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Elementary Quantitative Analysis II. A/S 305: Social Research Methods Sarah Goodrum, Ph.D. Elementary Quantitative Analysis II. Measures of Association Inferential Statistics. Measures of Association. tell us the strength and sometimes the direction of an association between 2 variables

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Elementary quantitative analysis ii

Elementary Quantitative Analysis II

A/S 305: Social Research Methods

Sarah Goodrum, Ph.D.

Elementary quantitative analysis ii1

Elementary Quantitative Analysis II

Measures of Association

Inferential Statistics

Measures of association
Measures of Association

  • tell us the strength and sometimes the direction of an association between 2 variables

    • remember . . .

Measures of association1
Measures of Association

  • Lambda– tells us the magnitude (but NOT the direction) of the relationship b/t the 2 variables; use when one or both variables are nominal.

  • Gamma– tells us the magnitude and the direction of the relationship b/t the 2 variables; use when variables are ordinal (or above)

Measures of association2
Measures of Association

  • Pearson’s r– reveals the magnitude and thedirection of the correlation coefficient (or relationship) b/t 2 interval or ratio level variables

    • to use Pearson’s r:

      • it must be assumed that the relationship is linear (i.e., increase in X -> increase in Y; increase in X -> decrease in Y)

      • need an adequate sample size (n>30)

Inferential statistics
Inferential Statistics

  • estimate how well your sample statistic reflects the population parameter

    • this is necessary when we have a sample and want to make generalizations to the population

Terms to know
Terms to Know

  • statistic– summary description of a given variable in the sample

  • parameter– summary description of a given variable in the population

  • normal distribution– the normal distribution is symmetric; this shape is produced by random sampling error

  • standard deviation- the square root of the variance; tells you how much dispersion (or spread) there is in the variable for your sample

  • confidence intervals – sets the upper and lower limits of your sample statistic; tells you how much confidence can be placed in the sample statistics

Terms to know con t
Terms to Know, Con’t

  • sample size– as sample size goes up, sampling error goes down

  • sampling error– degree of error to be expected in probability sampling; the larger the sampling error, the less representative the sample.

Types of inferential statistics 1 univariate
Types of Inferential Statistics: (1) Univariate

  • With univariate inferential statistics (e.g., confidence intervals, which are estimated using standard deviation) we move beyond describing the sample to making estimates (or inferences) about the larger population

  • Three things MUST be established:

    • Sample drawn from population of interest

    • Sample must be drawn randomly using simple random, systematic, or stratified random sampling

    • Inferential statistics tell us about sampling error NOT about non-sampling error (e.g., bad survey question; problematic interviewer)

Types of inferential statistics 2 bivariate tests of significance
Types of Inferential Statistics: (2) Bivariate: Tests of Significance

Tests of Significance- tell us the likelihood that the relationship observed b/t 2 variables in a sample can be attributed to sampling error only

  • Chi-square– tells us whether we can safely assume that there is a relationship b/t the values in the population; it does not measure the strength or the direction of the association b/t the 2 variables, but indicates whether the association is significant (or due to chance)