social science methods fall 2010 soyoung jung kevin balster melvin hale

Social Science MethodsFall 2010

Soyoung JungKevin Balster

Melvin Hale


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 on the level of measurement used for each variable : the nominal, ordinal, and interval or ratio levels

Common Methods of Bivariate Analysis

Contingency Tables (Cross- Classification)• often used 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)


GenderBorrowing materials

Using Public Computers

Studying Other Total

Male 30 40 20 10100

(100%)

Female 35 30 30 5100

(100%)

Primary Reason for Library Use (n=200)

Example of a Contingency Table

Example of a Scatter plot graph


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)

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.


Associations in Bivariate analysis

Determine the existence of an associationwhen one changes as the other changes

Describing associations:

Strength strong – moderate - weak

Direction Positive : the variables change in the same directionNegative : the variables change in the opposite direction

Nature (Pattern) – Linear or Curvilinear


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

An LIS exampleAn 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.


Independent Variables: GenderAgeRelationship 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.


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.


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.


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.


Bivariate Analysis Bivariate Analysis SummarySummary

The analysis of empirical relationships among Pairs of Variables, an ObjectiveExplanation.


Two Types of Two Types of ExplanationsExplanations

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.


Two Types of Two Types of ExplanationsExplanations

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.


RelationshipsRelationships

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.


Pros and Cons Pros and Cons

Advantages:

Quantify results – Simplify Relationships – Make predictions

Disadvantages:

Over-simplify results – Identify spurious relationships (Type 1 or Type 2 errors)


Best Used for:Best Used for:

Exploratory Research & Less Complex Situations

Testing the Ice


Thank You


social science methods fall 2010 soyoung jung kevin balster melvin hale

Documents

numerical variables

bivariate hypothesis

null hypothesis h0

agegenderrelationship

alternate hypothesis

bivariate analysisdetermine

linear associations

changesdescribing associations