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Correlational Research Relationships are everywhere, but are they strong ones…?

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Presentation on theme: "Correlational Research Relationships are everywhere, but are they strong ones…?"— Presentation transcript:

1 Correlational Research Relationships are everywhere, but are they strong ones…?

2 Descriptive Stats Recap Two major aspects? Two major aspects? Outlier? Outlier? Concepts Concepts –Central tendency (mean, median, mode) –Variability (range, standard deviation) –Outlier –Skew

3 Abbreviations Mean:  Mean:  Median: Mdn Median: Mdn Standard Deviation: SD or σ Standard Deviation: SD or σ Variation: S 2 or σ 2 Variation: S 2 or σ 2

4 Name that Skew Where is the mean, median, mode? Where is the mean, median, mode?

5 Correlations Which of the following are related to one’s happiness? Which of the following are related to one’s happiness? –gender –money –marital status –religious involvement –relationships with family, friends –community involvement

6 Correlations Statistics consistently show that body weight and reading achievement are positively correlated. A principal worried about her school’s test scores decides to feed her students Twinkies and sodas to increase the scores. What do you think of her idea?

7 Correlational Design Determines whether and to what degree a relationship exists between two or more quantifiable variables. Determines whether and to what degree a relationship exists between two or more quantifiable variables.

8 Example of Correlation

9 Correlational Design The degree of the relationship is expressed as a coefficient of correlation The degree of the relationship is expressed as a coefficient of correlation Examples Examples –Relationship between math achievement and math attitude –Relationship between degree of a school’s racial diversity and student use of stereotypical language –Your topics?

10 Correlation coefficient… +1.00 strong negative strong positive 0.00 no relationship

11 Advantages of Correlational Design Analysis of relationships among a large number of variables in a single study Analysis of relationships among a large number of variables in a single study Information about the degree of the relationship between the variables being studied Information about the degree of the relationship between the variables being studied

12 Muris and Meesters Article What do all the negative correlations mean? What do all the negative correlations mean? The strongest relationship is found between which variables? The strongest relationship is found between which variables? What can be claimed? What can be claimed? What questions do you have? What questions do you have?

13 Thought Questions

14 Cautions A relationship between two variables does NOT mean one causes the other A relationship between two variables does NOT mean one causes the other Correlation ≠ Causation

15 Cautions Lack of variability in scores (e.g. everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationships Lack of variability in scores (e.g. everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationships Large sample sizes and/or using many variables can identify significant relationships for statistical reasons and not because the relationships really exist (Avoid shotgun approach) Large sample sizes and/or using many variables can identify significant relationships for statistical reasons and not because the relationships really exist (Avoid shotgun approach)

16 Correlational Designs Guidelines for interpreting the size of correlation coefficients Guidelines for interpreting the size of correlation coefficients –Much larger correlations are needed for predictions with individuals than with groups Crude group predictions can be made with correlations as low as.40 to.60 Crude group predictions can be made with correlations as low as.40 to.60 Predictions for individuals require correlations above.75 Predictions for individuals require correlations above.75

17 Correlational Designs Guidelines for interpreting the size of correlation coefficients Guidelines for interpreting the size of correlation coefficients –Exploratory studies Correlations of.25 to.40 indicate the need for further research Correlations of.25 to.40 indicate the need for further research Much higher correlations are needed to confirm or test hypotheses Much higher correlations are needed to confirm or test hypotheses

18 Think… If you were going to take your action research project, and create a correlational study, what would it look like? If you were going to take your action research project, and create a correlational study, what would it look like?


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