Data management involves the actions of researchers to organize, describe, preserve, and share their data.
Start by creating a Data Management Plan (DMP).
Descriptive statistics are used to DESCRIBE the study population using calculations, tables and/or graphs.
Statistics of central tendency:
|Mean||The sum of all values in a group/# items in the group (Average)|
|Median||The value in the middle of a group of values (Typical)|
|Mode||The value that appears the most in a group of values (Most Common)|
Statistics of variation:
Range = (Highest # – Lowest #)
The simplest way to describe variation in a set of values
Very sensitive to data that doesn’t fit the typical pattern (called outliers)
|Interquartile Range (IQR)||
Identifies variation in a set of values after removing outliers (focus on the 50% of data closest to the mean)
Reported as a range of numbers
|Standard Deviation (SD)||
Identifies variation in a set of values by estimating the average distance of each score from the mean
Small SD = more concentratedLarge SD = less concentrated
Inferential statistics use data to make JUDGEMENTS about the differences between study groups for generalizing to the overall population.
Evaluates the statistical significance of the differences between two study groups or the relationships between two study variables. It estimates the ability to reject the null hypothesis that there is no difference between the two things.
Statistical significance is defined as p < 0.05, which is a < 5% chance that the decision to reject the null hypothesis is incorrect.
|T-test||Evaluates the difference in means between 2 study groups for a specific thing (called a variable)|
|Analysis of Variance [ANOVA]||Evaluates the difference in means between 3+ study groups for a specific variable|
|Correlation coefficient [r]||
Evaluates how to variables change in relation to each other.
Positive: variables increase or decrease similarly (both up or both down)
Negative: variables increase or decrease oppositely (one up, one down)