t(ea) for Two: Test between the Means of Different Groups When you want to know if there is a ‘difference’ between the two groups in the mean Use “t-test”. Why can’t we just use the “difference” in score? Because we have to take the ‘variability’ into account. T = difference between group means sampling variability

One-Sample T Test Evaluates whether the mean on a test variable is significantly different from a constant (test value). Test value typically represents a neutral point. (e.g. midpoint on the test variable, the average value of the test variable based on past research)

Example of One-sample T-test Is the starting salary of company A ($17,016.09) the same as the average of the starting salary of the national average ($20,000)? Null Hypothesis: Starting salary of company A = National average Alternative Hypothesis: Starting salary of company A = National average

SPSS demo (“employee data”) Review: Standard deviation: Measure of dispersion or spread of scores in a distribution of scores. Standard error of the mean: Standard deviation of sampling distribution. How much the mean would be expected to vary if the differences were due only to error variance. Significance test: Statistical test to determine how likely it is that the observed characteristics of the samples have occurred by chance alone in the population from which the samples were selected.

z and t Z score : standardized scores Z distribution : normal curve with mean value z=0 95% of the people in the given sample (or population) have z-scores between –1.96 and T distribution is adjustment of z distribution for sample size (smaller sampling distribution has flatter shape with small samples). T = difference between group means sampling variability

Confidence Interval A range of values of a sample statistic that is likely (at a given level of probability, i.e. confidence level) to contain a population parameter. The interval that will include that population parameter a certain percentage (= confidence level) of the time.

Confidence Interval for difference and Hypothesis Test When the value 0 is not included in the interval, that means 0 (no difference) is not a plausible population value. It appears unlikely that the true difference between Company A’s salary average and the national salary average is 0. Therefore, Company A’s salary average is significantly different from the national salary average.

Independent-Sample T test Evaluates the difference between the means of two independent groups. Also called “Between Groups T test” Ho:  1 =  2 H1:  1 =  2

Paired-Sample T test Evaluates whether the mean of the difference between the paired variables is significantly different than zero. Applicable to 1) repeated measures and 2) matched subjects. Also called “Within Subject T test” “Repeated Measures T test”. Ho:  d = 0 H1:  d = 0

SPSS Demo

Analysis of Variance (ANOVA) An inferential statistical procedure used to test the null hypothesis that the means of two or more populations are equal to each other. The test statistic for ANOVA is the F- test (named for R. A. Fisher, the creator of the statistic).

T test vs. ANOVA T-test Compare two groups Test the null hypothesis that two populations has the same average. ANOVA: Compare more than two groups Test the null hypothesis that two populations among several numbers of populations has the same average.

ANOVA example Example: Curricula A, B, C. You want to know what the average score on the test of computer operations would have been if the entire population of the 4 th graders in the school system had been taught using Curriculum A; What the population average would have been had they been taught using Curriculum B; What the population average would have been had they been taught using Curriculum C. Null Hypothesis: The population averages would have been identical regardless of the curriculum used. Alternative Hypothesis: The population averages differ for at least one pair of the population.

ANOVA: F-ratio The variation in the averages of these samples, from one sample to the next, will be compared to the variation among individual observations within each of the samples. Statistic termed an F-ratio will be computed. It will summarize the variation among sample averages, compared to the variation among individual observations within samples. This F-statistic will be compared to tabulated critical values that correspond to selected alpha levels. If the computed value of the F-statistic is larger than the critical value, the null hypothesis of equal population averages will be rejected in favor of the alternative that the population averages differ.

Interpreting Significance p<.05 The probability of observing an F-statistic at least this large, given that the null hypothesis was true, is less than.05.

Logic of ANOVA If 2 or more populations have identical averages, the averages of random samples selected from those populations ought to be fairly similar as well. Sample statistics vary from one sample to the next, however, large differences among the sample averages would cause us to question the hypothesis that the samples were selected from populations with identical averages.

Logic of ANOVA cont. How much should the sample averages differ before we conclude that the null hypothesis of equal population averages should be rejected. In ANOVA, the answer to this question is obtained by comparing the variation among the sample averages to the variation among observations within each of the samples. Only if variation among sample averages is substantially larger than the variation within the samples, do we conclude that the populations must have had different averages.

Three types of ANOVA One-way ANOVA Within-subjects ANOVA (Repeated measures, randomized complete block) Factorial ANOVA (Two-way ANOVA)

Sources of Variation Three sources of variation: 1) Total, 2) Between groups, 3) Within groups Sum of Squares (SS): Reflects variation. Depend on sample size. Degrees of freedom (df): Number of population averages being compared. Mean Square (MS): SS adjusted by df. MS can be compared with each other. (SS/df) F statistic: used to determine whether the population averages are significantly different. If the computed F static is larger than the critical value that corresponds to a selected alpha level, the null hypothesis is rejected.

Computing F-ratio SS Total: Total variation in the data df total: Total sample size (N) -1 MS total: SS total/ df total SS between: Variation among the groups compared. df between: Number of groups -1 MS between : SS between/df between SS within: Variation among the scores who are in the same group. df within: Total sample size - number of groups -1 MS within: SS within/df within F ratio = MS between / MS within

Formula for One-way ANOVA

Alpha inflation Conducting multiple ANOVAs, will incur a large risk that at least one of them would be statistically significant just by chance. The risk of committee Type I error is very large for the entire set of ANOVAs. Example: 2 tests.05 Alpha Probability of not having Type I error.95.95x.95 =.9025 Probability of at least one Type I error is = Close to 10 %. Use more stringent criteria. e.g..001

Relation between t-test and F-test When two groups are compared both t-test and F-test will lead to the same answer. t 2 = F. So by squaring t you’ll get F (or square root of t is F)

Follow-up test Conducted to see specifically which means are different from which other means. Instead of repeating t-test for each combination (which can lead to an alpha inflation) there are some modified versions of t-test that adjusts for the alpha inflation. Most recommended: Tukey HSD test Other popular tests: Bonferroni test, Scheffe test

Within-Subject (Repeated Measures) ANOVA SS tr : Sum of Squares Treatment SS block : Sum of Squares Block SS error = SS total - SS block - SS tr MS tr = SS tr/k-1 MSE = SS error/(n-1)(k-1) F = MS tr/MSE

Within-Subject (Repeated Measures) ANOVA Examine differences on a dependent variable that has been measured at more than two time points for one or more independent categorical variables.

Within-Subject (Repeated Measures) ANOVA

Factorial ANOVA T-test and One way ANOVA 1 independent variable (e.g. Gender), 1 dependent variable (e.g. Test score) Two-way ANOVA (Factorial ANOVA) 2 (or more) independent variables (e.g. Gender and Academic Standing), 1 dependent variable (e.g. Test score)

(End of Analytic Method I)

Main Effects and Interaction Effects Main Effects The effects for each independent variable on the dependent variable. Differences between the group means for each independent variable on the dependent variable. Interaction Effect When the relationship between the dependent variable and one independent variable differs according to the level of a second independent variable. When the effect of one independent variable on the dependent variable differs at various levels of second independent variable.

T-distribution A family of theoretical probability distributions used in hypothesis testing. As with normal distributions (or z-distributions), t distributions are unimodal, symmetrical and bell shaped. Important for interpreting data gather on small samples when the population variance is unknown. The larger the sample, the more closely the t approximates the normal distribution. For sample greater than 120, they are practically equivalent.