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1、,Will G HopkinsAuckland University of TechnologyAuckland NZ,Quantitative Data Analysis,Summarizing Data:variables;simple statistics;effect statistics and statistical models;complex models.Generalizing from Sample to Population:precision of estimate,confidence limits,statistical significance,p value,
2、errors.,Reference:Hopkins WG(2002).Quantitative data analysis(Slideshow).Sportscience 6,sportsci.org/jour/0201/Quantitative_analysis.ppt(2046 words),Summarizing Data,Data are a bunch of values of one or more variables.A variable is something that has different values.Values can be numbers or names,d
3、epending on the variable:Numeric,e.g.weightCounting,e.g.number of injuriesOrdinal,petitive level(values are numbers/names)Nominal,e.g.sex(values are namesWhen values are numbers,visualize the distribution of all values in stem and leaf plots or in a frequency histogram.Can also use normal probabilit
4、y plots to visualize how well the values fit a normal distribution.When values are names,visualize the frequency of each value with a pie chart or a just a list of values and frequencies.,A statistic is a number summarizing a bunch of values.Simple or univariate statistics summarize values of one va
5、riable.Effect or outcome statistics summarize the relationship between values of two or more variables.Simple statistics for numeric variablesMean:the averageStandard deviation:the typical variationStandard error of the mean:the typical variation in the mean with repeated samplingMultiply by(sample
6、size)to convert to standard deviation.Use these also for counting and ordinal variables.Use median(middle value or 50th percentile)and quartiles(25th and 75th percentiles)for grossly non-normally distributed data.Summarize these and other simple statistics visually with box and whisker plots.,Simple
7、 statistics for nominal variablesFrequencies,proportions,or odds.Can also use these for ordinal variables.Effect statisticsDerived from statistical model(equation)of the form Y(dependent)vs X(predictor or independent).Depend on type of Y and X.Main ones:,Model:numeric vs numerice.g.body fat vs sum o
8、f skinfoldsModel or test:linear regressionEffect statistics:slope and intercept=parameterscorrelation coefficient or variance explained(=100correlation2)=measures of goodness of fitOther statistics:typical or standard error of the estimate=residual error=best measure of validity(with criterion varia
9、ble on the Y axis),sum skinfolds(mm),body fat(%BM),Model:numeric vs nominale.g.strength vs sexModel or test:t test(2 groups)1-way ANOVA(2 groups)Effect statistics:difference between meansexpressed as raw difference,percent difference,or fraction of the root mean square error(Cohens effect-size stati
10、stic)variance explained or better(variance explained/100)=measures of goodness of fitOther statistics:root mean square error=average standard deviation of the two groups,female,male,strength,sex,More on expressing the magnitude of the effectWhat often matters is the difference between means relative
11、 to the standard deviation:,Fraction or multiple of a standard deviation is known as the effect-size statistic(or Cohens d).Cohen suggested thresholds for correlations and effect sizes.Hopkins agrees with the thresholds for correlations but suggests others for the effect size:,For studies of athleti
12、c performance,percent differences or changes in the mean are better than Cohen effect sizes.,Model:numeric vs nominal(repeated measures)e.g.strength vs trialModel or test:paired t test(2 trials)repeated-measures ANOVA withone within-subject factor(2 trials)Effect statistics:change in mean expressed
13、as raw change,percent change,or fraction of the pre standard deviation Other statistics:within-subject standard deviation(not visible on above plot)=typical error:conveys error of measurementuseful to gauge reliability,individual responses,and magnitude of effects(for measures of athletic performanc
14、e).,pre,post,strength,trial,Model:nominal vs nominale.g.sport vs sexModel or test:chi-squared test or contingency tableEffect statistics:Relative frequencies,expressed as a difference in frequencies,ratio of frequencies(relative risk),or ratio of odds(odds ratio)Relative risk is appropriate for cros
15、s-sectional or prospective designs.risk of having rugby disease for males relative to females is(75/100)/(30/100)=2.5Odds ratio is appropriate for case-control designs.calculated as(75/25)/(30/70)=7.0,females,males,30%,75%,rugby yes,rugby no,Model:nominal vs numerice.g.heart disease vs ageModel or t
16、est:categorical modelingEffect statistics:relative risk or odds ratioper unit of the numeric variable(e.g.,2.3 per decade)Model:ordinal or counts vs whateverCan sometimes be analyzed as numeric variables using regression or t testsOtherwise logistic regression or generalized linear modelingComplex m
17、odelsMost reducible to t tests,regression,or relative frequencies.Example,age(y),heartdisease(%),0,100,30,50,70,Model:controlled trial(numeric vs 2 nominals)e.g.strength vs trial vs groupModel or test:unpaired t test of change scores(2 trials,2 groups)repeated-measures ANOVA withwithin-and between-s
18、ubject factors(2 trials or groups)Note:use line diagram,not bar graph,for repeated measures.Effect statistics:difference in change in mean expressed as raw difference,percent difference,or fraction of the pre standard deviation Other statistics:standard deviation representing individual responses(de
