|
Question |
Answer |
Purpose of descriptive statistics - introduce start learning
|
|
Introduce the players of the game (the variables that play a role in the analysis), inform the reader of the nature of the variable (discrete, continuous, own research etc.).
|
|
|
|
start learning
|
|
- Nominal – only categories, no ranking - Ordinal – ordered categories, distances not clear - Interval – ordered/ranking, equal distances assumed - Ratio – ordered/ ranking, equal distances assumed, absolute zero, no negative numbers
|
|
|
|
start learning
|
|
extent to which measure correctly represents the concept of the study Internal validity – how well the study was done External validity – are the results generalized to other situations
|
|
|
|
start learning
|
|
measure that indicates the symmetry of a distribution compared to a normal distribution
|
|
|
|
start learning
|
|
tailedness of a distribution – how many observations are in the tails compared to normal distribution
|
|
|
|
start learning
|
|
Proportion of the variation y that is explained by the linear combination of the x variables
|
|
|
|
start learning
|
|
adjust for the number of predictors in the model – how well the model fits the data, corrected by the degrees of freedom
|
|
|
|
start learning
|
|
overall significance of the model
|
|
|
|
start learning
|
|
categorical variable with only two values, 0 and 1. Value 1 satisfies a condition
|
|
|
|
start learning
|
|
explanatory variables that are introduced in the regression model in order to assess or clarify the relationship between two or more variables (hypothesized relationship)
|
|
|
|
start learning
|
|
if u estimate the models including those variables, you can solve the problem of multicollinearity
|
|
|
|
start learning
|
|
high correlation between at least two independent variables.
|
|
|
|
start learning
|
|
Uneven distribution of errors in the scatterplot, i.e., different variances for different observations (e.g., groups of observations have different variances, or the variance could depend on the size of the observation).
|
|
|
|
start learning
|
|
the DV influences the IV Reverse causality occurs when the dependent and independent variables have been gathered at the same point of time. The time dimension is neglected and cause does not precede effect.
|
|
|
|
start learning
|
|
correlation between the explanatory variables and error term
|
|
|
|
start learning
|
|
iv in a regression model is not correlated with an error term
|
|
|
|
start learning
|
|
relationship between X predictor and Y outcome depends on a third variable Z moderator
|
|
|
|
start learning
|
|
a mediator explains the relationship between two variables
|
|
|
Logistic regression rather than OLS? start learning
|
|
if the dependent variable is a categorical (usually, binary) variable
|
|
|
Exploratory and confirmatory factor analysis start learning
|
|
EFA is used to identify and quantify factors or latent variables, whereas CFA is used for testing hypotheses about the structures of those latent variables
|
|
|
|
start learning
|
|
correlation between a factor and variable, can take values between -1 and 1 due to a correlation
|
|
|
Main uses of factor analysis start learning
|
|
- To understand the structure of the set of variables in the analysis - To analyze a questionnaire to measure the underlying (latent) variable - To reduce the dataset to a manageable size while retaining as much of the original information as possible
|
|
|
|
start learning
|
|
- proportion of variance that a variable share with other variables in the analysis
|
|
|
|
start learning
|
|
measure of how much of the common variance (communality) of the observed variables a factor explains.
|
|
|
Purpose of descriptive statistics - problems start learning
|
|
- Signal possible problems and things to keep in mind, e.g. large standard deviations (why are those a problem? - large heterogeneity, might be two different subpopulations). Whether there are negative values or not, truncated distribution
|
|
|
