Bivariate Analysis

1. Bivariate Analysis Soyoung Jung Kevin Balster Melvin Hale Social Science Methods Fall 2010

2. Variables Bivariate analysis is the analysis of two variables; one independent and one dependent. Ex. The effect of library instruction on library use It is undertaken to see if there is any association between the independent and dependent variables. Selection of a method depends onthe level of measurement used for each variable : the nominal, ordinal, and interval or ratio levels Social Science Methods Fall 2010

3. Common Methods of Bivariate Analysis • Contingency Tables (Cross- Classification) • oftenused when both variables are nominal or ordinal (categorical variables) • do not require assumptions about the nature of the association • Correlation Coefficient • used for linear associations between two numerical variables • measures the strength and direction of the linear relationship • scatter-plot graph (scattergram) Social Science Methods Fall 2010

4. Example of a Contingency Table Example of a Scatter plot graph

5. Hypotheses Bivariate hypothesis – proposing a relationship between two phenomena. Null Hypothesis: H0 - No relationship between the two variables. : the values of the two variables are independent of one another, the correlation coefficient r = 0) Social Science Methods Fall 2010

6. Alternate Hypothesis: H1 – There is a relationship between the two variables. This hypothesis can only be accepted after the null hypothesis is rejected. The usual goal is to reject the null hypothesis (H0), to conclude that the variables are not independent of one another. Social Science Methods Fall 2010

7. Associations in Bivariate analysis Determine the existence of an association when one changes as the other changes Describing associations: Strength strong – moderate - weak Direction Positive : the variables change in the same direction Negative : the variables change in the opposite direction Nature (Pattern) – Linear or Curvilinear Social Science Methods Fall 2010

8. http://mste.illinois.edu/courses/ci330ms/youtsey/scatterinfo.htmlhttp://mste.illinois.edu/courses/ci330ms/youtsey/scatterinfo.html

9. An LIS example Health Information Ties: Preliminary Findings on the Health Information Seeking Behavior of an African-American Community Ophelia T. Morey The study was performed to see if there was a correlation between gender, age, or relationship strength and how people sought out health information. Social Science Methods Fall 2010

10. Independent Variables: Gender Age Relationship Strength Dependent Variable: Source of health information Note: While there are three different independent variables in the study, they were all analyzed independently of each other so the analyses were bivariate. Social Science Methods Fall 2010

11. Hypotheses: H0: Age/Gender/Relationship Strength have no affect on where individuals search for health information. H1: Age/Gender/Relationship Strength have an affect on where individuals search for health information. Social Science Methods Fall 2010

12. The following data were collected: Gender (nominal) Age Group (ordinal) Strength of relationship to information source (ordinal) Information source (nominal) All analyses were performed using a χ² test. Social Science Methods Fall 2010

13. Findings: There was no correlation found between gender and where respondents received their health information. A correlation was found between age and where respondents searched for health information. A correlation was found between the closeness of a relationship and where respondents got their information. Social Science Methods Fall 2010

14. Bivariate Analysis Summary The analysis of empirical relationships among Pairs of Variables, an Objective Explanation. Social Science Methods Fall 2010

15. Two Types of Explanations Idiographic Explanations: Multiple factors affect a specific outcome, with limited generalizability. I missed my flight. 1) Alarm clock failed, 2) Car was on “E”, 3) Had to park in the remote lot, 4) Everybody at security refused to go through the body scanner, 5) The flight was overbooked. Social Science Methods Fall 2010

16. Two Types of Explanations Nomothethic Explanations: A few causal factors impact a class of conditions or events, and can be explained with an economy of terms, usually applied as groups. • People who are usually Late for their appointments. • People who make Excuses. A Bivariate analysis is a nomothethic explanation. Social Science Methods Fall 2010

17. Relationships Causal: Changes in one variable (independent) affects the other variable (dependent). The cause takes place before the effect. This is called a correlation. Spurious: A statistical coincidence shown to be caused by a third variable. Social Science Methods Fall 2010

18. Pros and Cons Advantages: Quantify results – Simplify Relationships – Make predictions Disadvantages: Over-simplify results – Identify spurious relationships (Type 1 or Type 2 errors) Social Science Methods Fall 2010

19. Best Used for: Exploratory Research & Less Complex Situations Testing the Ice Social Science Methods Fall 2010

20. Thank You Social Science Methods Fall 2010