19、rived from within-subject standard deviations in the two groups),pre,post,strength,trial,drug,placebo,Model:extra predictor variable to control for somethinge.g.heart disease vs physical activity vs ageCant reduce to anything simpler.Model or test:multiple linear regression or analysis of covariance
20、(ANCOVA)Equivalent to the effect of physical activity with everyone at the same age.Reduction in the effect of physical activity on disease when age is included implies age is at least partly the reason or mechanism for the effect.Same analysis gives the effect of age with everyone at same level of
21、physical activity.Can use special analysis(mixed modeling)to include a mechanism variable in a repeated-measures model.See separate presentation at newstats.org.,Problem:some models dont fit uniformly for different subjects That is,between-or within-subject standard deviations differ between some su
22、bjects.Equivalently,the residuals are non-uniform(have different standard deviations for different subjects).Determine by examining standard deviations or plots of residuals vs predicteds.Non-uniformity makes p values and confidence limits wrong.How to fixUse unpaired t test for groups with unequal
23、variances,orTry taking log of dependent variable before analyzing,orFind some other transformation.As a last resort Use rank transformation:convert dependent variable to ranks before analyzing(=non-parametric analysissame as Wilcoxon,Kruskal-Wallis and other tests).,Generalizing from a Sample to a P
24、opulation,You study a sample to find out about the population.The value of a statistic for a sample is only an estimate of the true(population)value.Express precision or uncertainty in true value using 95%confidence limits.Confidence limits represent likely range of the true value.They do NOT repres
25、ent a range of values in different subjects.Theres a 5%chance the true value is outside the 95%confidence interval:the Type 0 error rate.Interpret the observed value and the confidence limits as clinically or practically beneficial,trivial,or harmful.Even better,work out the probability that the eff
26、ect is clinically or practically beneficial/trivial/harmful.See sportsci.org.,Statistical significance is an old-fashioned way of generalizing,based on testing whether the true value could be zero or null.Assume the null hypothesis:that the true value is zero(null).If your observed value falls in a
27、region of extreme values that would occur only 5%of the time,you reject the null hypothesis.That is,you decide that the true value is unlikely to be zero;you can state that the result is statistically significant at the 5%level.If the observed value does not fall in the 5%unlikely region,most people
28、 mistakenly accept the null hypothesis:they conclude that the true value is zero or null!The p value helps you decide whether your result falls in the unlikely region.If p0.05,your result is in the unlikely region.,One meaning of the p value:the probability of a more extreme observed value(positive
29、or negative)when true value is zero.Better meaning of the p value:if you observe a positive effect,1-p/2 is the chance the true value is positive,and p/2 is the chance the true value is negative.Ditto for a negative effect.Example:you observe a 1.5%enhancement of performance(p=0.08).Therefore there
30、is a 96%chance that the true effect is any enhancement and a 4%chance that the true effect is any impairment.This interpretation does not take into account trivial enhancements and impairments.Therefore,if you must use p values,show exact values,not p0.05.Meta-analysts also need the exact p value(or
31、 confidence limits).,If the true value is zero,theres a 5%chance of getting statistical significance:the Type I error rate,or rate of false positives or false alarms.Theres also a chance that the smallest worthwhile true value will produce an observed value that is not statistically significant:the
32、Type II error rate,or rate of false negatives or failed alarms.In the old-fashioned approach to research design,you are supposed to have enough subjects to make a Type II error rate of 20%:that is,your study is supposed to have a power of 80%to detect the smallest worthwhile effect.If you look at lo
33、ts of effects in a study,theres an increased chance being wrong about at least one of them.Old-fashioned statisticians like to control this inflation of the Type I error rate within an ANOVA to make sure the increased chance is kept to 5%.This approach is misguided.,The standard error of the mean(ty
34、pical variation in the mean from sample to sample)can convey statistical significance.Non-overlap of the error bars of two groups implies a statistically significant difference,but only for groups of equal size(e.g.males vs females).In particular,non-overlap does NOT convey statistical significance
35、in experiments:,In summaryIf you must use statistical significance,show exact p values.Better still,show confidence limits instead.NEVER show the standard error of the mean!Show the usual between-subject standard deviation to convey the spread between subjects.In population studies,this standard deviation helps convey magnitude of differences or changes in the mean.In interventions,show also the within-subject standard deviation(the typical error)to convey precision of measurement.In athlete studies,this standard deviation helps convey magnitude of differences or changes in mean performance.,
