English
English
WEBVTT
00:00:00.900 --> 00:00:03.450
Multiple case study is
a very common approach
00:00:03.450 --> 00:00:05.130
to qualitative research.
00:00:05.130 --> 00:00:07.542
In this video, I will talk about Kathleen Eisenhardt's approach
00:00:07.557 --> 00:00:10.625
to the multiple case studies.
00:00:10.650 --> 00:00:12.610
In contrast to quantitative research
00:00:12.610 --> 00:00:14.710
where there are often
right and wrong choices
00:00:14.710 --> 00:00:18.250
when it comes to methods, qualitative research is more diverse.
00:00:18.270 --> 00:00:20.820
And it's more difficult
to make those choices
00:00:20.820 --> 00:00:23.480
and you really need to
understand what you're doing.
00:00:23.480 --> 00:00:29.249
And it requires a lots of expertise to justify your research approach.
00:00:29.260 --> 00:00:34.915
However, to get started people need easy to follow rules and guidelines.
00:00:34.950 --> 00:00:40.082
To this end, many researchers have started to imitate others
00:00:40.100 --> 00:00:41.800
and this has led to the emergence
00:00:41.800 --> 00:00:44.730
of two templates in management research.
00:00:44.730 --> 00:00:48.100
We have the Eisenhardt
method and the Gioia method
00:00:48.100 --> 00:00:54.289
and these have been basically emerged, when researchers have started to look at
00:00:54.300 --> 00:00:58.247
who has been able to publish qualitative research in leading journals
00:00:58.250 --> 00:01:01.450
and then started to imitate these authors.
00:01:01.450 --> 00:01:04.650
This Eisenhardt's method is sometimes
00:01:04.650 --> 00:01:08.210
called post positivist, I prefer to call it realist.
00:01:08.210 --> 00:01:11.280
The idea here is that the method
00:01:11.280 --> 00:01:16.280
is more about facts and
events that people describe
00:01:16.770 --> 00:01:21.330
instead of being about how people interpret those descriptions.
00:01:21.340 --> 00:01:23.880
So whereas the Gioia method
is about understanding
00:01:23.880 --> 00:01:26.710
how people interpret
the events around them,
00:01:26.710 --> 00:01:28.120
in Eisenhardt's method,
00:01:28.120 --> 00:01:33.829
we are interested in events themselves and the way people interpret those events
00:01:33.850 --> 00:01:37.190
can produce bias to our analysis results.
00:01:37.190 --> 00:01:42.245
So we try to be more objective whereas Gioia method we try to understand
00:01:42.280 --> 00:01:45.620
the people's subjective use
on the events around them.
00:01:45.620 --> 00:01:47.150
There are also other differences.
00:01:47.150 --> 00:01:53.120
In Eisenhardt's method the analysis proceeds a bit differently.
00:01:53.140 --> 00:01:58.380
The approach requires multiple cases, typically four to 10 different cases.
00:01:58.380 --> 00:02:02.240
So for example, if you
study power and politics
00:02:02.240 --> 00:02:05.310
which is done in an
example paper that I use,
00:02:05.310 --> 00:02:09.810
then you need to pick a couple of organizations where power is,
00:02:09.810 --> 00:02:11.420
where politics is applied
00:02:11.420 --> 00:02:15.110
and couple of organizations
where politics is applied less
00:02:15.110 --> 00:02:19.020
and then you analyze those
cases and even compare.
00:02:19.020 --> 00:02:21.930
The analysis here in Eisenhardt's method
00:02:21.930 --> 00:02:25.210
is focused on first on
within-case analysis.
00:02:25.210 --> 00:02:27.160
So you try to understand each case,
00:02:27.160 --> 00:02:31.909
write the description about the case and then you compare across the cases
00:02:31.950 --> 00:02:36.950
to come up with generalizable
theory and propositions.
00:02:37.030 --> 00:02:40.790
So this is much more about
objective description and explanation
00:02:40.790 --> 00:02:47.242
of reality instead of being about people's interpretation of that reality.
00:02:47.270 --> 00:02:52.060
These also differ in, how
you report the results
00:02:52.060 --> 00:02:55.090
and how you write the paper.
00:02:55.090 --> 00:02:59.658
In Eisenhardt's method there is this quantitative focus.
00:02:59.690 --> 00:03:02.250
Kathleen Eisenhardt's papers quite often
00:03:02.250 --> 00:03:07.657
classify cases according to dimensions as being high and low.
00:03:07.660 --> 00:03:11.699
For example, you can classify the cases into high performing companies
00:03:11.730 --> 00:03:16.574
and low performing companies or companies that are in the middle.
00:03:16.580 --> 00:03:21.198
And then you seek associations between different variables.
