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Statistics for OT
Basic statistcal info needed for OT
| Question | Answer |
|---|---|
| Nominal (aka categorical) | Lowest level/scale of measurement -- naming level, no order |
| 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 |
| Interval | Level/scale of measurement that indicates how space is defined between data elements -- no true zero (e.g. heights of people) |
| Ratio | Level/scale of measurement that indicates how space is defined between data elements -- has a true zero (e.g. temperature) |
| 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) |
| 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 |
| Statistics come from where? | Samples |
| Parameters come from where? | Populations |
| Census | Data from every member of a population |
| Positive skew | Data is clustered close to the Y axis |
| Negative skew | Data is clustered away from the Y axis |
| Parametric | Bell curve -- 50 subjects or more |
| Non-parametric | no bell curve - non-normal data - samples less than 50 |
| Variability | Differences among scores -- aka 'spread' or 'dispersion' -- outliers are considered a weakness |
| Standard deviation | How much scores differ (vary) from the MEAN of the scores |
| What is the percentage of scores that fall in 1 SD? | 68% -- or about 2/3 |
| What is the percentage of scores that fall in 2 SD? | 95% |
| What is the percentage of scores that fall in 3 SD? | 99.7% |
| Type I Error | Rejecting null when it is true |
| Type II Error | Failing to reject the null when it is false |
| 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 |
| Ho | Null hypothesis symbol |
| Hi | Alternate hypothesis symbol |
| t-test statistic symbol | t |
| ANOVA statistic symbol | F |
| Pearson r statistic symbol | r |
| Linear regression equation | Y = a + bX |
| Chi Square statistic symbol | x2 (wiggly looking x) |
| Mann-Whitney U Test statistic symbol | U |
| Wilcoxon Signed-Ranks Test | T (italicized) |
| Kruskal-Wallis H Test | H (italicized) |
| 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 |
| When is it appropriate to use ANOVA? | Testing differences in 3 or more group means - null hypothesis: NO statistical difference between the group means |
| What is a one-way ANOVA? | Subjects are classified ONE way -- effect of ONE independent variable on ONE dependent variable |
| What is a two-way ANOVA? | Subjects are classified TWO ways -- effect of TWO independent variables on ONE dependent variable |
| 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 |
| 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 |
| Linear regression -- Y is what? | The score to be predicted |
| Linear regression -- a is what? | Intercept - point where straight line meets y-axis |
| Linear regression -- b is what? | Angle of the line |
| Linear regression -- X is what? | Score on the variable X -- the score we know |
| Linear regression - what is 'Line of Best Fit'? | The concentration of data points that yields a line |
| 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 |
| 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 |
| 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 |
Created by:
msmaus
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