School

University of GuelphDepartment

Sociology and AnthropologyCourse Code

SOAN 2120Professor

Scott SchauThis

**preview**shows half of the first page. to view the full**2 pages of the document.**Quantitative Analysis

-numerical representation and manipulation of observations for the purpose of describing and

explaining the phenomena that those observations reflect

Coding in Quantitative Data Analysis-getting the data from the subjects into a format that we can

manipulate and understand

Coding

-for computers to work, you must translate your data into something that they can read

-coding schemes are guided by theory

-codebook-document that describes the location of variables w/in a data set and lists the codes

assigned to the attributes composing those variables

[i.e. a computer doesn’t understand M & F so we say that males could be 0 and females are 1]

Purposes:

-primary guide used in the coding process

-guide for locating variables and interpreting codes in the data fine during analysis

Data Entry

-data entry specialists enter the data into statistical software of Excel spreadsheet

-optical scan sheets

-sometimes it’s part of the process of data collection

Data Cleaning

Possible-code cleaning

-codes which have not been assigned to the attributes of a variable are removed

Contingency cleaning

-checking that only those cases that should have data entered for a particular variable do in fact have

such data

Univariate Statistical Analysis [1 variable]

-the examination of the distribution of cases of only 1 variable [usually for descriptive cases]

-one-way frequency distribution

Measures of Central Tendency

-measure of the “average” or “typical” value of a variable

-mode: most frequent value

-mean: the division of the sum of all the attributes of a variable by the total number of cases

-median: middle value in the ranked distribution of observed values

Formulas

odd sample: median=[(n+1)/2]th observation [i.e. if we have 11 numbers-> n=11 (n+1=12) (12/2=6)]

even sample: median= midpoint between (n/2)th and [(n/2)+1]th observation [i.e. we have 10 numbers

n=10 (n+1= 11) (11/2=5.5) ]

Examples:

below is the number of times a sample of 20 students had Kraft dinner throughout the semester

7 2 16 4 0 6 13 4 5 12 0 1 3 9 8 45 3 9 0

Mean= 7+2+16+7+4+0+6+13+4…+9 / 20 = 7.7

-interpret your results: the mean number of times these 20 students in the sample had Kraft dinner is

7.7 times

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