W3: RQ for Associations (Page 22-34) Flashcards
What is the margin of error in confidence intervals. Are they symmetrical?
Margins of Error:
Lengths from the sample estimates to the lower bound and to the upper bound.
They are asymmetric around sample estimates
What is a contingency table
A (two-way) contingency table contains frequency counts of how many people belong jointly to each category of one variable and each category of a second variable
What does a joint cell in a contingency table contain
Frequency count of all participants who belong to Variable A AND B (keyword: And)
What is the row and column variable on a contingency table called
Marginal Cells
What can help us understand a two-way table
Mosaic Plots
How do we analyse a contingency table
Chi-Squared Null Hypothesis Test (Bio Psych)
What is a Cramer’s V
Measure of strength of association in contingency table, where at least 1 variable has 3 + categories
What does the Cramer’s V measure and what does it not measure
Strength of association, not Direction (no intrinsic ordering to 3/more categories)
What is the value of Cramer’s V range
Between 0 to 1. Closer to 1 = Stronger Association
Is Cramer’s V a sample statistic or a population parameter
It can be both a sample statistic and a population parameter
Does Cramer’s V use words like significant? What are some issues raised in the lecture?
No use of words like significant
No use of words like no association
No P value (Cramar’s Value does not provide, CI is more informative)
What is an estimator
An estimator is the mathematical function applied to our sample scores to obtain an estimated value for a population parameter (Formula to get summary characteristic)
if an estimator calculates a sample statistic value, it is called ___
point estimator
if an estimator calculates a confidence interval, it is called ___
interval estimator.
How many estimators are there for the same population parameter
Can be multiple
How can estimators of a population parameter vary in 3 properties. FIRST WAY
- ) 95% UNBIASED INTERVAL ESTIMATOR captures the true population parameter value 95% of the time on average over the long run
- If it does not, it is biased. Biasness does not depend on sample size
How can estimators of a population parameter vary in 3 properties. SECOND WAY
2.) 95% CONSISTENT INTERVAL ESTIMATOR gets increasing closer to capturing the true population parameter value 95% of the time on average over the long run as sample size increases.
– If it does not, it is inconsistent. Consistency relates to sample size
How can estimators of a population parameter vary in 3 properties. THIRD WAY
3.) 95% interval estimator of a population is MORE EFFICIENT if it produces a more narrow confidence interval on average over the long run compared to some competing estimator
How can estimators of a population parameter vary in 3 properties. Summary
- ) Biasness
- ) Consistency
- ) Efficiency
what is the ideal interval estimator. What sometimes happens
Unbiased, Efficient, Consistent.
Sometimes, a consistent efficient estimator (maybe bias) > unbiased inefficient estimator
What are correlations and cramer’s V examples of
Effect Sizes
What are effect sies
Umbrella term used for any quantitative measure of strength of relationship between construct measures.
They are estimated by sample statistics and can be applied to population parameters. More useful when accompanied by confidence intervals.
When both variables in a contingency table contain 2 categories, what is often used to report effect size
Odds Ratio
What are odds ratio
Measure of strength of association between 2 variables both containing 2 categories.
Ratio of 2 sets of odds formed by considering odds of one category in a variable within each category of the other variable
What are odds
Probability of one event/category occuring relative to probability of it not occuring
What happens to odds ratio if category order on one variable was reversed
The value will just be a reciprocal
When we have an odds ratio <1, what do we usually do
- ) Reverse ordering of two categories in one of the variable in a contingency table
- ) Recalculate odds ratio in revised table and interpret the estimate which will >1
