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Erschienen in:
Introduction to Modern Survey Analytics Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

4. Beginning Deep Survey Analysis

verfasst von : Walter R. Paczkowski

Erschienen in: Modern Survey Analysis

Verlag: Springer International Publishing

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Abstract

I had divided the analysis of survey data into Shallow Analysis and Deep Analysis. The former just skims the surface of all the data collected from a survey, highlighting only the minimum of findings with the simplest analysis tools. These tools, useful and informative in their own right, are only the first that should be used, not the only ones. They help you dig out some findings but leave much buried. I covered them and their use in Python in the previous chapter.

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Vorheriges Kapitel Shallow Survey Analysis
Nächstes Kapitel Advanced Deep Survey Analysis: The Regression Family
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Fußnoten
2
As a personal anecdote, I once had a client who wanted to know if a difference of one cent in the prices of two products was significant—the products were selling for about $10 each.
 
3
To be “stat tested” as many like to say.
 
4
I once did some survey analysis work for a large food manufacturing company (to remain nameless) that used α = 0.20.
 
5
Economists refer to this as a perfectly competitive market. All firms in such a market are price takers, meaning they have no influence on the market price. Therefore, there is only one market price.
 
6
It is easy to show that for \(\bar {X} = {1}/{n} \sum (X_i - \bar {X}) = 0\).
 
7
It can actually be any level. As you will see, however, the first levels is dropped by statsmodels.
 
8
The cross-product term cancels after summing terms.
 
9
Of course, the military branch does not determine your age. But the age distribution varies by branch is the main point.
 
10
\(\binom {7}{2} = \dfrac {7!}{2! \times 5!} = 21\).
 
11
QA7: “Did you ever serve in a combat or war zone?” There is a clarifying statement: “Persons serving in a combat or war zone usually receive combat zone tax exclusion, imminent danger pay, or hostile fire pay.”
 
13
In the Design of Experiments literature, a treatment is an experimental condition placed on an object that will be measured. The measure is the effect of that treatment. The objects may be grouped into blocks designed to be homogeneous to remove any nuisance factors that might influence the responses to the treatments. Only the effect of the treatments is desired. In the survey context, the treatments are the CATA questions, and the blocks are the respondents themselves. See Box et al. (1978) for a discussion of experimental designs.
 
14
See the article “Cochran’s Q test” at https://​en.​wikipedia.​org/​wiki/​Cochran%27s_​Q_​test. Last accessed September 30, 2020.
 
16
The original data had “Yes” = 1, “No” = 2, and “Don’t Know” = 3.
 
17
“In mathematics, specifically set theory, the Cartesian product of two sets A and B, denoted A × B is the set of all ordered pairs (a, b) where a is in A and b is in B.” Source: Wikipedia article “Cartesian product”: https://​en.​wikipedia.​org/​wiki/​Cartesian_​product. Last accessed on October 2, 2020. For this problem, the collection of branches is one set, and the collection of gender is another.
 
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Metadaten
Titel
Beginning Deep Survey Analysis
verfasst von
Walter R. Paczkowski
Copyright-Jahr
2022
Buch
Modern Survey Analysis

Print ISBN: 978-3-030-76266-7

Electronic ISBN: 978-3-030-76267-4

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-76267-4_4