00:03:21.200 --> 00:03:23.640
So this is a much more
quantitative approach
00:03:23.640 --> 00:03:26.430
to qualitative research
than the Gioia method,
00:03:26.430 --> 00:03:30.610
which is more about writing
stories on what happened.
00:03:30.610 --> 00:03:34.030
And importantly, the Eisenhardt methods,
00:03:34.030 --> 00:03:38.130
while you develop propositions
that are statements
00:03:38.130 --> 00:03:43.947
about causality between two concepts, you need to explain the causal process.
00:03:43.960 --> 00:03:47.520
So she emphasizes that a study,
00:03:47.520 --> 00:03:52.520
a multiple case study
must provide explanations,
00:03:52.720 --> 00:03:57.650
not only associations and claims, but also explanations of how, why,
00:03:57.650 --> 00:04:01.190
and when those causal processes work.
00:04:01.190 --> 00:04:02.750
Let's take a look at an example.
00:04:02.750 --> 00:04:05.620
So my example is Eisenhardt
and Bourgeios 1988,
00:04:05.620 --> 00:04:08.480
power and politics in
high-velocity environments.
00:04:08.480 --> 00:04:13.210
And how does Kathleen Eisenhardt describe their data analysis process?
00:04:13.210 --> 00:04:17.904
Importantly, the data analysis process needs to be described transparently
00:04:17.920 --> 00:04:22.737
because there is no clear guideline or no clear standards
00:04:22.760 --> 00:04:26.010
on how you apply different techniques.
00:04:26.010 --> 00:04:28.540
Whereas in quantitative
research you could easily say
00:04:28.540 --> 00:04:31.200
that you apply regression
analysis and that's it,
00:04:31.200 --> 00:04:33.540
in qualitative research
there's much more freedom
00:04:33.540 --> 00:04:34.820
on how you analyze your data.
00:04:34.820 --> 00:04:38.819
And therefore you need to explain how exactly you did it.
00:04:38.860 --> 00:04:41.840
So the data analysis starts
00:04:41.840 --> 00:04:44.360
by quantifying your data
and seeking patterns.
00:04:44.360 --> 00:04:49.430
So quite often in multiple case study following the Eisenhardt approach,
00:04:49.430 --> 00:04:53.950
you have some concepts in your mind before you collect the data.
00:04:53.950 --> 00:04:58.010
And then you simply code for evidence of those concepts
00:04:58.010 --> 00:04:59.680
and different levels of those concepts.
00:04:59.680 --> 00:05:04.000
For example, power and
politics in this paper.
00:05:04.000 --> 00:05:07.560
Then you seek associations between power and politics are the correlated.
00:05:07.560 --> 00:05:11.710
If so then you seek
evidence for causality.
00:05:11.710 --> 00:05:15.190
Developing profiles is something that Eisenhardt are also recommends.
00:05:15.190 --> 00:05:20.170
So you've write a case description for each case, or it's executive,
00:05:20.170 --> 00:05:22.290
and you can do the same for each decision.
00:05:22.290 --> 00:05:25.380
So you can write profiles,
00:05:25.380 --> 00:05:31.130
short descriptions of the key things or key units that you study.
00:05:31.130 --> 00:05:33.620
Then you develop timelines.
00:05:33.620 --> 00:05:37.730
You can have a piece of
paper that has a timeline
00:05:37.730 --> 00:05:40.700
then you put the key
events on that timeline.
00:05:40.700 --> 00:05:46.720
And you basically
construct causal processes
00:05:46.720 --> 00:05:50.760
or descriptions of some kind of processes using those timelines.
00:05:50.760 --> 00:05:53.080
Then once you have developed
00:05:53.080 --> 00:05:56.070
the case descriptions,
developed the timelines,
00:05:56.070 --> 00:05:58.460
then you start to compare pair-wise.
00:05:58.460 --> 00:06:02.520
For example, you could take pairs, you take a high performing company
00:06:02.520 --> 00:06:07.100
a low performing company, you compare and you try to find differences.
00:06:07.100 --> 00:06:11.700
Or if you have two high-performing companies, you try to find similarities.
00:06:11.700 --> 00:06:14.120
And this way you try to find patterns
00:06:14.120 --> 00:06:20.980
that explain, how the cases differ or how they're similar with one another.
00:06:20.980 --> 00:06:26.480
Then you iterate this many, many times and from this a theory emerges.
00:06:26.480 --> 00:06:30.160
So your first seek patterns then based on those patterns
00:06:30.160 --> 00:06:34.882
you start to seek evidence for causality and you iterate many, many times
00:06:34.882 --> 00:06:37.320
because sometimes when you find a pattern
00:06:37.320 --> 00:06:42.310
in for example two cases, that pattern may not exist in other cases.
