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67 Cards in this Set
- Front
- Back
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E.g. nominal scale
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sex
eye colour DSM diagnosis religion i.e. unordered categories |
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E.g. ordinal scale
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likert scale
i.e. categories and rank order |
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E.g. interval scale
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IQ scores
temperature i.e. rank order and equal intervals can add & subtract |
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What type of measurement?
1. religion 2. IQ score 3. DSM diagnosis 4. height 5. gender 6. likert scale 7. reaction time 8. frequency of agressive acts |
1. nominal
2. interval 3. DSM diagnosis 4. interval 5. nominal 6. ordinal 7. ratio 8. ratio |
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E.g. ratio scale
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# calories consumed
reaction time i.e. rank order, equal intervals AND absolute zero can multiply & divide |
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Types of measurement in increasing complexity
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N ominal
O rdinal I nterval R atio |
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Cumulative frequency
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total number observations that fall at or below each score
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Kurtosis
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the relative peakedness of a distribution
more peaked = leptokurtic more flat = platykurtic |
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Skewed distribution
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more than half observations fall on one side of distribution
postive = low score negative = high score |
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Measure of central tendancy
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mode (Mo)
median (Md) mean (M or X) |
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Relationship bet measures of central tendency in skewed distributions
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positively skewed d:
mean, greater than median, greater then mode negatively skewed d: mode, greater than median, greater than mean |
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Measures of variance
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range
variance standard deviation |
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Def: variance
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a measure of variability calculated by dividing the sum of squares
SS / n (population) SS / n-1 (sample) |
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Def: standard deviation
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square root of the variance
a measure of variability of scores around the mean SS / n - then take square root |
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Re: Inferential statistics,
What is a sample statistic used for |
to estimate a population parameter
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Sampling error
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random error responsible for diff bet sample values and population
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Sampling distribution of the mean
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distribution of means obtained if large number of equal-size random samples are drawn from same population
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3 predictions of Central Limit Theorem
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1. as sample size increases, sample distribution of mean approaches normal distribution
2. mean of sampling distribution of the mean = population mean 3. SD of sample distribution of the mean = population SD divided by square root of sample size |
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Def: Standard error of the mean
What does it measure |
SD of sampling distribution of the mean
variability due to effects of random error |
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What happens to standard error when
1. SD larger & sample size smaller 2. SD samller & sample size larger |
1. SE larger
2. SE smaller |
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2-tailed vs 1-tailed hypothesis
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2-tailed = nondirectional
1-tailed = directional |
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rejection / critical region
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region of unlikely values
lies in one or both tails of sampling distribution values occur as a result of sampling error |
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retention region
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region of likely values
lies in central portion of sampling distribution |
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What happens to hypotheses if sample statistic is in rejection region
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null hypothesis is rejected
alternate hypothesis is retained |
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What happens to hypotheses if sample statistic is in retention region
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null hypothesis is retained
alternate hypothesis is rejected |
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Size of rejection region defined by...
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alpha
level of significance note: alpha = 0.05 means 5% in rejection region |
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Type I error
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reject a true null hypothesis
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Type II error
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retain a false null hypothesis
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Statistical "confidence"
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certainty about the decision re: null hypothesis
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Statistical "power"
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ability to reject a false null hypothesis
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Parametric tests
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evaluate hypotheses about population means, variances etc.
e.g. t-test, ANOVA interval or ratio scale |
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non-parametric tests
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evaluate hypotheses about shape of distribution
e.g. Mann-Witney U test, Wilcoxon ordinal or nominal scale |
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degrees of freedom
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N-1 (t-test)
C-1 (chi-square) |
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What information do you use to select an inferential statistic?
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scale of measurement
dependent variable study design |
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What statistical test would you use for nominal data?
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single-sample Chi-square
multiple-sample Chi-square |
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What statistical test would you use for ordinal data?
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Mann-Witney U-test
Wilcoxon matched pairs test Kruskal-Wallis test |
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What are the non-parametric alternatives to:
1. independent t-test 2. correlated t-test 3. one-way ANOVA |
1. Mann-Witney
2. Wilcoxon 3. Kruskal-Wallis |
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What statistical tests would you use for interval and ration data?
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t-test
ANOVA |
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Name types of t-test
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simple sample
independent samples (between) correlated samples (within) |
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Name types of ANOVA
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one-way
factorial (2-way, 3-way) randomized block factorial ANCOVA repeated measures mixed (split-plot) MANOVA |
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When use one-way ANOVA vs. factorial ANOVA?
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one-way = 1 IV
factorial = 2+ IVs |
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What are the Post Hoc tests for ANOVA?
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Scheffe's S test
Tukey's HSD test Fisher's LSD test |
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Which Post Hoc test is least vulnerable to Type I Error, but more vulnerable to Type II error?
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Scheffe's
Tukey's |
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Which Post Hoc test is least vulnerable to Type II Error, but more vulnerable to Type I error?
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Fisher's
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The numerator of the f-ratio is a measure of variablity due to...?
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treatment effects & error
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In ANOVA, the "mean square within" provides info about:
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sampling fluctuations
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Why use one-way ANOVA instead of seperate t-tests?
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to reduce Type I error rate
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How do you calculate f-ratio?
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MSB/MSW
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How do you calculate MSB?
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SSB/df
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How do you calculate MSW?
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SSW/df
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Use: MANOVA
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1+ IV
2+ DV (interval/ratio) *helps increase statistical power by assessing effects of IV on all DVs |
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Ex: planned "a priori" analysis (4)
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Dunn-Bonferroni t
linear contrasts orthogonal comparisons trend analysis |
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Axis on scattergram
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X = IV = predictor
Y = DV = criterion |
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Which correlation coefficient is most commonly used with...
1. interval and ratio data 2. rank data 3. nominal data |
1. Pearson r (also Eta)
2. Spearman rank 3. Contingency (C) |
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How do you translate correlation coefficient score into something meaninful?
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calculate coefficient of determination to provide a measure of shared (explained) variability.
- square the coefficient e.g. if coefficient is .60, .60 x .60 = .36, .36 x 100 = 36%, therefore, 36% of scores on DV explained by IV... remaining 64% is unexplained variance. |
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Use: regression analysis
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to predict a score on a criterion (DV) based on person's obtained score on predictor (IV).
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How do you locate a regression line
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least squares criterion
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Use: multiple regression
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2+ continuous or discrete predictors
1 criterion |
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Ex: multiple regression
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1. simultaneous (simple)
2. stepwise 3. hierarchical |
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When use multiple regression instead of ANOVA?
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if groups are unequal in size
if IVs on a continuous scale |
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Use: canonical correlation
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[an extension of mult reg]
2+ continuous predictors 2+ continuous criterions |
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Ex: multivariate techniques (4)
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multiple regression
canonical correlation discriminant function analysis logistic regression |
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Ex: bivariate correlational techniques (2)
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scattergram
correlation coefficient |
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Ex: bivariate prediction (1)
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regression analysis
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Ex: multivariate techniques (2)
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path analysis
LISREL |
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Ex: correlation & prediction tehcniques (4 main)
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bivariate correlational techniques
bivariate prediction multivariate techniques: correlation & prediction multivariate techniques: causal modeling |
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Use: discriminant function analysis
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2+ continuous predictors
1 discrete criterion |
