Pg (440-447 only)
Confidence intervals look like 78% (+ - 3%). The interval is 75% to 81%.
Wednesday, March 31, 2010
Monday, March 29, 2010
Non-parametric tests
Chi-square test of independence (two variables or groupings). The differences between groups are tested. You test the Ho and either reject or fail to reject Ho. No association between groups is called independence.
(SPSS, Analyze, Descriptive Statistics, Cross-tab, Statistics, Chi-square)
Chi-square test of goodness of fit (one variable or group). The differences between the group and your expectations (chance) are tested. You test the same way as the test of independence. Expectations of Ho are equal proporation across all choices (chance).
(SPSS, Analyze, Nonparametric Tests, Chi-square)
Chi-square test of significance of a proportion (compare 2 frequencies only like "yes" and "no"). Small case of goodness of fit test.
Chi-square homework set in blackboard.
Friday, March 26, 2010
Non-parametric tests (Chapter 13)
Chapter 13 (Pages 407-416, 431-432 only)
Parametric techniques when we have scaled data (interval or ratio) such as r, z, t, F.
Non-parametric techniques when we have non-scaled data (ranked or categorical) such as Chi-square or phi coefficient.
Chi-square test of independence (conditional probability):
This is the test of two variables (groups) that shows they are independent of each other.
Chi-square test of goodness-of-fit:
Compare proportions.
Parametric techniques when we have scaled data (interval or ratio) such as r, z, t, F.
Non-parametric techniques when we have non-scaled data (ranked or categorical) such as Chi-square or phi coefficient.
Chi-square test of independence (conditional probability):
This is the test of two variables (groups) that shows they are independent of each other.
Chi-square test of goodness-of-fit:
Compare proportions.
Monday, March 22, 2010
ANOVA and exam 2
Repeated measures output. If you get significance then do Post Hoc, which is a paired samples t-test with corrected p-value for multiple t-test. Bonforoni correction: 0.05 divided by the number of comparisons.
Between Groups:
Important output for Between Groups ANOVA. It follows the BG WG formulas.
Study guide in blackboard for exam 2.
Studying in this exam (7 kinds):
1-way ANOVA (2 kinds) - (1-way = 1 independent variable)
1. Between Groups (SPSS: Analyze, Compare Means, One-way ANOVA)
2. Repeated measures (SPSS: Analyze, General Linear Model, Repeated Measures)
T-test (3 kinds)
1. Single sample (SPSS: Analyze, Compare Means, One sample t-test)
2. Between Groups (SPSS: Analyze, Compare Means, Independent samples t-test)
3. Repeated measures (SPSS: Analyze, Compare Means, Paired-samples t-test)
Pearson r
Simple linear regression
Wednesday, March 17, 2010
ANOVA - Chapter 12
Don't read pg 368-373.
Things beyond this class:
Things beyond this class:
- Factorial ANOVA designs
- Dunns procedure
Announced that even though the course schedule has the 1st draft of full report due, the draft is now due Apr 5.
ANOVA accounts for the variation within groups (WG) and between groups (BG).
Optimal case is low variation within groups (WG) and large variation between groups (BG) because it is said that you have controlled extraneous variables and are measuring what you want to measure.
Total Variance (TV) = BG + WG
F statistic = BG/WG
A significant F statistic does not tell you which means are significantly different from which means. It only tells you that at least one mean is significantly different from another.
Monday, March 15, 2010
T-test and ANOVA
T-tests are designed for smaller samples.
Three ways to compare means of two groups (sample vs. population, sample vs. sample, pre vs. post)
pg 308-320 is not on the test. Scan it, but don't do z-test practice problems.
pg 339 #3 question (make sure you understand how to answer this.)
Data enter one column for age and one column for status (married or bachelor).
ANOVA
Chapter 12
T-test only accomodates comparing 2 means. ANOVA is used to compare more than 2 means. Multiple t-tests inflates the alpha level, so we use ANOVA, since a extreme sample would show up multiple times in the pairings. Sample 1 vs. Sample 2, Sample 1 vs. Sample 3, etc. If sample 1 is an outlier, each pairing might give you a type 1 error.
ANOVA is the workhorse of statistics...any research question you have, many would conform it to how to run an ANOVA. That is a little backwards, but it has been like that in the past.
We will study between-groups and repeated-measure ANOVAs.
ANOVA (analysis of variance)
When to use ANOVA:
1. More than two means compared
2. The group means should vary widely from grand mean (mean of means)
3. The groups' raw data does not vary widely. (Close clustering of scores)
Total variance = Variance + error or Between-groups variance + within-groups variance
Between-groups variance is the good stuff. (treatment + error)
Within-groups variance is the bad stuff (variance due to error only since each member of the group is exposed to the same treatment).
F statistic = BG/WG or (treatment + error)/error
F = 1 (means treatment had no effect)
Three ways to compare means of two groups (sample vs. population, sample vs. sample, pre vs. post)
pg 308-320 is not on the test. Scan it, but don't do z-test practice problems.
pg 339 #3 question (make sure you understand how to answer this.)
Data enter one column for age and one column for status (married or bachelor).
ANOVA
Chapter 12
T-test only accomodates comparing 2 means. ANOVA is used to compare more than 2 means. Multiple t-tests inflates the alpha level, so we use ANOVA, since a extreme sample would show up multiple times in the pairings. Sample 1 vs. Sample 2, Sample 1 vs. Sample 3, etc. If sample 1 is an outlier, each pairing might give you a type 1 error.
ANOVA is the workhorse of statistics...any research question you have, many would conform it to how to run an ANOVA. That is a little backwards, but it has been like that in the past.
We will study between-groups and repeated-measure ANOVAs.
ANOVA (analysis of variance)
When to use ANOVA:
1. More than two means compared
2. The group means should vary widely from grand mean (mean of means)
3. The groups' raw data does not vary widely. (Close clustering of scores)
Total variance = Variance + error or Between-groups variance + within-groups variance
Between-groups variance is the good stuff. (treatment + error)
Within-groups variance is the bad stuff (variance due to error only since each member of the group is exposed to the same treatment).
F statistic = BG/WG or (treatment + error)/error
F = 1 (means treatment had no effect)
Friday, March 12, 2010
Chapter 11
Analyze data from experiments, quasi-experiments, and comparing groups
T-test (used to compare groups) - replaces z-test
T-test of the means of groups. Use the t-test when the population standard deviation is unknown (sigma).
Sample compared to population:
T = sample mean - population mean divided by standard error
df = degrees of freedom, typically n-1
Sample compared to sample:
T = sample1 mean - sample2 mean divided by standard error
df = n1 +n2 - 2
Pre-post comparison: (repeat measures format)
Page 335 for formula
T-test (used to compare groups) - replaces z-test
T-test of the means of groups. Use the t-test when the population standard deviation is unknown (sigma).
Sample compared to population:
T = sample mean - population mean divided by standard error
df = degrees of freedom, typically n-1
Sample compared to sample:
T = sample1 mean - sample2 mean divided by standard error
df = n1 +n2 - 2
Pre-post comparison: (repeat measures format)
Page 335 for formula
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