Bivariate Descriptive Analysis First step in analyzing your data Three components Cross-tabulations...
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Transcript of Bivariate Descriptive Analysis First step in analyzing your data Three components Cross-tabulations...
Bivariate Descriptive Analysis First step in analyzing your data Three components Cross-tabulations and frequency distributions Significance testing Correlations Initial look at how the data fits together and
the relationships between the data Always done before the regressions because
lends a framework for the analysis
Crosstabs
Have already done in lab What do they Mean? Measure of how two variables and therefore two
concept categories overlap/relate Example white race versus poverty income may be
14% African American versus poverty income may be
27% Thus fewer whites live in poverty than African
Americans BY THIS DATA
Data and Samples
Cannot assume that the data are representative
Need to be cautious about statements such as “this relates to that” in a certain way
Rates versus Raw numbers
Raw numbers do not reflect the relative strength of the relationships and should never be used in data explication
Rates are relative comparisons regardless of the numbers and better reflect relationships
General descriptives are valuable too Mean Median Mode Each for each variable—dependent and
independent General Idea of the distribution of each Variance and Standard deviation
Graphs
Helpful only if they show a clear delineation or difference in the data
Descriptives versus Regressions Descriptives are raw measures that do not
give precision Regressions give more precise relationships
between independent and dependent variables
Can show those relationships CONTROLLING for other variables
Descriptives have no such controls
Correlations
Measures of association Measures of the strength and direction of a
relationship between two variables or concepts statistically
Need to know what kind of variable you have Nominal, ordinal, scale
Nominal Correlations
Lambda 0 to 1 Zero means unrelated 1 means completely overlapping (the same) Usually in between Which variables? All the dichotomous
variables (ones and zeros) you just made If comparing two nominals use lambda DOES NO SHOW DIRECTION
Ordinals
Not applicable here Comparison of two ordinal variables Use Spearman’s Rho or Gamma Gamma ranges from -1 to +1 Shows direction and strength—larger number
(-/+) then stronger relationship
Interval/Scale Variables
Need Mean, Variance, Range and Standard deviation as frequency measures
PRE concept Association measures are r-squared and
Pearson’s r T-test and p value associations for
significance Very high r value means auto-correlation (the
variables measure the same thing)
Nominal and Ordinal
Use Chi-square If Scale or interval use T test for significance
testing These test difference and likeness If not significant (p>0.05) then the concepts
and the variables used are not arrayed differently and may not be significant to the regression
Chi-square
Lambda and Gamma issues Need for ordinal and Nominal variables If Lambda is zero (rare) the ONLY measure of
association and significance that is valuable Differences in Chi-square models will be discussed
with regression models Non-parametric measure of association and
difference—compares two variables with know frequencies of distribution –does the data relate or not together?
Significance Testing
Defined as the likelihood that a relationship between two variables in a sample exists in the population the sample is designed to represent
Inference is the Strength of that relationship Measures of Goodness of Fit—how do the
parameters chosen fit together (well or not well)
Making Sense of This
Frequencies—what does the data say is the distribution of EACH of your variables
Crossatbulations—what are the relationships between sets of two of your variables, how do they cross relate
Association tests—are the two variables related in some way
Significance tests—what is the strength of that two-way relationship