Basic statistcal info needed for OT
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| Nominal (aka categorical) | Lowest level/scale of measurement -- naming level, no order
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| Ordinal | Level/scale of measurement where data is put into order, from high to low -- does not indicate how space is defined between data elements
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| Interval | Level/scale of measurement that indicates how space is defined between data elements -- no true zero (e.g. heights of people)
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| Ratio | Level/scale of measurement that indicates how space is defined between data elements -- has a true zero (e.g. temperature)
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| Descriptive statistics | Summarizes data - uses all 4 levels/scales of measurement - uses mean, median, mode to get average - can use % (e.g. how many units per 100 have a certain characteristic)
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| Inferential statistics | Only uses interval & ratio level/scale of measurement - tools to show how the confidence we have when generalizing from a sample to a population - allows us to test for statistical differences between groups
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| Statistics come from where? | Samples
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| Parameters come from where? | Populations
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| Census | Data from every member of a population
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| Positive skew | Data is clustered close to the Y axis
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| Negative skew | Data is clustered away from the Y axis
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| Parametric | Bell curve -- 50 subjects or more
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| Non-parametric | no bell curve - non-normal data - samples less than 50
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| Variability | Differences among scores -- aka 'spread' or 'dispersion' -- outliers are considered a weakness
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| Standard deviation | How much scores differ (vary) from the MEAN of the scores
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| What is the percentage of scores that fall in 1 SD? | 68% -- or about 2/3
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| What is the percentage of scores that fall in 2 SD? | 95%
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| What is the percentage of scores that fall in 3 SD? | 99.7%
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| Type I Error | Rejecting null when it is true
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| Type II Error | Failing to reject the null when it is false
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| Alpha | The probability that researchers will use to reject the null -- a .01 null is a higher level than a .05 null -- aka level of significance
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| Ho | Null hypothesis symbol
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| Hi | Alternate hypothesis symbol
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| t-test statistic symbol | t
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| ANOVA statistic symbol | F
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| Pearson r statistic symbol | r
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| Linear regression equation | Y = a + bX
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| Chi Square statistic symbol | x2 (wiggly looking x)
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| Mann-Whitney U Test statistic symbol | U
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| Wilcoxon Signed-Ranks Test | T (italicized)
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| Kruskal-Wallis H Test | H (italicized)
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| When is it appropriate to use a t-test? | Comparing 2 group means -- test of dependent (scores are related) or independent (scores have no relationship between groups -- null hypothesis: NO difference between the group means - non parametric equivalent is Wilcoxon Signed Ranks
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| When is it appropriate to use ANOVA? | Testing differences in 3 or more group means - null hypothesis: NO statistical difference between the group means
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| What is a one-way ANOVA? | Subjects are classified ONE way -- effect of ONE independent variable on ONE dependent variable
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| What is a two-way ANOVA? | Subjects are classified TWO ways -- effect of TWO independent variables on ONE dependent variable
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| When is it appropriate to use Pearson r? | Finding a relationship (correlation) between 2 variables and finding strength of the relationship -- closer to +1 or -1 == a stronger relationship
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| When is it appropriate to use linear regression? | When we find a relationship between 2 variables -- the linear regression equation can be used to predict future scores
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| Linear regression -- Y is what? | The score to be predicted
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| Linear regression -- a is what? | Intercept - point where straight line meets y-axis
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| Linear regression -- b is what? | Angle of the line
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| Linear regression -- X is what? | Score on the variable X -- the score we know
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| Linear regression - what is 'Line of Best Fit'? | The concentration of data points that yields a line
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| When is it appropriate to use Chi Square? | When we need to determine how the members of a population are distributed among 2 or more categories
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| One-way Chi Square? | Allows analysis of ONE categorical variable (such as modalities to treat RA) - 1x2, 1x3 - null hypothesis: there is no TRUE DIFFERENCE between expected and observed results
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| Two-way Chi Square? | Allow analysis of TWO categorical varaibles - 2x2, 2x3 - null hypothesis: There is no TRUE RELATIONSHIP between category 1 and category 2
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Created by:
msmaus
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