MCRS LECTURES 5-10

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  • units of analysis/cases subjects or objects that are mentioned (respondents, newspapers etc)
  • variables characteristics of units of analysis
  • values possible categories per characteristics (gender, age, education level etc)
  • measurable definition of a concept description of the meaning of a concept in research that is as accurate as possible
  • concept construct or variable
  • manifest (measured variable) can be directly measured or observed
  • latent variable variable that is not directly observed (requires multiple definitions to be measured)
  • reliability consistency (is it replicable)
  • validity is it right?
  • range maximum value minus minimum value
  • interquartile range "middle ground of dispersion" ignoring top and bottom 25%
  • Standard Deviation (SD) square root of variable
  • why do we use SD to make generalizations about a wider population from which we've drawn our sample and to calculate probabilty of any particular result occuring
  • Variance step 1 subtract mean value for the group from each individual value
  • variance step 2 square each result (to eliminate problem of dealing with negative number)
  • variance step 3 & 4 add the results , divide he sum of squares by number of values minus 1 to get an average of the squared variations from the mean
  • z-score number of units of SD any one value is above or below the mean
  • why do we use z-scores it allows us to compare numbers from different measuring systems
  • z-score interpretation the larger the z-score, the further its value from the groups mean and vice versa
  • nominal measurement and methods Mode (pie chart, bar graph, frequency table)
  • ordinal measurement and methods mode, median (bar graph, frequency table)
  • interval measurement and method mode, median, MEAN (histogram)
  • ratio measurement and method mode, median, MEAN (histogram)
  • central tendency mode mean median
  • normal distribution condition 1 always bell-shaped
  • normal distribution condition 2 sample size should be at least 100
  • example of normal distribution exam grades, age, income
  • skewness left or right skew. adds extremescores or outliers to the sample. makes mean and median different from a normal distribution
  • what measure of central tendency if distribution is skewed? median
  • negative value on skewness left-skewed
  • positive value on skewness right skew
  • nominal variable dispersion no dispersion
  • at least ordinal variable dispersion range
  • at least interval variable dispersion standard deviation, variance, deviance (error)
  • dispersion how different is each score from the center of a distribution (arithmetic mean)
  • sample group where you get your data from
  • deviance (error) negative value below the mean minus mean score (x^i - xBar)
  • sum of squared errors (ss) indicates total dispersion or total deviance of scores from the mean
  • average dispersion variance
  • normal distribution percentage 2,5% 13,5% 34% 34% 13,5% 2,5%
  • why do we use standard normal distribution to visualise data in same type of distribution (through z-scores)
  • correlations are (1) a way of measuring the extent to which 2 variables are related, a measure of the degree of association (symmetry) among variables
  • correlations are (2) indicates if variable changes in predictable manner as another variable changes, examines if one increases, the other also increases/decreases/stays the same
  • pearson product moment correlation (Pearsons R) degree of association between 2 scale (interval, ratio) variables
  • covariance when 2 variables covary. knowing how one variable changes helps in predicting how another variable changes
  • covariance interpretation when variables covary, they are related to some extent (correlation among variables)
  • -1 or +1 correlation perfect correlation
  • <.3 correlation weak
  • .3-.5 correlation moderate
  • >.5 correlation strong
  • coefficient of determination squaring one value of r you get the proportion of variance in one variable shared by the other
  • r2 coefficient of determination
  • sign of correlation (+ or -) indicates direction of relationship
  • exploratory factor analysis (data reduction technique) measure latent constructs, search for patterns (dimensions/factors), establish one or more factors/dimensions
  • what kind of data is correlation valid for numerical data
  • 1 item measured on a 5-point scale use ordinal categorization
  • 1 item on a 7-point scale use interval categorization
  • 2 or more items measured on 5/7 point scale use interval categorization (if true zero)
  • validity definition accuracy of the measure
  • reliability definition consistency of the measure
  • coefficient is the same as? communalities
  • common variance variance that a variable shares with other variables
  • unique variance variance that is unique to the particular variable
  • common cause among variables indication for latent factor
  • uninteresting factor loadings >.3 eigen value
  • multiple regression 2 independent variables and 2 dependent variables measured on interval level
  • simple regression 1 scale is used
  • what if there are 2 dependent variables 2 simple regression analyses
  • what rotation method do we use? oblique (direct oblimin)
  • when do we use direct oblimin to rotate factors? when we expect a relationship between factors
  • steps for factor analysis 1. EFA 2. Crohnbach's alpha, check if recoding negatives is necessary 3. Create variable/scale/composite mean (compute variable) 4. Descriptives (mean and sd)
  • Correlation is not causality
  • this variable explains the relationship between two variables mediator variable
  • non-spuriousness nothing except X must be influencing Y
  • goal of measurement aim for highest level of measurement
  • categorical measurement nominal/ordinal (cannot calculate categories or ranking)
  • numerical (continuous) ratio/interval
  • crohnbachs alpha reliability analysis, (important to reverse code negative variables)
  • why do we use factor rotation because we always expect underlying dimensions of a latent construct to correlate, which is why we use oblique rotation)

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