FINAL lecture notes.docx

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Department
Sociology and Anthropology
Course
SOAN 2120
Professor
David Walters
Semester
Fall

Description
Lecture Note Information Purpose of the Course: The purpose of this course is to provide an introduction to research methodology in the social sciences. This class will cover both qualitative and quantitative approaches to social research. Quantitative Vs. Qualitative: Quantitative – applied statistics, numbers Qualitative – interviews, research, everything else Experiments Vs. Surveys: Experiments – very controlled environment – Pretest, Treatment, Post-test Survey – ask them a number of q‟s – do not manipulate subjects – each question on survey represents a variable Types of Variables: Quantitative Variables – continuous (rank), numerical (e.g. education… #of years of schooling and income…# money) Categorical – non-rank order, non-numerical (e.g. religion… Catholics do not rank higher than protestants and race…skin colour) Distributions: When the mean is equal to the median (the average is equal to the middle), this is a normal distribution. Normal distributions allow us to conduct significance tests. The distribution of a variable using data from our sample is called sample distribution. The distribution of the variable in the population is called population distribution. The distribution of the mean of a variable from all possible samples is the sampling distribution. Sampling distribution – as its standard deviation gets smaller as the size of the sample get larger. Sampling distribution – based on calculating a statistic over and over again from sample samples of the same size. Measures of Central Tendency: Mean, Mode, Median, Range Refresher: mean = average, mode = most occurring, median = middle (when written out from least to greatest), and range = largest number subtracted by smallest number Variance/Standard Deviation: (N = so in other words, the sample size) Standard deviation is the square root of the variance. To calculate the standard deviation: 1) calculate the mean 2) for every observation: a. first observation minus mean = value. Square that value = new squared value. b. Second observation minus mean = value. Square that value = new squared value. c. … d. … e. last observation minus mean = value. Square that value = new squared VALUE. 3) Add all the squared values to new a new value (sum of squared deviations). 4) Divide that value by the sample size (n) minus 1. Validity and Reliability: (Kayley – go to Brad‟s notes for this – UoG notebook) Unbiased/Sampling Error: The difference between the statistic and the parameter due to random process is known as sampling error. Random samples have less error than nonprobability samples. The sampling error decreases as sample size goes up. Central Limit Theory: - regardless of the population distribution, with repeated sampling, the shape of the sampling distribution is approximately „normal‟ (or bell shaped) with the population parameter at its centre. - ?? Law of Large Numbers: - the larger the samples, the more the confidence because it‟s closer to the population estimate Z-Scores - z scores = take „x‟, subtract the mean and divide by the standard deviation –!Tis a specified valuece between two values –!Test for the difference between •!Testtwo valuesd on the sampling •!Tests are based on the samplingmany standard deviations an observation is from the mean P-Values •!z-score-u* = p
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