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SOAN 3120 (35)
Lecture

# September 10.docx

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School
Department
Sociology and Anthropology
Course
SOAN 3120
Professor
Michelle Dumas
Semester
Fall

Description
September 10, 2013 – Quantitative Methods – SOAN 3120*02 Week 1: Chapter 1 – Picturing Distributions Research – Why conduct research? - we want to know something - explore a particular phenomenon Methods - Analysis – ask questions, figure out how to get answers, collect data, analyze if you have quant. Data you use statistical means to analyze and interpretive means for qualitative. Theory – can help guide us on way we ask our questions, how, what we are looking for and our lit. review sets up what has been asked before and research done before From hypothesis, you collect data, you have observations, could be sending out a survey, conducting interviews, etc. From the data, you have empirical generalizations. General concepts and terminology - variable o independent – the cause – causing something o dependent - the effect, the results, the consequence - definitions o conceptual – descriptive in words o operational – how you are going to measure that variable – eg. how many groups of different classes are you going to include? - statistics o descriptive – giving description to what the numbers are telling us – describing our data o inferential - making generalizations for the population Variables: Quantitative - quantitative: numerical values o can do arithmetic, add, subtract them – recorded in units of measurement (seconds, kilograms, etc.) - counts o like the number of individuals registered in this class! They are a whole number. - Amounts o Ratios  Eg. GDP per capita  Non-negative  Don’t need to be whole numbers, can be fractions or have decimal places  Also called ratio variables  Meaningful to have a ratio which is essentially two values divided by one value into the other value - relative frequencies September 10, 2013 – Quantitative Methods – SOAN 3120*02 o proportions, percents and rates – they have a minimum and maximum value – we look at, for example, the rate of crime – the number of times something happens as a portion of a population set o eg. infant mortality:  1000x number of children dying in first year divided by the number of live births - interval scales – arbitrary unit of measurement and have arbitrary zero point – temperature – degrees Celsius – essentially we can compare differences in temperature, how they differ from one place or season to another – 0 on a Celsius scale does not represent no heat Variables: Categorical - qualitative or nominal variables o categories that usually are mutually exclusive – only fit into one category and make it fairly exhaustive that you have to fit into at least one category – other, none of the above or decline to answer – region ethnicity, race, gender – no intrinsic order to these categories on surbey or way we use them – one is not better then the other – we just happen to have different categories – marital status as well. - ordinal variables o categories that have a natural order o rate our satisfaction on a particular service or product and they ask us to rate our satisfaction levels – does have natural order in that some things are different then the others – strongly agreed versus strongly disagree or neutral – may be assigned a number but only for analysis Question: Variables If we asked people to report their weight, would that variable be considered a categorical or quantitative variable? – QUANTITATIVE We ask people whether their weight is underweight, normal, overweight or obese. IS this variable categorical or quantitative? – CATEGORICAL Aspects of Statistics - data analysis: patterns o describing data – use visual representation – graphs, charts – sometimes that doesn’t make sense – can still have tables where you have numerical representations of your data – look for patterns in data – what does it look like – more females then males? – more people who finished university than did not? – what is happening and being able to describe that – most interesting looks at patterns happening that you wouldn’t expect – the relationship between variables
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