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STATISTICS David Pieper, Ph.D.

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1 STATISTICS David Pieper, Ph.D.

2 Types of Variables Categorical Variables Organized into category No necessary order No quantitative measure Examples male, female race marital status treatment A and treatment B

3 Types of Variables Ordinal Data Ranked or ordered Examples: –strongly agree, agree, disagree –worse, no change, better –1st place, 2nd place, 3rd place

4 Types of Variables Continuous Variables Have specific order Examples: –weight –temperature –blood pressure –time May be converted to categorical or ordinal

5 Types of Statistics Descriptive –summarize data for clearer understanding Inferential –generalize results from sample to population –make probability decisions

6 Descriptive Statistics Measures of central tendency –mean –mode –median Measures of variability –range –variance –standard deviation –standard error

7 Research Hypothesis Null hypothesis: relationship among phenomena does not exist Example: kids who attend daycare have no greater incidence of colds than kids who do not attend daycare

8 Probability and p Values p < 0.05 –1 in 20 or 5% chance groups are not different when we say groups are significantly different p < 0.01 –1 in 100 or 1% chance of error p < 0.001 –1 in 1000 or.1% chance of error

9 Type of Statistical Test to Use Continuous variable as end point –2 groups: t-test –3 or more groups: ANOVA Relation between 2 categorical variables: –Chi-square test –Fisher’s Exact test (2 x 2) Relation between 2 continuous variables: –Regression analysis or correlation

10 T-test When comparing 2 independent groups and end-point variable (dependent variable) is continuous Purpose is determine if the difference between the 2 groups is unlikely due to chance May be paired or unpaired

11 T-test Example: Blood pressure before and after exercise program (paired t-test) Compare blood pressure in a group undergoing cardiac rehab to a control group not undergoing rehab (unpaired t-test)

12 Analysis of Variance (ANOVA) When comparing 3 or more groups (independent variables) and end-point (dependent variable) is continuous.

13 Analysis of Variance (ANOVA)

14 p < 0.001 overall there is a difference between groups - does not tell us which groups are different from one another Post-hoc analysis with Tukey’s multiple comparison test A vs B p < 0.001 A vs C p > 0.05 (not significantly different) B vs C p < 0.001

15 Chi-square Test When comparing 2 or more groups and the dependent variable is categorical Minimum frequency in any cell must be at least 5 If less than 5 and a 2 x 2 analysis - use Fisher’s Exact Test

16 Is there a relationship between hypertension and gender? Chi square analysis - p < 0.001

17 Correlation or Regression When determining if there is a linear relationship between 2 continuous variables Ranges from -1 to 1 Assumptions: –Relationship is linear –Random variables

18 Pearson’s Correlation Coefficient Is Diastolic BP related to Weight? r = 0.805 p < 0.01

19 Pearson’s Correlation Coefficient r = 0.805 does not mean weight gain causes increase in BP or vice versa Correlation does not prove cause and effect


21 Name the Statistical Test Do students improve their knowledge after a lecture, as measured by the number of correct answers on a quiz before and after the lecture? a.ANOVA b.Chi-Square c.Paired t-test * d.Unpaired t-test

22 Name the Statistical Test Is there an association between smoking status and 3 levels of socioeconomic status? a.Mann-Whitney U-test b.Pearson’s correlation c.Turkey’s test d.Chi-Square *

23 Name the Statistical Test Is there a relationship between length of hospitalization and number of medications prescribed when patient is discharged? a.Logistic regression b.Pearson’s correlation * c.Repeated measures ANOVA d.Chi-Square

24 Free Statistics Software

25 Illustrations Graphs - not tables Replace keys with direct labels Use color Each axis must have a label with units Each graph must have a legend


27 Girls Boys

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