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BIVARIATE

BIVARIATE. Glenda Gamboa Nicholas Gallagher Gina Hass Linda Isaac Sheila Purcell. Statistical Hypothesis Testing. Hypothesis tests are tools used to apply statistics to real life problems

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BIVARIATE

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  1. BIVARIATE Glenda Gamboa Nicholas Gallagher Gina Hass Linda Isaac Sheila Purcell

  2. Statistical Hypothesis Testing • Hypothesis tests are tools used to apply statistics to real life problems • They are based on contradictions, by forming a null hypothesis and then testing it with sample data.

  3. Statistical Hypothesis Testing NULL HYPOTHESIS (Hø): a plausible hypothesis, which may explain a given set of data, unless statistical evidence indicates otherwise (in which case, the null hypothesis is REJECTED and an Alternative Hypothesis (Ha) can be devised). If the null hypothesisexplains the data, it is ACCEPTED due to a lack of evidence, and no further tests are necessary.

  4. EXAMPLE • Hypothesis: • Children raised by parents with degrees are more likely to go to college • Independent Variable: Being raised by parents with degrees • Dependent Variable: Going to college

  5. ERRORS TYPE 1 ERRORS: reject the null hypothesis when it is really true. TYPE 2 ERRORS: fail to reject the null hypothesis when it is really false.

  6. MEASUREMENTS OF RELATIONSHIP Nominal: "involves naming or labeling...placing cases into categories and counting their frequency of occurence" (Levin & Fox 2004, 5) Ordinal: at this level, the researcher "seeks to order her/his cases in terms of the degree to which they have any given characteristic...but does not indicate the magnitude of difference between numbers" (Levin & Fox 2004, 5) Interval: "not only tells us about the ordering of categories but also indicates the exact distance between them" (Levin & Fox 2004, 5)

  7. ORGANIZING THE DATA IN GRAPHIC FORM: Pie Charts: "one of the simplest methods of graphical presentation. Pie charts are particularly useful for showing the differences in frequencies and precentages among categories of nominal-level variable." (Levin & Fox 2004, 38) Bar Graphs: "can accommodate any number of categories at any level of measurement." (Levin & Fox 2004, 38)

  8. More Graphic Presentations Frequency Polygon: "tends to stress continuity rather than differentness; therefore, it is particularly useful for depicting ordinal and interval data. This is because frequencies are indicated by a series of points placed over the score values or midpoints of each class interval...The height of each point or dot indicates frequency or percentage of occurrence." (Levin & Fox 2004, 40) Shape of Frequency Distribution: "Frequency polygons can help us visualize the variety of shapes and forms taken by frequency distributions." (Levin & Fox 2004, 41)

  9. Still not tired of graphic presentations? Kurtosis: "A shape characteristic of a frequency distribution that reflects the sharpness of the peak (for a unimodal distribution) and the shortness of the tails..."(Oxford English Dictionary) 

  10. Nominal Measures of Relationship Classifies objects into categories based on some characteristic of the object Gender Marital status Race College major Religious affiliation Categories are mutually exclusive The order is not important

  11. Nominal Measures ofRelationship The mode is the most appropriate measure to use. 1996 Party Identification Among Nonsouthern Whites (Hypothetical Data) ____________________________________________________ Party Identification f ____________________________________________________ Democrat 126 Independent 78 Republican 96 ___ Total: 300 (Frankfort-Nachmias and David Nachmias. 2000. Bivariate analysis. In Research Methods in the Social Sciences 351 - 384. New York: Worth. )

  12. Nominal Measures ofRelationship Chi-square test Fisher’s exact test Lambda (Guttman coefficient of predictability)

  13. Ordinal Measures of Relationship Objects represent the rank order Categories are mutually exclusive Categories have logical order

  14. Ordinal Measures of Relationship The central tendency of an ordinally measured variable can be represented by its mode or its median Sign Test Runs Test Gamma

  15. Interval Measures of RelationshipSpatial measurement which is used to show the distance between values. Dates and temperature (not Kelvin) are good examples of interval measurement. The difference between 30 and 40 degrees Fahrenheit is the same as the difference between 70 and 80 degrees. Distance between units matters most, but  because there is no natural zero one cannot say that 80 degrees is twice as hot as 40 degrees. Ratio measurement is like interval measurement but ratios rely a natural zero (i.e. weight, height, age...).

