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Chapter 8: Quantitative Data Analysis
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Terms in this set (24)
Quantitative Data Analysis
The numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena those observations reflect.
The most most common in social science
2 Basic Approaches Coding Categories
1. The use of a well-developed coding scheme (as used in previous research)
2. The generation of codes from one's data
Coding Example 1:
To generate codes from one's data, one can code responses in terms of what general category responses represent.
Financial Concerns : Selecting the items that apply.
1. Tuition too high
2. Not Enough Parking
3.Faculty don't know...
4. too many requirement
5. book cost too much
6. not enough financial aid
Coding Example 2:
Here student responses are coded as academic or nonacademic issues.
Coded as academic or non-academic: Select one or the other.
Same items above
Codebook
Document used in data processing and analysis that tells the location of different data items in a data file. Codebooks describe:
1. The identifiers assigned to different variables
2. The codes assigned to the attributes of those variables
Examples Codebooks
Identity Standard Measures:
Section Text: In thinking about who you are, please
select the number below that best reflects the degree to
which you see yourself as having each of these
characteristics.
idslike Likeable
0 = Not at all, 1, 2, 3, 4, 5, 6 = Very Much
idsint Intelligent
0 = Not at all, 1, 2, 3, 4, 5, 6 = Very Much
idsmor Moral
0 = Not at all, 1, 2, 3, 4, 5, 6 = Very Much
Univariate analysis
Analysis of a single variable, for purposes of description (not explanation).
1. frequency distributions,
2. central tendency
3. measures of dispersion.
E.g. a univariate analysis of the variable gender represents the number of men and women in a sample.
Frequency Distribution
Description number of times various attributes of a variable are observed in a sample.
Central Tendency
Average, representing the mean, median, or mode.
EX: Birth rate
Number of Children Born (N=2,825)
X F
0 802
1 474
2 743
3 + 411
total: 2825
Central Tendencies
1. Mean; an average computed by summing the values of several observations and dividing by the number of observations.
2. Median: an average representing the value of the "middle" case in a rank-ordered set of observations.
3. Mode; an average representing the most frequently observed value or attribute.
Univariate Analysis Definitions
1. Dispersion represents how "spread out" a distribution of values is around a central value (i.e. average).
2. Standard deviation (SD) is a common measure of dispersion: 68% of all cases lie within +/- 1 SD from the mean, 95% within +/- 2 SDs, and 99.9% within +/- 3 SDs.
e.g of dispersion Gold Score: High SD
High= Standard deviation is spread out (wider graph)
e.g Low SD
Tightly clustered values (high peak graph)
In presenting data, one is constrained by two (often contradictory) goals:
1. Presenting the fullest degree of data detail
2. Presenting data in a clear, coherent, manageable form
Subgroup Comparison
A basic way to analyze or present data is to examine how different subgroups of one's sample compares on specific questions.
If some attributes of a variable are under- represented (i.e. there aren't many cases in the attribute), one can collapse attribute categories.
E.g. imagine a 5 point Likert scale with the following results: 3% SA, 29% A, 23% N, 41% D, 4% SD
▪ The SA and SD categories are extremely small, proportionate to the others. One could collapse these categories and combine them with A and D respectively (the new results: 32% A, 23% N, 45% D).
Omitting "Don't Know" Answers
"Don't know" answers can be problematic in data analysis, because they aren't particularly useful in most instances.
One can omit "don't know" responses and simply analyze the proportions among respondents who did answer a specific way.
Bivariate analysis is the analysis
Analysis of variables simultaneously, for the purpose of determining an empirical relationship between them.
Bivariate analysis is often explanatory; it is used to test whether a hypothesis is true regarding the effect of an independent variable on a dependent variable.
Bivariate Table Construction Steps
1. Cases are divided into groups according to the attributes of the independent variable.
2. Each of these subgroups is then described in terms of attributes of the dependent variable.
3. Finally, the table is read by comparing the
independent variable subgroups with one another in
terms of a given attribute of the dependent variable.
Bivariate Table Interpretation
Determine the logical direction of the relationship (which variable is independent vs. dependent).
2. If a table is percentaged down, read it across.
3. If a table is percentaged across, read it down.
31.
e.g., create a table to examine how gender influence the IV and DV and write % of weekly, less often and equal to it to 100.
E.g Table Constration and separating data steps:
1. The cases are divided into men and women.
2. Each gender subgroup is described in terms of approval or disapproval of sexual equality.
3. Men and women are compared in terms of the percentages approving of sexual equality.
Guidelines for presentation of tables:
1. A table should have a heading or title that describes what is contained in the table.
2. Original content should be clearly presented,
3. The attributes of each variable should be clearly indicated.
4. The base on which percentages are computed should be indicated.
5. Missing data should be indicated in the table.
Multivariate analysis
The analysis of simultaneous relationships among several variables.
E.g. the examination of the effects of age, gender, and social class on religiosity.
Develop a graph with # of religious service attendance, gender and age... options of # of weekly, less often and add the total percentage
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