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Midterm exam review Quantitative .doc

3 Pages

Social Science
Course Code
SSCI 2910U
Nicole M

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Univariate statistics: - describe patterns & trends, doesn’t test relationships - bar charts: visual representation for nominal/ordinal variables, use % to compare - histograms: for interval-ratio variables, bars represent range of values, height corresponds to number of unstandardized count (Column Variable) Independent variable: cause or antecedent of dependent variable (Row Variable) Dependent variable: effect or consequence of independent variable nominal frequencies  mode (# and valid %) ordinal frequencies  mode bar chart (%) (#and valid %) median interval­ratio frequencies  mode histogram  (#and valid %) median (normal curve) mean Mode: most frequent value (no mode, unimodal = 1 mode, bimodal = 2 modes) limitation: have no mode or too many modes = lose statistical value meaning Median: midpoint of distribution, only for ordinal and interval-ratio variables limitation: doesn’t account for extreme high or low response scores Mean: average response score, only interval ratio variables limitation: highly affected by extreme scores Normal curve: standard spread of data - unimodal (one mode), symmetrical (equal sides), equal measures of central tendency (mode, median and mean are all same) Skewness: comparison of 2 sides horizontally, split by the mean symmetry: lack of skew, normal curve with even distribution, equal to 0 positive skew: disproportion of high scores, mean > median, value greater than 0 negative skew: disproportion of low scores, mean < median, value less than 0 Kurtosis: comparison of vertical distribution mesokurtic: lack of kurtosis, k = 3 platykurtic: flat, k < 3 leptokurtic: peaked, k > 3 range: amount of data b/w minimum & maximum response score variance: average amount deviate from mean, same as squared standard deviation standard deviation: average distance deviate from mean, same as square root variance Bivariate Analysis: - 2 variables, use cross tabulations, assess the nature of the relationship b/w variables - explaining variables, propose hypothesis, examine conditional distributions - test for relationship b/w 2 nominal/ordinal variables - recode interval ratio data into nominal/ordinal Conditional distribution: display distribution
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