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Chapter 8

School

University of GuelphDepartment

Sociology and AnthropologyCourse Code

SOAN 2120Professor

William WaltersChapter

8This

**preview**shows pages 1-2. to view the full**7 pages of the document.**Chapter Eight

Coding Data

Coding: Systematically reorganizing raw numerical data into a format that is

easy to analyze using computers.

Codebook: A document (one or more pages) describing the coding

procedure and the location of data for variables in a format that a computer

can use.

Pre-coding: Placing code categories on the questionnaire. If this is not done,

the first step is to create a codebook.

Entering Data

There are four ways to get raw quantitative data into a computer:

Code Sheet: Gather the info, and then transfer it from the original

source onto a code sheet. Then type line by line into computer.

Direct-Entry Method, Including CATI: Type the information or get

the respondent to type the information in the computer. The

computer must be preprogrammed to accept answers.

Optical Scan: Enter the information onto optical scan sheets (like a

Scantron), which will then transfer the info into the computer.

Bar Code: Convert the info into different widths of bars associated

with specific numerical values; use a bar code reader to transfer the

info into the computer.

Cleaning Data

After careful coding, the researcher verifies the accuracy of coding, or

“cleans” the data.

Researchers verify the codes in two ways:

Possible code cleaning (or wild code checking): involves checking

the categories of all variables for impossible codes

Contingency Cleaning (or Consistency Checking): involves

cross-classifying two variables and looking for logically impossible

combinations

Results with One Variable

Frequency Distributions

Descriptive Statistics: Numerical data

Can be categorized by number of variables involved

(univariate, bivariate, or multivariate)

Univariate: describes one variable. To describe this, we use

frequency distribution (nominal-, ordinal-, interval- or

ratio-level data and takes many forms)

Histograms, bar charts and pie charts are great representation

of these.

Only pages 1-2 are available for preview. Some parts have been intentionally blurred.

For interval or ration level data, info is often formed into

categories and is plotted in a frequency polygon. (Frequency

along y-axis, values of variable/score around x-axis)

Measures of Central Tendency

Mode: Most common/frequent number

Median: Middle number once arranged numerically or if there’s two middle

numbers, both numbers added then divided by 2.

Mean: All numbers added then divided by the number of numbers.

Measures of Variation

Definition: A one-number summary of a distribution (its center).

Distributions also have spread, dispersion or variability

Two distributions can have identical measures of central tendency but

different spreads.

Variation

Zero Variation: means that every person in the population has the

median and mean value.

Measured in three ways

Range: Largest and smallest scores. E.g. a bar has ages 19-26 in

it. The range is then 7.

Percentile: Tell the score at a specific place within the

distribution. The median is the 50th percentile.

Standard Deviation: Requires the interval or ratio level of

management. Gives an average distance between all scores

and the mean.

Normal distribution looks like a hill

How to calculate standard deviation (recap of in-class notes)

1. Compute the mean

2. Subtract the mean from each score

3. Square the resulting difference for each score

4. Total up the squared differences to get the sum of squares.

5. Divide the sum of squares by the number of cases minus 1 to

get the variance

6. Take the square root of the variance, which is the standard

deviation

Calculating Z-Scores

1. Z-Score = (Score-Mean)/Standard Deviation

---------------------------------------------

Standard Deviation

Results with Two Variables

Bivariate Statistics: Let a researcher consider two variables together and

describe the relationship between variables.

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