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# Chapter 14: Quantitative Data Analysis

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## Danielle Ostrosky

on 18 November 2013

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#### Transcript of Chapter 14: Quantitative Data Analysis

Quantitative Data Analysis
Nicole Morrison & Danielle Ostrosky
Main Idea
Overview of Important
INFORMATION
Univariate Analysis
Bivariate Analysis
Quantitative Analysis
Discussion!
& Any questions?

Babbie, E. (2010). The Practice of Social Research. Belmont, CA: Wadsworth Publishing Company.

THANK YOU! :)
Distributions
Combining Terms
Seen as an extension of bivariate analysis
Can construct a multivariate analysis table by essentially following the same steps outlined for bivariate analysis
rather than just one independent and dependent variable, there will be more than one independent variable
2 basic approaches
: well-developed scheme, or generate code from data
How you code is important!
Subgroup Comparisons
Quantitative Data Analysis
Multivariate
Analysis
"The numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect" (p. 414).
Developing Code Categories
Codebook Construction
Data Entry
End product
= conversion of data items into numerical codes
Serves 2 essential functions
: used in coding process & guide to locating variables
Convert data to machine-readable format (process done before quantitative analysis)
The most basic format
Frequency distribution
Central Tendency
Way to present data in form of an average
Mean, median, and mode
Dispersion
Standard deviation= sophisticated measure of dispersion
Interquartile range= measures range of scores for he middle 50% of subjects
Continuous vs. Discrete Variables
Continuous= ratio variable
Discrete= jumps from category to category without intervening steps
Quantitative
ANALYSIS
Univariate
ANALYSIS
Detail vs. Manageability
Subgroup
COMPARISONS
Bivariate
ANALYSIS
Multivariate
ANALYSIS
Want to have a lot of detail, but also be able to manage it
"The analysis of a single variable, for the purposes of description" (p. 418).
Quantitative Analysis
Univariate Analysis
Subgroup Comparisons
Bivariate Analysis
"The analysis of two variables simultaneously, for the purpose of determining the empirical relationship between them" (p. 430).
Examples: construction of simple percentage table, or computation of simple correlation coefficients
Examples: frequency distributions, averages and measures of dispersion
Multivariate Analysis
"The analysis of the simultaneous relationships among several variables"
(p. 434).
Examples: examining effects of age, sex and social class on religiosity
when a small percentage of respondents select the two extreme responses, you should combine, or "collapse" the categories
Handling "Don't Knows"
Good idea to give the option of saying "don't know" when asking for opinions on issues
Able to separate and recalculate percentages excluding the "don't knows"
Numerical Descriptions
Numerical testing can often verify the findings of the in-depth, qualitative studies
Steps in construction of explanatory bivaraiate table:
1. Cases divided into groups according to independent variable
2. Subgroups described in terms of dependent variable
3. Table read by comparing independent variable subgroups with one another in terms of given attribute of dependent variable
Chapter 14
"[They] tell us how different groups in the population responded to [a] question. You can undoubtedly see a pattern in the results, though possibly not exactly you expected" (p. 426).
Presenting Tabular Data
1. Descriptive title
2. Original content is clearly presented
3. Attributes of each variable are clearly indicated
4. When percentages are reported in table, the base they are computed on should be indicated
5. Report all cases' data, even if they are missing data (ex. "no answer")
Quantification
:
the process of converting data to a numerical format (p. 414).
Full transcript