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- 1. Correlational Research By Sheila Wilson, Bonnie Dompierre, and Darrell Lewis
- 2. What is Correlational Research? Correlational research involves collecting data relation exists between two or more variables. to determine whether, and to what degree a The degree of relation is expressed as a correlation coefficient. P. 216 Textbook These are not cause and effect relationships!
- 3. What is the process of Correlational Research? Low intelligence test score = Low grade point average. High intelligence test score. = High grade point average Example: Intelligence and academic achievement are related. Individuals with high scores on intelligence tests tend to have high grade point averages, while individuals with low scores on intelligence tests tend to have low grade point averages.
- 4. What is the purpose of correlational study? *It might be used in a relationship study to determine relations among variables, such as IQ and GPA; IQ and Weight; IQ and Errors (Textbook); or Living together and divorce rates; or Internet usage and depression. *It might be used in a prediction study to make predictions, using the results of the examples above to make predictions about GPA, Weight, Errors, Divorce rates, and Internet usage. Education Participants: A major complex variable, achievement, is investigated in correlational research!
- 5. Variables that are not highly related can be dropped. Variables that are highly related can be examined closer to determine the nature of the relations.
- 6. TABLE 8.1 • Hypothetical sets of data illustrating a strong positive relation between two variables, no relation, and a strong negative relation. Strong Position Strong Negative Relation No Relation Relation IQ GPA IQ Weight IQ Errors 1. Iggie 85 1.0 85 156 85 16 2. Hermie 90 1.2 90 140 90 10 3. Fifi 100 2.4 100 120 100 8 4. Teenie 110 2.2 110 116 110 5 5. Tiny 120 2.8 120 160 120 9 6. Tillie 130 3.4 130 110 130 3 7. Millie 135 3.2 135 140 135 2 8. Jane 140 3.8 140 166 140 1 Correlation r = + .95 r = + .13 r = -.89
- 7. A way to interpret correlation coefficients: Coefficient Relation Between Variables Between +0.35 and -0.35 Weak or none IQ:Weight = +0.13 Between +0.35 and +0.65 Moderate Between -0.35 and -0.65 Moderate Between +0.65 and 1.00 Strong IQ:GPA = +0.95 Between -1.00 and -0.65 Strong IG:Errors= -0.89
- 8. Common new researcher mistakes, and the shared variance is the variation of the variable.
- 9. Choose logical variables. Use theoretical basis.
- 10. PARTICIPANT AND INSTRUMENT SELECTION
- 11. To determine common variance, square the correlation coefficient: .80 = (0.80)2 or 0.64, or 64% common variance. 0.00, or [0.00]2 shows 0.0 or 00% common variance. 1.00 or [1.00]2 shows 1.00 or 100% common variance. A .50 means only a 25% common variance, where 75% of the variance is unexplained variance. Statistical significance is the probability that the results could have occurred due to chance. It’s about the math:
- 12. Types of Correlational Research: Relationship Study – A researcher attempts to gain insight into variables that are related to a complex variable. Academic achievement vs. Socioeconomic status Prediction Studies – A researcher uses two highly related variables to predict scores on other variables. High GPA and IQ as a predictor of success in college.
- 13. In a Relationship (Study) • Researchers gain insight to variables or factors that are related to a complex variable. • In Educational Research: a) academic achievement b) motivation c) self-concept
- 14. Data Collection First: Identify the variables to be related. Ex: Academic Achievement vs. Socioeconomic Status Next, identify appropriate population to sample.
- 15. Pearson r is the most common technique. Spearman rho is a rank correlation coefficient. Others: phi coefficient (gender based, political affiliation, smoking status, educational status), Kendall’s tau, Biserial, Point biserial, Tetrachonic, Intraclass, and Correlation ratio, or eta.
- 16. Correlational Techniques in Relationship Studies:
- 17. Prediction Studies and Data collection: Definition: An attempt to determine which number or variables are most highly related to criterion variable. Data collection requires participants to provide desired data and to be available to the researcher. Shrinkage occurs when the second predictor group has less accurate data than the first predictor group. Cross-validation should occur if one-of-a-kind circumstances in one group result.
- 18. Data Analysis and Interpretation of Prediction Studies Each predictor variable must correlate with the criterion variable. Two types of Prediction studies occur: single prediction, and multiple prediction. Single Variable prediction equation: Y= a + bX Prediction and Relationship studies are similar, in that they can be formulated for each number of subgroups or total groups.
- 19. FYI • Many sophisticated statistical analyses are based on correlation data: • Multiple Regression • Discriminate Function Analysis • Canonical Analysis • Path Analysis • Structural Equation Modeling, AKA LISREL • Factor Analysis
- 20. Houston, we have a problem… Problems in interpreting Correlational Coefficients: Proper correlation method calculation may not have been used. Relations cannot be found if reliabilities are low. Invalid variables produce meaningless results. The range of scores could be too narrow or too broad. Large samples may show correlations that are statistically significant but unimportant. goodmovieslist.com

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