N_REsearch_Discussion10.
Olivera71
tappen_2e_chapter_19.pptxChapter 19
Basic Quantitative Data Analysis
Data Cleaning
Check for odd symbols, truncated or overlong times
Recheck scoring
Recheck coding categories
Compare one variable value with value in second variable
Look for outliers
Reasons for Missing Data
Participant skipped item or questionnaire, purposely or inadvertently
Participant withdrew, became ill, or died
Had to omit all or part of the data collection
Poor directions or poorly worded question
Data missed during data entry
Categorizing Missing Data
Missing completely at random (MCAR)
Missing at random (MAR)
Missing not at random (MNAR)
Replacing Missing Data
Complete case analysis is when you drop any participant from the analysis when they have missing data
If a lot of participants are missing data it may negatively impact the results
Replacing Missing Data
Principles in handling missing data are:
Some missing data cannot be replaced
Imputation uses existing information to estimate the missing values
The easiest approach is to replace missing data with the group’s mean (average) on the item
Replacing Missing Data
Principles in handling missing data are:
A more justifiable approach is to use the average of the individual participant’s scores or ratings on the remaining items of a multi-item scale
Missing values may be estimated from values at previous time points
Replacing Missing Data
Principles in handling missing data are:
Incomplete cases (participants) may be deleted and the analysis may be done on those who completed the study
A regression imputation may be done to estimate the values of the missing data
Replacing Missing Data
Principles in handling missing data are:
Expectation maximization uses a series of iterations to reach convergence
Multiple imputation contrasts and combines replacement values to find the best estimates
Visual Representations
Stem and leaf illustrates distribution of values
Box plots illustrate distribution of values
Bar and pie charts demonstrate differences between groups and subgroups
Plots can show relationships between interval level variables
Basic Descriptive Statistics
Normal distribution is represented by a symmetrical bell-shaped curve
Positive skew has more cases at low end of values
Negative skew has more cases at high end of values
Basic Descriptive Statistics
Mode is the value that occurs most often
Median is the middle score in the distribution
Mean is the average of all scores
Basic Descriptive Statistics
Range is the distance between the highest and lowest scores
The range or distance between these endpoints can be divided into various portions
Basic Descriptive Statistics
Variance is the average of the squared deviations from the mean
Standard deviation is the square root of the variance
Bivariate Association
Bivariate refers to relationships between a set of variables
Pearson product moment correlation coefficient represented as r is the most commonly used bivariate measure of association
Bivariate Association
A correlation matrix can be used to analyze multiple variables at one time to see the differences in the strengths of relationships between various pairs of variables
You may also calculate the coefficient of determination (R2)
Additional Measures of Association
Spearman rank-order correlation is used for ordinal data
The chi-square statistic is used for nominal data
Additional Measures of Association
Fisher’s Exact Test is used if there are less than five cases per cell
The Mann-Whitney U Test is used instead of chi-square if the data are ordinal
