SOC 232 Lecture Notes - Null Hypothesis, Statistical Significance, Standard Deviation

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Published on 13 Apr 2013
School
Department
Course
Professor
Soc 232
March 27- April 1
1
Measures of Dispersion
The amount of variation in a sample.
Range: Highest score minus lowest score.
Shows the influence of outliers.
Standard deviation: Measures the amount of variation around the mean.
Influenced by outliers.
Bivariate Analysis
Determines whether there is a relationship between two variables.
Note: determination of a relationship is not proof of causality.
Amount of Explained Variance
eta, Kendall’s tau-b, Spearman’s rho, Pearson’s r
Squaring shows how much the variation in one variable will explain variation in
the other variable.
Allows prediction of the second variable based on the score from the first.
Statistical Significance
Can a sample finding be used to estimate a characteristic of the whole
population?
Stated as a probability level:
The probability that the results are not due to chance.
A Null hypothesis tests the significance of the bivariate association.
e.g. It states that there is no relationship between two variables, or that two
populations do not differ on some characteristic.
To test for statistical significance:
Set up a null hypothesis.
Establish an acceptable level of significance.
It must be .05 or lower (≤ .05).
(The maximum acceptable in social research)
Determine the statistical significance of the findings.
Use a statistical test such as Chi Square
If the null is correct there is no relationship.
If the null is rejected and the statistical significance (p) of the findings are ≤ .05
there is indirect support for the research hypothesis.
It is unlikely that the results occurred by chance.
Analysis of Variance
Multivariate Analysis
Referred to as ‘elaboration’.
Examines the relationship between three or more variables.
Can be used to test for spuriousness.
Can be used to test for intervening variables
X Y (intervening variable?) Z
E.g. Income Physical Vibrancy Self-esteem
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Document Summary

Standard deviation: measures the amount of variation around the mean. Determines whether there is a relationship between two variables. Note: determination of a relationship is not proof of causality. Amount of explained variance eta, kendall"s tau-b, spearman"s rho, pearson"s r. Squaring shows how much the variation in one variable will explain variation in the other variable. Allows prediction of the second variable based on the score from the first. The probability that the results are not due to chance. A null hypothesis tests the significance of the bivariate association. e. g. it states that there is no relationship between two variables, or that two populations do not differ on some characteristic. It must be . 05 or lower ( . 05). (the maximum acceptable in social research) Use a statistical test such as chi square. If the null is correct there is no relationship.

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