alone 15
15. 15. Prediction DesignKey Characteristics of PRD• Author states that prediction capability is the goal of the research• Use of predictor variable followed with a criterion variable• Author forecasts future performance 16
16. 16. Key Characteristics of CorrelationalDesign As suggested by the explanatory & prediction design, CR includes specific characteristics: Displays of scores (scatterplots & matrics) Associations between scores (direction, form, & strength) Multiple variable analysis (partial correlation & multiple regression) 17
17. 17. Primary Tools for Correlation Designs Mathematical Tools  Graphical Tools  Product-Moment  Scatter plots correlation coefficient  Correlation matrixes  Coefficient of  Simple graphical determination regressions  Spearman rho  Venn Diagrams  Phi-coefficient  Point-biserial correlation  Regression lines 18
18. 18. Graphical Tools: Scatter Plots Scatter plots plot two variables against one another to provide a visual picture of the relationship between the variables. (Warning-Connecting dots on a plot suggests control over the IV and defines a particular trend with outlying point being in error) PowerPoint presentation Of Scatter Plots 19
19. 19. Simple Scatter Plot: Direction of Association Hours of Internet Depression use scores per week from 15-45 50 Laura 17 30 - Chad 13 41 40 Patricia 5 18 Bill 9 20 Mary 5 25 30 M + Todd 15 44 Angela 7 20 20 David 6 30 10 - Maxine 2 17 M John 18 48 5 10 15 20 Mean Score 10 29.3 Hours of Internet Use X=I.V. 20
20. 20. Forms of AssociationA. Positive Linear (r=+.75) B. Negative Linear (r=-.68) C. No Correlation (r=.00) 21
21. 21. Forms of Association D. Curvilinear F. Curvilinear E. Curvilinear 22
22. 22. Correlation Matrix Correlation matrixes chart the entire variable set against itself and display the coefficients for each permutation of the matrix. In other words the variances themselves for every combination of variables. 23
23. 23. Correlation Matrix Degree of Association: Determined as a -1.0 to 0 to 1.0 value where as the value 0 shows that there exists no correlation and a value of -1.0 or 1.0 shows a 100% correlation 24
24. 24. Typical Correlation Matrix 1 2 3 4 5 6 1.School satisfaction - 2. Extra-curricular activities -.33** - 3. Friendship .24 -.03 - 4. Self-esteem -.15 .65** .24* - 5. Pride in school -.09 -.02 .49** .16 - 6. Self-awareness .29** -.02 .39** .03 .22 - 25
25. 25. Simple Graphical Regression Regression lines can be determined both mathematically or graphically and are and indication of the rate of change between two variables. This rate indicates the magnitude of effect one variable has upon another. 26
26. 26. Simple Graphical Regression Regression Line 50 41 40DepressionScores 30 Slope 20 10 Intercept 5 10 14 15 20 Hours of Internet Use Per Week 27
27. 27. Venn Diagrams and Multi-Variant SystemsCommon Variance vs. Partial Variance Sometimes it is difficult to see all the relationships in a system by just staring at raw data. Plotting a Venn Diagram allows one to graphically represent the intersection of and thus the variance between multiple variables. 28
28. 28. Venn Diagrams and Multi-Variant SystemsCommon Variance vs. Partial VarianceBivariate Correlation: Independent Variable Dependent Variable Time on Task Achievement r=.50 Time-on-Task Achievement r squared=(.50)2 Shared variance 29
29. 29. Venn Diagrams and Multi-Variant Systems Common Variance vs. Partial Variance Independent Dependent r=.50 Variable Variable r squared=(.50)2Time on Task Achievement Time-on-Task Achievement Motivation r squared = (.35)2 Partial Correlations: use to determine extent Motivation to which a mediating variable influences both independent and dependent variable 30
30. 30. Flow chart of a Correlational study Determine if a correlational study best address the research problem Identify the individual study Identify two or more measure for each individual in this study Collect data and monitor potential threats Analyze the data and represent the results Interpret the results 31
31. 31. Determine if a correlational study bestaddress the research problem A study related to requiring the identification of the direction and degree of association between two sets of scores It is useful for identifying the types of association Need to explain in complex relationships of multiple factors When researchers use research questions 32
32. 32. Identify the individual study Randomly select the individual/participants to generalize the result The group needs to be of adequate size for use of correlational statistics, such as N=30 33
33. 33. Identify two or more measure for eachindividual in this study Identify two or more characteristics which will be compared of a group, measures of variables in the research questions need to be identified Instrument that measure the variables need to be obtained Instruments should have proven validity and reliability 34
34. 34. Collect data and monitor potential threats To administer the instruments and collect at least two sets of data from each individuals Researchers will be overly assured about threats of collecting data sets 35
35. 35. Analyze the data and represent the resultsFor data analyzing: Pearson’s correlation coefficient Partial correlation coefficient Multiple regression coefficientTo represent result: correlational matrix of all variables as well as statistical table (for a regression study) reporting the R and R2 values and the beta weights for each variable 36
36. 36. Interpret the results In this step the results of this study are discussing the magnitude and the direction of correlation coefficient Interpretation will includes the impact of intervening variables in a correlational study Regression weight of variables in a regression analysis and developing a predictive equation for use in a predictive study 37
37. 37. How do we evaluate a correlational StudyTo evaluate correlational study we might follow the criteria given below: Adequacy of sampling for hypothesis testing Display the results in matrices and graphs Assessment of the magnitude of the relationship based on the coefficent of determination, P values, effect size 38
38. 38. How do we evaluate a correlational Study Form of relationships and appropriate statistics Identify predictor and criterion variables Predicted the direction of relationship among variables based on observed data Statistical procedures 39
39. 39. 40
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