Lecture 11 Flashcards
Baseline groups
Comparison groups should be similar in terms of baseline characteristics.
Age, gender, ethnicity, variables of interest
Selecting a specific statistical test to perform requires some basic considerations about:
Type of
variable (independent vs dependent)
data (continuous vs discrete)
distribution (Normal/gaussian vs skewed)
Define variables
Representation of measures in an analysis
Can either be independent (the intervention) or dependent (the response)
Independent variable
The intervention. Defines the condition under which the dependent variable is measured. Experiementer controlled.
Independent variable affects change in the dependent variable.
The dependent variable
Response. Outcome or endpoint being measured or tested.
Continuous/infinite data
Infinite number of equally spaced values are possible. Measured. Cant count how many water drops are coming out of a hose, must measure it in L
Discrete/limited data
Limited number of values possible within the range of measurement. Counted, like single drops coming out of a faucet.
Defining data by scales
Ratio/interval scales
Ordinal scales
Nominal scales
Ratio or interval scales
Uniform intervals between consecutive measurements. Used to measure continuous data. Ex: 1 Km.
Ordinal scales
Rank of specific order where the interval values may not be known or constant. used to measure discrete data.
Ex: Leikert scale. Hot, hotter, hottest
Continuous data is measured by ____
Ratio/interval scales
Discrete data is measured by ____
Ordinal scales
Nominal scales
Used when data cannot be ordered, but values are discrete. Ex: age, race, gender. Named data.
1 dependent variable + no independent variable is what kind of analysis
Univariate analysis. Does not explain any relationship. Summarize, describe, look for patterns, normal distribution. NOT explanatory.
1 dependent variable + 1 independent variable is what kind of analysis
Bivariate analysis
Two variables. Compare.
1 dependent variable + 2 independent variables is what kind of analysis
Multivariate analysis
More than two variables. Evaluate relative importance.
Examples of univariate analysis
Mean, median, mode, range, variance/dispersion, standard deviation, bar charts, histogram, pie chart
Relationships evaluated during a bivariate analysis
Presence, strength, significant, descriptive or inferential.
Independent on X, Dependent on Y
What can you evaluate during a multivariate analysis
Relative importance.
Regression with a dependent variable: Linear (continuous) or logistic (dichotomous). Can model and form predictions. Strong statistical test.
Correlation. When no dependent variable: Relationship or connection. Not as strong as regression.
Difference between regression and correlation (both multivariate analysis)
Regression has a dependent variable. Strong statistical test.
Correlation does not have a dependent variable. Weaker statistical test.
n vs N
n samples
N entire population
Distribution of sample population measures
Central tendency.
Normal/gaussian distribution
Symmetrical, bell shaped distribution of values where the mean represents the highest point of the curve and whose spread is defined by the standard deviation.
Gaussian/normal curve.
__ represents the highest point of the curve
Spread is defined by ____
Mean, SD
