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Garson, G. D. (2016). Case Study Analysis & QCA. Asheboro, NC: Statistical Associates Publishers.
 

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Overview
 
Table of Contents
 
ISBN: 978-1-62638-044-8

ASIN number (e-book counterpart to ISBN): ASIN: B007UWXBH0.
 

@c 2016 by G. David Garson and Statistical Associates Publishers. worldwide rights reserved in all languages and on all media. Permission is not granted to copy, distribute, or post e-books or passwords.
 


Overview

An illustrated graduate-level introduction to case study research, including qualitative comparative analysis (QCA), also known as configurational analysis, and covering information metrics methodology.

NEW IN THE 2016 EDITION

LIST OF ADVANTAGES OF QCA FOR CASE STUDY RESEARCH
1.	It is useful for causal explanation, leading to theory development.
2.	QCA supports theory testing by determining if expected patterns conform to 
generalizations based on observed cases. QCA also supports policy decision-making by 
uncovering both necessary and sufficient conditions needed for a desired outcome.
3.	It handles various types of causality, including asymmetric causality and multi-
conjunctural causality (where the causal effect reflects a combination  of  multiple 
causes -  a  "causal  package" -  rather  than  a  single  cause). 
4.	Where correlational approaches tend to dismiss low-correlated causal variables, the 
set-theoretic asymmetrical approach of QCA can identify strong set theoretic relationships 
involving limited subsets of case.  Where correlative 
studies may yield null findings, it is possible QCA may identify strong patterns involving 
the same variable and even situations where presence of the variable is important in one 
causal pattern while absence of the variable is important in a second causal pattern.
5.	QCA incorporates counterfactual reasoning. The complex, parsimonious, and 
intermediate solutions of QCA in effect engage in "thought experiments" which treat 
"remainders" (logical causal patterns with no observed cases) differently, either excluding 
them (complex solution), including those which simplify the solution (parsimonious 
solution), or include those which simplify the solution and which are consistent with 
researcher-specified causal assumptions (intermediate solution). 
6.	QCA promotes recognition of equifinality (multiple causal paths leading to the same 
outcome). QCA is thus particularly salient when the researcher has reason to think that 
more than one causal path may lead to an outcome of interest and that the effect of any 
given condition may only come to light when that condition is associated with certain other 
conditions.
7.	QCA involves causal asymmetry and often will show that the causal conditions 
associated with presence of the outcome are different from the causal conditions associated 
with the absence of the outcome. Multiple causal 
configurations may be associated with either high or with low membership in a given outcome 
state. Understanding multiple asymmetrical causal relationships often is more helpful to 
both theory building an policy analysis that are quantitative "percent of variance 
explained" solutions.
8.	As a corollary, models in QCA need not assume unidirectional causality or be 
recursive as is required for most models using standard quantitative modeling techniques. 
9.	For ordinal and interval data in fuzzy set QCA, data are calibrated rather than 
using the usual raw data usual in quantitative social science. Calibration 
incorporates external standards and external knowledge about cutting points for inclusion 
and non-inclusion of cases in membership in an attribute, and about cross-over (tipping) 
points for likelihood of membership or non-membership. Unlike uniform 
methods of measurement in correlational analysis, calibration in QCA may be different for 
different cases as appropriate based on external knowledge).
10.	 Where correlational methods may dismiss a causal variable if its additive net 
effect is small when previously-entered variables are controlled, QCA takes a more holistic
approach which shows how combinations of causal variables work in sets.
11.	Generalization is possible whether the number of cases is small, medium, or large.
12.	 QCA lends itself to set-theoretic graphical analysis, in which Venn-type diagrams 
are extended to include causal arrows representing "necessary" and "sufficient" causal 
conditions. 
13.	QCA affords one useful basis of analysis in triangulation and multi-method 
approaches, which are often thought to be more valid than monomethod research designs.
14.	 QCA can be used to supplement quantitative research, as by identifying variables 
to include in a quantitative model or by spotlighting mediating variables.

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Below is the unformatted table of contents.

CASE STUDY ANALYSIS & QCA
Example datasets	6
Overview	6
Key Concepts and Terms	7
Types of case studies	7
Research designs for case study research	9
The grounded exploratory design	9
Counterfactual comparison design	10
Experimental single case research design (SCRD)	11
Cross-sectional comparison design	11
Longitudinal comparison design	12
Dynamic comparison design	12
Case selection strategies	13
Data collection	16
Data collection methods	16
Linear or iterative?	18
"Thick description"	18
Constant comparison	22
Case quality control	23
Pattern matching	24
Overview	24
Congruence testing	25
Explanation-building	25
Terminating data collection	25
Process tracing	27
Overview	27
Controlled observation	27
Time series analysis	28
Critical incident technique (CIT)	28
Meta-analysis	29
Overview	29
Implementation considerations	31
Quantitative meta-analysis of cases	33
Example: DOE (2010)	35
Configurational (QCA) analysis	37
Overview	37
Types of QCA	42
Coverage vs. consistency	42
Dealing with inconsistency and contradictions	46
QCA: Limits, criticisms, and assumptions	47
fsQCA software	49
Crisp set QCA	50
Crisp set QCA example 1	50
Crisp set QCA example 2	67
Crisp set QCA example 3	88
Longitudinal pattern matching	91
Research design considerations	91
Fuzzy-set QCA	96
Overview	96
Fuzzy set QCA example 1	97
Fuzzy set QCA example 2	109
Internal structural heterogeneity index (ISHI	110
Qualitative and Multi-Method Research periodical	112
Information metrics / structured-focused comparison	113
Data	113
Overview	113
Entropy, conditional entropy, and uncertainty	114
Information metrics example	115
Statistical packages for the uncertainty coefficient	122
Information metrics complements QCA	128
Assumptions of case studies methods	131
Frequently Asked Questions	131
What are common standards for case studies based dissertations?	131
Is Institutional Review Board/Use of Human Subjects approval necessary for case study research?	133
Is case study research a social science substitute for scientific experimentation?	133
Aren't case studies unscientific because of researcher bias?	133
Aren't case studies unscientific because findings cannot be generalized?	135
Is a "case history" the same as a "case study"?	135
Are all case studies forms of case study research?	135
Is a joint or team approach to case study research preferred over single-investigator research?	136
What is a "consortium benchmarking" case study?	136
How is case study research related to complexity science?	137
What is nVivo?	137
What is xSight?	138
What is Leximancer?	138
What type of sampling is required in QCA?	138
How many predictor variables can QCA handle?	139
How does calibration work in fsQCA?	140
What are "remainders" in QCA?	143
How does the QCA algorithm simplify the truth table?	144
How is QCA related to cluster analysis?	146
How are hierarchical data handled in QCA?	147
What support exists for QCA?	148
What QCA software is available?	149
Bibliography	151
Acknowledgments	162
Page count: 164