Quantitative research II - Preparing data

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Week 7
Nikolas Bosin
FlashCards por Nikolas Bosin, atualizado more than 1 year ago
Nikolas Bosin
Criado por Nikolas Bosin aproximadamente 5 anos atrás
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Resumo de Recurso

Questão Responda
What is a suitable statistical software for large data sets? Stata
What is a suitable statistical software for open source? R
What is a statisctical software that is graphically oriented? SPSS
Three important points about the preperation for data analysis are... - Coding - Editing - Not to overwrite original dataset
What is "coding"? Coding describes process through which survey answers, texts, etc. are transformed into processable numbers
How do we call answers (e.g. in surveys) that are not processable? Missing values
What are values to describe the central point of data? - Mode (even applicable to nominal variables) - Median (expects at least categorical variables) - Mean (only makes sense for metric variables)
What are values to describe the spread around the central point? - Variance - Standard deviation - Range
Formula for the standard error SE = s/(√n)
Formula for covariance Gebe den Text hier ein...
Formula for Pearson's correlation coefficient Gebe den Text hier ein...
How do you call a score that is obviously deviant from the remainder of the data set? Outlier
Why do we use data reduction? To express the same amount of information with less data
What are two data reduction techniques? - Factor analysis (groups variables) - Cluster analysis (groups observations)
What is "factor analysis"? Factor analysis tries to identify a set of common underlying dimensions, known as factors, in a group of variables (--> Questions about how confident someone is in a personality test should give information about their openness --> here openness is the factor and the questions are the variables)
There are two different types of factor analysis, which are ... - Exploratory factor analysis - Confirmatory factor analysis
The three steps of exploratory factor analysis are... 1. Examine which variables are correlated by writing them in a correlation matrix 2. Extract factors from the variables 3. Factors are rotated to maximise the relationships between the variables and some of the factors
How many factors do we obtain? There are three rules of thumb to tell... - Kaiser criterion (factors with eigenvalues >1) - Percetage of variance criterion - Elbow criterion
What to do with the results of a factor analysis? - Study cross loadings (cross loadings = when one variable loads on multiple factors) - Asses reliability of factors through Cronbach's alpha (>=0,7 is good)
Whats the formula for Cronbach's alpha? Gebe den Text hier ein...
What is the goal of the cluster analysis? To assign observations to homogeneous clusters
What clustering algorithms exist? - K-means clustering - Hirarchical clustering

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