
Statistical Tests

tarix  09.02.2017  ölçüsü  1,43 Mb.   #8125 

Statistical Tests
Let’s Take it Step by Step... Identify topic Literature review Research hypothesis Design study Power analysis Write proposal Design data tools Committees
Goals To understand why a particular statistical test was used for your research project To interpret your results To understand, evaluate, and present your results
Mystat: http://www.systat.com/MystatProducts.aspx Mystat: http://www.systat.com/MystatProducts.aspx List of Free Statistics Software: http://statpages.org/javasta2.html
Before choosing a statistical test… Figure out the variable type  Scales of measurement (qualitative or quantitative)
Figure out your goal  Compare groups
 Measure relationship or association of variables
Nominal Ordinal Interval Ratio
Nominal Scale (discrete) Simplest scale of measurement Variables which have no numerical value Variables which have categories Count number in each category, calculate percentage Examples:  Gender
 Race
 Marital status
 Whether or not tumor recurred
 Alive or dead
Ordinal Scale Variables are in categories, but with an underlying order to their values Rankorder categories from highest to lowest Intervals may not be equal Count number in each category, calculate percentage Examples:  Cancer stages
 Apgar scores
 Pain ratings
 Likert scale
Interval Scale Quantitative data Cannot multiply & divide values Example:  Temperature on a Celsius scale
 00 indicates point when water will freeze, not an absence of warmth
Ratio Scale (continuous) Quantitative data with true zero  Can add, subtract, multiply & divide
Examples:  Age
 Body weight
 Blood pressure
 Length of hospital stay
 Operating room time
Scales of Measurement Nominal Ordinal Interval Ratio
Two Branches of Statistics Descriptive  Frequencies & percents
 Measures of the middle
 Measures of variation
Inferential  Nonparametric statistics
 Parametric statistics
Descriptive Statistics First step in analyzing data Goal is to communicate results, without generalizing beyond sample to a larger group
Frequencies and Percents Number of times a specific value of an observation occurs (counts) For each category, calculate percent of sample
Measures of the Middle or Central Tendency Mean  Average score
 Most common measure, but easily influenced by outliers
Median  50th percentile score
 Use when data are asymmetrical or skewed
Measures of Variation or Dispersion Standard deviation (SD)  Square root of the sum of squared deviations of the values from the mean divided by the number of values
Standard error (SE)  Standard deviation divided by the square root of the number of values
Measures of Variation or Dispersion Variance  Square of the standard deviation
Range  Difference between the largest & smallest value
Inferential Statistics Nonparametric tests Parametric tests  Used for analyzing interval & ratio variables
 Makes assumptions about data
 Normal distribution
 Homogeneity of variance
 Independent observations
Which Test Do I Use? Step 1 Know the scale of measurement Step 2 Know your goal  Is it to compare groups? How many groups do I have?
 Is it to measure a relationship or association between variables?
Key Inferential Statistics ChiSquare Ttest Analysis of Variance (ANOVA) Pearson’s Correlation Linear Regression
p < 0.05 p < 0.05 p < 0.01  1 in 100 or 1% chance of error
p < 0.001  1 in 1000 or .1% chance of error
Research Hypothesis Topic research question Research question hypothesis  Null hypothesis (H0)
 Predicts no effect or difference
 Alternative hypothesis (H1)
 Predicts an effect or difference
Are These Categorical Variables Associated?
ChiSquare Most common nonparametric test Use to test for association between categorical variables Use to test the difference between observed & expected proportions  The larger the chisquare value, the more the numbers in the table differ from those we would expect if there were no association
Limitation  Expected values must be equal to or larger than 5
Alternative to ChiSquare Fisher’s exact test  Is based on exact probabilities
 Use when expected count <5 cases in each cell and
 Use with 2 x 2 contingency table
Do These Groups Differ?
Unpaired ttest or Student’s ttest Frequently used statistical test Use when there are two independent groups
Unpaired ttest or Student’s ttest Test for a difference between groups  Is the difference in sample means due to their natural variability or to a real difference between the groups in the population?
Outcome (dependent variable) is interval or ratio Assumptions of normality, homogeneity of variance & independence of observations
Do These Groups Differ?
Analysis of Variance (ANOVA) or Ftest Three or more independent groups Test for a difference between groups  Is the difference in sample means due to their natural variability or to a real difference between the groups in the population?
Outcome (dependent variable) is interval or ratio Assumptions of normality, homogeneity of variance & independence of observations
Let’s Test For A Difference
Is there a relationship between the variables?
Pearson’s Correlation Measures the degree of relationship between two variables Assumptions:  Variables are normally distributed
 Relationship is linear
 Both variables are measured on the interval or ratio scale
 Variables are measured on the same subjects
Scatterplots r = 1.0  +1.0
Let’s Test For A Relationship
Interpretation of Results The size of the p value does not indicate the importance of the result Appropriate interpretation of statistical test  Group differences
 Association or relationship
 “Correlation does not imply causation”
Don’t Lie With Statistics !
Dostları ilə paylaş: 

