RESULTS & DATA ANALYSIS

Descriptive Statistics  Descriptive (describe)  Frequencies  Percents  Measures of Central Tendency mean median mode

Measures of Spread  Range  Nominal data = number of responses in each category (A’s = 4, B’s = 7, C’s = 21, D’s = 1, F’s=2)  Other data = difference between responses for the greatest and least numeric values (Age of oldest is 104 and youngest is 18. Range =86 years)  Tertiles, Quartiles, Quintiles Interquartile Range (IQR) Range for the 25 th to 75 th percentiles which captures the middle 50%

Measures of Spread  Standard Deviation – describes, on average, how much individual values differ from the mean  Standard Scores or Z-scores – describes how many SD’s away from the sample mean an individual score or response is

68% of individuals 95% of individuals >99% of individuals Sample Mean +1 SD -1 SD +2 SD +3 SD -2 SD -3 SD Normal Distribution

Inferential Statistics (Comparative)  Based on Probability  Tests of significance: are observed differences real differences or simply the result of chance t-test: test difference between means of two groups (t statistic) ANOVA (Analysis of Variance): test difference among three of more independent groups (F statistic)

Interpreting p-values or probability value  Used to decide whether the results observed are likely to reflect real differences between groups  The standard is to use α = 0.05 or 5% (1 in20) t = 1.11, p =0.042 t = 1.02, p =  Probability that results observed have occurred by chance alone  α = probability of Type I error (finding statistical significance when in reality there is none)

Significance  Statistical  Practical  Clinical

Parametric and Non-Parametric  Parametric = DV is some measured quantity (ratio or interval) so it makes sense to calculate means and SD  You can draw bell curves through the data defined by two parameters…the mean and SD….hence parametric (beside, near)  Non-Parametric = DV is count or rankings so means and SD have no meaning (e.g. The average religion of Americans is 2.67)

Parametric Tests  T-tests  ANOVA (analysis of variance)  Linear Regression or Multiple Linear Regression  ANCOVA (analysis of covariance) – combines regression and analysis of variance

Non-Parametric Tests  Chi-square  Logistic Regression  Log-linear analysis