** Summary of Statistical
Tests (03 25 08) **(sample results below were fabricated)

Return to Opening Page of PSYC 4650

__T Test__

To compare the means on a dependent variable of two independent groups. E.g., mean mental health scores of ADD vs. non ADD groups of children.

**Sample results:** The mental health score of children
with ADD (mean= 3.34) was significantly lower than that of children without ADD
(mean=4.45), indicating that ADD children have more mental health problems than
nonADD children, *t* (25)= 2.43, p<001 .

__Correlation__

To compare the relationship between two sets of measures on individuals to see if the scores are related. E.g., relationship between mental health scores and birth weight of ADD and nonADD children.

**Sample results**: There was a small (*r*=.14)
but statistically significant (*p*<.001) positive correlation between birth
weight and mental health scores, indicating that the lower the child’s birth
weight, the more mental health problems the children had.

__One Way ANOVA__

To compare the means on a dependent variable of several independent groups. E.g., mean mental health scores of ADD vs. LD vs. non ADD/non LD.

**Sample results**: Mental health scores of ADD children
(mean=3.51) and LD children (mean =3.47) were lower than mental health scores of
non ADD, nonLD children (mean=4.43), and the differences were statistically
significant, F (2, 469) = 57.23, p<.001.

__Two-Way (multi factor) ANOVA__

To compare means of a dependent variable among two or more levels of two or more factors (independent variables). Includes study of interactions of the independent variables.

E.g., comparison of mean mental health scores of ADD vs. LD vs. nonADD/ nonLD and Boys vs. Girls.

**Sample results: **Two-way ANOVA showed that mental
health scores of ADD children (mean=3.51) were significantly lower than nonADD
children (mean= 4.32), F (1, 359) = 23.91, p<.01. Also, mental health scores of
boys (mean= 3.01) were significantly lower than mental health scores of girls
(mean=4.21), F (1, 359) =15.92, p<.05. Furthermore, there was a significant
interaction between the gender and ADD/nonADD factors, F (1, 359) = 6.45, p <05.
Post hoc analysis using the Tukey test showed that mean of ADD boys was
significantly lower (p<.05) that the means of the other groups, which did not
different among themselves.

__Cross Tabs, Chi Square__

Cross tabs examines the frequency or percentage of pairings between two categorical variables. Chi Square provides a statistical measure of two variables which are theoretically expected do be independent vs. whether they are actually observed to be independent of each other. E.g., % boys vs. girls with ADD vs. nonADD.

**Sample Results**: A cross tabs analysis paired
percentages of boys vs. girls, with percentages of ADD vs. nonADD cases. The
overall chi square was statistically significant, *Χ ^{2}* (1)