  16. Interval Measures of RelationshipSpatial measurement is good for determining correlation (linear dependence) without doing any calculations. Pearson's Product-Moment Correlation Coefficient = rWhen r = 1, there is a perfect positive relationshipWhen r = -1, there is a perfect negative relationshipWhen r = 0, there is no relationship

  17. Interval Measures of Relationship

  18. Interval Measures of RelationshipNumerical example of Pearson's Correlation here.

  19. LITERATURE REVIEW I couldn't find any peer reviewed articles using bivariate analysis for research in our field from the last 10 years!  Well, there was one but the Bivariate group from last year used it...  “Online Workplace  Training in Libraries"  By Connie K Haley

  20. Real Fast... • Studied people's preferences for online or in-person training in correlation with their demographic data, experience, and other variables in order to identify possible relationships.    • The methodology was quantitative using demographic characteristics and the Likert-scale assessment of training preferences; as well as qualitative using open-ended questions. • A summary of the deductive theories were that younger and or better educated/trained people would prefer online training. • The data did not support the original assumptions and only established a relationship between a preference for online training and the training providers as well as the training location.

  21. Highlighting the Bivariate Analysis!   Looking for statistically significant relationships between Variables and Preference for online training Insignificant relationship Significant relationships

  22. A Snapshot of Community-Based, Research InCanada: Who? What? Why? How? • Studied the context Community-Based Research (as opposed to "outside-expert driven research") in Canada by comparing the levels of involvement by organization type and other descriptive variables of participants. • A 25 question survey reviewed by the University of Toronto was produced and emailed to 2,000 appropriate potential participants with 308 returning completed surveys.  The data was analyzed using univariate and bivariate stats tests.     • Academic and Non-profit organization were most actively pursuing Community-Based Research with a high level of satisfaction; also impacting policy and programing on a noticeable level.

  23. Highlighting the Bivariate Analysis!

  24. Advantages of Bivariate Models Bivariate models are easy to create and interpret. It is convenient to quantify variables and have a mathematical expression for a relationship. They can provide a good starting-off point. For example, a bivariate model shows that taller people tend to make more money than shorter people. Now that a relationship has been defined, a study can be done to explain why this is true.

  25. Disadvantages ofBivariate Models They may be oversimplified and cannot always be taken at face value. An analysis of income vs. gender is informative, but the additional variable for race gives us a better picture. Men earn more than women, but white women earn more than black men.

  26. Even More Disadvantages ofBivariate Models Relationships may be indirect. People with historically African-American names tend to earn less than people with white names, but giving your child a white-sounding name will not necessarily make him more successful.

  27. Even More Disadvantages ofBivariate Models Correlation is not causation. If I have a rock and no tigers show up for a week, one should not conclude that my rock is a tiger repellent.

  28. REFERENCES Bartlett II, James E., Joe W. Kotrlik and Chadwick C. Higgins. Organizational research: Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal 19, no.1[Spring]: 43 - 50. Frankfort-Nachmias and David Nachmias. 2000. Bivariate analysis. In Research Methods in the Social Sciences 351 - 384. New York: Worth. Haley, Connie K. 2008. Online workplace training in libraries. Information Technology and Libraries 27, no.1[March]:33 - 40. Levin, Jack and James Alan Fox. 2004. Elementary statistics in social research. Boston: Allyn and Bacon. Oxford English Dictionary. http://dictionary.oed.com/

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