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PTP 560 Research Methods Week 9 Thomas Ruediger, PT

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Calculate Sn, Sp, LRs

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Confidence Intervals with small samples What is small sample size? Less than 30 (one of those special numbers in stats) Sampling distribution tends to spread out Standard normal curve not adequate Use the t-distributions Theoretical sampling distributions Flatter peak, wider at the tails Approaches normal curve as sample size increases Use values of t instead of z Described by degrees of freedom (n-1 for confidence intervals)

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Hypothesis testing Are the differences – Representative of “real” effects? – Just by chance? Null hypothesis ( H O ) – Means are not different – Stated in terms of population parameter – μ A =μ B Alternative hypothesis (H 1 ) – Difference too large to be by chance – μ A ≠μ B – May be directional or non-directional

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Truth Decision H o is True Do Not Reject H o α Correct β Reject H o H o is False Correct Type I error Type II error

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Type I Error Significance Level (Alpha level, α level) Your choice of how much risk you are willing to take of saying there is a difference when there really is no difference Set this before the study – Conventionally is 0.05 This is arbitrary, but almost always what we choose – Choose the level based on the Type I error concern

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Type I Error Probability Values (Evaluated after the study) – Probability of finding this big a difference by chance p =.07 of this big a difference by chance – You are not stating the probability of the inverse p =.93 that it is real difference is not appropriate – Compare p-value to alpha level if greater – Compare your p-value (calculated after the study) with your α level (set before the study) – If the p-value is less than α, reject the null – If the p-value is greater than α, fail to reject the null

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Type II Error Statistical Power Beta (β) – Probability of failing to reject a false H O(null hypotheses) – Β of 0.20 is 20% chance we will make a Type II error Statistical Power – Complement of β (not compliment) – In this example 0.80 (1.00 – 0.20 = 0.80) – 80% probability of correctly rejecting the null Before: a priori - power used to determine sample size After: post hoc – power reported if H O not rejected “If there was a difference, could we have found it?”

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Determinants of Statistical Power Significance Criterion – As α increases, power increases (As α increases from 0.05 to 0.10, power increases) Variance – As variance decreases, power increases Sample size – As sample size increases, power increases Effect size (difference b/w the group means) – As effect size increases, power increases

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z- z - score represents the distance between: – A sample score and – Sample mean – Divided by the standard deviation You will see this in osteoporosis scores (+2 for z- score is 2 SD away from a healthily woman mean) z - ratio represents the distance between: – A sample mean and – Population mean – Divided by the standard error of the mean

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Critical Region That portion of the curve above and below z If calculated z > critical z, reject H O One or two tailed test? Non directional hypothesis– two tailed z of 2.00 encompasses % (non-critical) 4.66% is the critical area z of 1.96 encompass 95%, Critical region is 5%, 2.5% in each tail of a non-directional test Directional hypothesis– one tailed z of encompasses 95% Critical region is 5%, all in one tail of a directional test, while NON-direction will be 2.5% Practically, you are disregarding everything in the other tail Table A.1 back of P & W

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Figure A: Intervention 1 is different than Intervention 2 Figure B: Intervention 1 is less effective than Intervention 2

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Parametric Statistics Used to estimate population parameters Based on assumptions – Randomly drawn from a normally distributed population – Variances in the samples equal (at least roughly) – Interval or ratio scale Classically, if assumptions violated, use non- parametric tests Many view parametric stats as Robust enough to withstand even major violation

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t-test Examines two means Two groups Two conditions/two performances Statistical significance based on Difference in the means Between the groups The effect size Variance Within the groups How variable are the scores Fig 19.1

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t-test Based on a ratio Difference between group means/Variability within groups Difference between means Treatment effect and error variance One mean- second mean and variability between the groups. In both the numerator and denominator of t-test ratio, so holds it to zero. Variability within groups Error variance alone Equal and unequal variances affect t-ratio SPSS and most other packages automatically test for this. Where? Ratio can be written: Treatment effect and error variance/Error variance NOTE: Error variance Not mistakes Is anything that is not due to the independent variable

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t-test If the null is true Ratio reduces to: Error/Error The bigger the difference - The bigger the ratio How does the ratio get bigger? This ratio is compared to the critical Determines significance Is the ratio significant? Based on critical value (but now t instead of z) Entering arguments (Table A.2) Alpha level (almost always 0.05 Degrees of freedom ( one or a few less than n ) CI can be constructed for where the true mean difference lies

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t-test Independent t-test Usually random assignment Can be convenience assignment No inherent relationship between the groups Degrees of freedom = total sample size – 2 Paired t-test An inherent relationship between the groups Self (repeated measure test) Twins Difference scores for each pair compared Degrees of freedom = number of paired scores – 1 Find this in P & W or in an SPSS output table – don’t calculate for this class!

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ANOVA Examines three (or more) means Three (or more) groups Three (or more) conditions/ three (or more) performances Statistical significance based on Difference in the means Between the groups The effect size Variance Within the groups How variable are the scores This should sound familiar

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ANOVA Based on ratio Treatment effect and error variance/Error variance For ANOVA it is the F-ratio Derived from the Sum of Squares (SS) Larger the SS the larger the ______________? Calculate SS (each score minus sample mean, square each result, sum them) Then determine the Mean Square (MS) MS b = SS b /df b (df b = one less than the number of groups) MS e = SS e /df e (df e = total N – number of groups) F statistic is the ratio = MS b /Ms e Ratio of the between groups variance to error variance Find this in P & W or in an SPSS output table – don’t calculate for this class!

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