- California State University - Long Beach
- Sociology 260
- MIDTERM #2
Last Modified: 2014-08-23
takes the distance from the data point to the line & squares it.
plots line with smallest #
measured -1 to 1 strength being absolute distance from 0 not taking into sign, sign indicates strength
if using 2 variables regression. will give same #
This is a fast and easy way to analyze and show data.
It does not include any tests of significance.
They can be as simple as 2X2, or much more complex, including control variables.
Another way of viewing bivariate data
Usually work for nominal or ordinal-type data
Allow for quantification of relationships
- The Analysis of 2 variables at a time
- major question - Why
- Primary Goal - Explanation
- Look for correlation, relationships & assoc (all same thing)
- cause variable
- X axis
- Effect Variable
- Y axis
- A measure of Assoc appropriate to nominal variables.
- Values (Strength) 0.00 to +1.00
1.00 = perfect assos
- Direction not applicable
- A measure of assoc for ordinal variables.
- Strength -1.00 to +1.00
+1/-1 = perfect assoc.
- Direction: if both increase positive. if one increases & one decreased negative assoc.
- making claims about the population based on testing the sample (Teacher def)
- making generalizations from samples to population (book def)
- Measures of Central Tendency
- Measures of Dispersion
- Measures of Association
- Likelihood that an observed relationship between variables in a sample represents something other than sampling error. Usually by p<.05
- 5% chance or less mistakenly rejecting the Null Hypothesis
- Mistakenly saying there is a relationship between variables when there isn't one
- Mistakenly accepting null Hypothesis.
- Saying there is a no relationship when really is one.
- Associaton (change in 1 causes change in other)
- Time Order (cause 1st, effect 2nd)
- Make sure not some other cause didn't acct for
- -.9 is stronger than +.1
- Use Absolute distance from 0.
- CAN NEVER PASS 1 (1.1 is impossible
.21 - .49 Moderate
.5 - .69 Strong
.7 - 1 Very Strong
Direction can only be interpreted if variables are ordinal. If both increase direct, if one increases & other decreases indirect.
- Phi - 2 dichotomous variables (nominal or ordinal).
- Lambda - Nominal Variables.
- Gamma - Ordinal Variables
- Pearsons r - 2 Interval/Ratio (scale) Variables
EX: Chi test for (name variables) give test result of 16.007 w/1 df which is significant to p<.001. Therefore I would reject the Null
EX: The value for Phi has meas of Assoc between (name of variables) is .091 this indicates a weak assoc.
Major Question - what
Primary Goal - Description
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