00:06:42.310 --> 00:06:46.560
And therefore might not be the ideal pattern to follow
00:06:46.560 --> 00:06:49.070
when you construct your theory.
00:06:49.070 --> 00:06:51.450
Finally, you compare with prior research
00:06:51.450 --> 00:06:57.210
to seek for things that are similar and look for things
00:06:57.210 --> 00:07:00.330
that are conflicting with your theory.
00:07:00.330 --> 00:07:04.530
This explanation of data analysis also contains two other important
00:07:04.530 --> 00:07:06.050
and interesting things.
00:07:06.050 --> 00:07:09.230
First, there are conflicts.
00:07:09.230 --> 00:07:12.260
So people will not always
tell you the same thing.
00:07:12.260 --> 00:07:17.440
Even if people observe the same events they interpret the events differently
00:07:17.440 --> 00:07:20.960
and people are not objective in how they explain reality.
00:07:20.960 --> 00:07:25.080
Instead they explain their own interpretations of the reality.
00:07:25.080 --> 00:07:26.550
And in Eisenhardt's approach,
00:07:26.550 --> 00:07:29.710
you try to eliminate the influence of a person's interpretation
00:07:29.710 --> 00:07:31.830
of reality from the analysis.
00:07:31.830 --> 00:07:34.370
So if you have conflicting evidence
00:07:34.370 --> 00:07:38.470
then you start looking
at what is the reason
00:07:38.470 --> 00:07:39.850
why there's a conflict?
00:07:39.850 --> 00:07:44.550
Why is one person telling us one thing, another person telling us another thing?
00:07:44.550 --> 00:07:47.200
And then based on those
conflicting accounts
00:07:47.200 --> 00:07:51.170
you try to infer what is
the reality actually like.
00:07:51.170 --> 00:07:55.100
Then another thing that
is useful to understand
00:07:55.100 --> 00:07:59.476
is that the patterns are another laws.
00:07:59.476 --> 00:08:05.250
So the fact that most of
the cases that applied here,
00:08:05.250 --> 00:08:09.090
that applied politics
had centralized power.
00:08:09.090 --> 00:08:13.600
Power centralization does not always lead to the use of politics.
00:08:13.630 --> 00:08:16.940
So you need to understand that it's not a perfect correlation
00:08:16.940 --> 00:08:19.500
like it's never in a
quantitative analysis.
00:08:19.500 --> 00:08:23.400
Instead we look for
some kind of association
00:08:23.400 --> 00:08:27.800
that may not be always strong they can be weak as well.
00:08:27.800 --> 00:08:29.460
This is how Eisenhardt herself
00:08:29.460 --> 00:08:34.460
describes the process in
the seminar, 1989 paper.
00:08:34.700 --> 00:08:37.210
So Eisenhardt's approach starts
00:08:37.210 --> 00:08:40.970
with a research question
like we always do.
00:08:40.970 --> 00:08:43.420
In contrast to some
grounded theory studies
00:08:43.420 --> 00:08:48.270
where we start from a clean slate, we don't have any ideas
00:08:48.270 --> 00:08:50.830
of what the research result might be.
00:08:50.830 --> 00:08:53.940
In Eisenhardt's approach
there is quite often
00:08:53.940 --> 00:08:58.940
some concepts that have been
chosen before the study.
00:08:59.180 --> 00:09:02.220
That we want to study the power, we want to study politics.
00:09:02.220 --> 00:09:07.298
So we need to code for evidence of power and evidence or politics in the data.
00:09:08.250 --> 00:09:11.650
Then selecting cases follows
theoretical sampling.
00:09:11.650 --> 00:09:15.310
So if we want to study the
effects of power and politics
00:09:15.310 --> 00:09:18.370
we should get cases
where there's variation
00:09:18.370 --> 00:09:20.580
in power and variation in politics.
00:09:20.580 --> 00:09:24.280
So if possible,
we should seek companies
00:09:24.280 --> 00:09:26.850
that we know have centralized power
00:09:26.850 --> 00:09:30.990
and companies that we know where
power is not centralized,
00:09:30.990 --> 00:09:34.430
and we can also do this while we sample.
00:09:34.430 --> 00:09:39.930
So let's say that we don't know for sure if cases have centralized power or not,
00:09:39.930 --> 00:09:44.700
and let's assume that
we have done four cases
00:09:44.700 --> 00:09:48.690
three have centralized power, in one the power is not centralized.
00:09:48.690 --> 00:09:53.120
Then on the remaining eight cases we should focus on finding companies
00:09:53.120 --> 00:09:55.630
where power is not centralized to the CEO.
