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SOC202H1 (21)

CH 4 textbook notes

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Scott Schieman

CH4 – MEASURING AVERAGES  CENTRAL TENDENCY STATISTIC (CTS) – provides an estimate of the typical, FREQUENCY DISTRIBUTION CURVES – A substitute for frequency histogram or usual or normal score found in a distribution of raw scores polygon in which we replace these graphs with a smooth curve. o Three central tendency statistics, each has strengths/weaknesses  AUC represents the total number of subjects in the population o Equal to a proportion of 1.00 or percentage of 100 percent. THE MEAN – The sum of all scores in a distribution divided by the number of  Smooth curve == estimate way scores are distributed in the population scores observed (sample size) o Not depict sample distribution  Most useful of central tendency statistic for summary of typical/avrg score  Horizontal axis = scores of a variable X; vertical axis = p/% frequency o Conceptually: mean = value of the scores in the distribution if every  NORMAL DISTRIBUTION – a frequency distribution curve in which the subject has the same score  “equal share” mean, median and mode of a variable are equal  Applicable to interval/ratio variables only o Distribution itself has bell shaped  To combine 2 different size samples o Median splits a ranked distribution of scores in half, symmetrical o Add sum of all scores of both groups divided by total sample size o Mode = center point = peak of distribution  Weakness: easily affected by extremes (lows/highs) Outliers o 1. Sensitive to values of the scores in a distribution  Can positively or negatively inflate sum of all scores o 2. Sensitive to sample size  Outliers especially problematic w/ smaller samples o Adjusted Mean – mean calculated w/ outliers removed THE MEDIAN – The middle score in a ranked distribution  SKEWED DISTRIBUTION – a frequency distribution curve in which the mean  Value which divides the distribution of scores in half median, and mode of the variable are unequal and many of the subjects o Half the case falls above, half the case falls below  Median is a location point, the middle position score have extremely high/low scores o Useful for when distribution is skewed, not as susceptible to outliers o Positively Skewed Distribution (Right Skew) – extreme scores in the  Different from midrange = halfway point btwn min. & max. values of X high or positive end of the score distribution  High extreme inflates mean, mode unaffected, median inbtwn  To calculate, first rank; then divided n by 2 & get near the middle score o If n is odd, median will be an actual sample o If n is even, median is average btwn the two middle scores o Larger sample:  Then find the value at that rank location o Negativ
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