I square statistics

•Test of heterogeneity

•I2 can be calculated from Cochrane Q according to the formula: 

•The percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (chance).

•Any negative values of I2 are considered equal to 0, so that the range of I2 values is between 0% and 100%.

•A meta-analysis with

•I2= 0 means that all variability in effect size estimates is due to sampling error

•I2= 50 means that half of the total variability among effect sizes is by true heterogeneity between studies

•I2percentages of around 25% , 50% and 75% would mean low, medium, and high heterogeneity, respectively

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Cochrane Q

•Cochrane Q – indicates the presence or absence of heterogeneity without measuring the effect

•The Q test is computed by summing the squared deviations of each study’s effect estimate from the overall effect estimate, weighting the contribution of each study by its inverse variance

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Confidence Interval and Prediction Interval

•Confidence interval (C.I.) the width of the diamond: in 95% of cases mean effect size will fall inside the diamond

•Quantifies accuracy of the mean 

•Only considers error of estimation of mean

•Prediction interval (P.I.) horizontal line to either end of the diamond: in 95% of cases the true effect in a new study will fall inside the horizontal lines

•P.I. addresses the actual dispersion of effect sizes

•Considers both true dispersion as well as error

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Sampling

Probability sampling:

Each population element has a known probability or chance of being chosen for sample 

E.g. Simple random sampling, stratified sampling, cluster sampling, 

multistage sampling, systematic random sampling 





Non-probability sampling:

One cannot be assured of having known probability of each population element

E.g. Volunatary sampling, Convenient sampling 

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Kish grid

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Likelihood Ratio

Likelihood Ratio

•Likelihood ratio +: How much the increase in the probability of having the disease if the test result is positive

  LR+ = Sn/(1-Sp)

•Likelihood ratio-: How much decrease in the probability of having the disease if the test result is negative

    LR- = (1-Sn)/Sp

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Cronbachs Alpha

•Cronbach’s Alpha: 

It is a measure of internal consistency of a test/scale

•Measure that the scale is consistent measure of a concept

•Computed by correlating the score for each item of the scale with the total score for each observation and then comparing

that to the variance of all individual item scores

•It is a f(x) of number of items in a test, average covariance between pair of items, and variance of total score

•Minimum recommended value 0.65-0.8

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