00:09:55.630 --> 00:09:59.870
With other constructs at this performance it's much easier because you can get
00:09:59.870 --> 00:10:02.910
objective measures of those concepts
00:10:02.910 --> 00:10:05.990
without actually going in
and doing the interviews.
00:10:05.990 --> 00:10:09.380
Then quite often in this approach
00:10:09.380 --> 00:10:12.950
there are multiple ways
of collecting data.
00:10:12.950 --> 00:10:17.370
So you collect data through interviews but you can also use survey forms.
00:10:17.370 --> 00:10:23.050
So for example, if you want to study if power is centralized within the CEO,
00:10:23.050 --> 00:10:25.820
you can simply use a
power centrality scale
00:10:25.820 --> 00:10:30.108
in the form of a paper-based survey, which you give to the informant
00:10:30.108 --> 00:10:31.740
to fill before the interview.
00:10:31.740 --> 00:10:34.480
So this is a combination
of different kinds of data.
00:10:34.480 --> 00:10:39.480
You can use data also from
databases like financial data
00:10:40.070 --> 00:10:41.980
and you do the interviews.
00:10:41.980 --> 00:10:47.050
Then importantly data collection and analysis needs to overlap
00:10:47.050 --> 00:10:49.770
always in qualitative analysis
because you never know
00:10:49.770 --> 00:10:52.470
what all the concepts are while you start.
00:10:52.470 --> 00:10:55.210
So you might have an idea
of the two central concepts
00:10:55.210 --> 00:10:58.814
but if you want to study the process through which power influences politics
00:10:58.814 --> 00:11:01.760
you may not know what
the process actually is.
00:11:01.760 --> 00:11:06.300
And once you start to have an understanding, what is the process like?
00:11:06.300 --> 00:11:09.940
Then you can focus your
later data collection efforts
00:11:09.940 --> 00:11:11.610
on the important parts of the process
00:11:11.610 --> 00:11:14.310
that you discovered early
on in your research.
00:11:14.310 --> 00:11:17.750
Then you do analysis, analysis always starts with within-case analysis.
00:11:17.750 --> 00:11:19.800
So you analyze each case separately
00:11:19.800 --> 00:11:25.000
and then you do cross-case analysis or you do a pair-wise comparisons.
00:11:25.050 --> 00:11:29.510
And for example comparisons using more than two companies.
00:11:29.510 --> 00:11:36.230
Then follows the writing of the tables to show associations and then you write
00:11:36.230 --> 00:11:42.090
the narrative or description of why there are these relationships in the text
00:11:42.090 --> 00:11:49.050
and you enter quotes into the text that explain the process or explain the data,
00:11:49.050 --> 00:11:54.490
show the data that allowed you to infer that there actually is a process.
00:11:54.490 --> 00:11:59.080
And then finally you compare
against prior literature
00:11:59.080 --> 00:12:02.780
because this is typically done,
00:12:02.780 --> 00:12:07.780
to come up with a theory
between known concepts.
00:12:07.880 --> 00:12:12.410
So it's not very common to apply this kind of multiple case study
00:12:12.410 --> 00:12:16.220
and develop new concepts
using multiple case studies.
00:12:16.220 --> 00:12:20.370
So grounded theory is perhaps better for developing new concepts.
00:12:20.370 --> 00:12:25.760
This is more about finding associations and finding causal relationships
00:12:25.760 --> 00:12:30.400
between concepts that you choose before you collect the data.
00:12:30.400 --> 00:12:35.578
And finally, how do you know when do you quit adding more cases
00:12:35.578 --> 00:12:37.395
or stop adding more cases?
00:12:37.588 --> 00:12:40.570
Eisenhardt uses the concept
of theoretical saturation.
00:12:40.570 --> 00:12:44.070
So when you have a case
that no longer gives you
00:12:44.070 --> 00:12:47.330
more information to guide your theorizing
00:12:47.330 --> 00:12:50.930
then you can conclude that
you have reached saturation.
00:12:50.930 --> 00:12:55.030
So collecting more cases would not add much value anymore.
00:12:55.030 --> 00:12:59.140
So you add cases as long
as your theory develops
00:12:59.140 --> 00:13:01.950
but once you have the feeling
00:13:01.950 --> 00:13:05.610
that the latest case
did not really add much
00:13:05.610 --> 00:13:08.910
then you decide that this
is the number of cases
00:13:08.910 --> 00:13:10.770
that I'm gonna use.
00:13:10.770 --> 00:13:14.660
Quite often the number of cases is between eight and 10
00:13:14.660 --> 00:13:16.210
when this approach is followed.
Is current object generated automatically
