WEBVTT 00:00:00.060 --> 00:00:06.870 The first strategy for making coastal claims using  quantitative data is a randomized experiment.  00:00:06.870 --> 00:00:11.520 The idea of randomized experiment  is that we have some population   00:00:11.520 --> 00:00:18.840 of interest from which we take a sample. Then we divide the sample into two randomly.  00:00:18.840 --> 00:00:22.590 One is called a treatment group and  another one is the control group.  00:00:22.590 --> 00:00:29.280 Because we select those two groups  randomly there are no differences   00:00:29.280 --> 00:00:32.850 statistically between these two groups. Or if there are some differences   00:00:32.850 --> 00:00:40.200 then it's due to chance only. This relates to back to our example of   00:00:40.200 --> 00:00:46.350 dividing the men and women-led companies into two  groups randomly to see if there's a difference.  00:00:47.640 --> 00:00:51.270 We divide these in to two groups  with treatment and control.  00:00:51.270 --> 00:00:55.740 Then we apply some kind of  treatment to the treatment group.  00:00:57.300 --> 00:01:01.980 Typically this example is from  medical research so this is   00:01:01.980 --> 00:01:08.640 applied in medicine and it's easy to understand. One group receives appeal, the other one doesn't.  00:01:08.640 --> 00:01:17.880 Then after let's say two days, we assume that  the effect, takes two days to be realized.  00:01:17.880 --> 00:01:24.420 We measure the health of these two groups,  we compare if the group that received appeal   00:01:24.420 --> 00:01:30.810 medicine is better than the second group. Then we conclude that there's a causal effect.  00:01:30.810 --> 00:01:37.920 The why this is a valid causal claim  is that these groups are perfectly   00:01:37.920 --> 00:01:42.240 comparable to start with because they  are randomly chosen from the same sample.  00:01:42.240 --> 00:01:49.290 Therefore the only plausible explanation beyond  chance for and a difference between the groups is   00:01:49.290 --> 00:01:58.680 that there is an actual effect of the treatment. This works well under certain conditions.   00:01:58.680 --> 00:02:04.590 So we need to have a random  assignment that's very important.  00:02:04.590 --> 00:02:10.470 If we have people who get to choose whether they  receive the medicine or not, then those people   00:02:10.470 --> 00:02:16.200 who are more sick will likely choose to be in  the treatment group than the control group.  00:02:16.200 --> 00:02:25.140 And then comparison here would confound the  selection effect of how people chose to be in   00:02:25.140 --> 00:02:30.570 these groups and the treatment effect. Then we have a large enough sample.  00:02:30.570 --> 00:02:35.160 And then some other assumptions  that are not as relevant.  00:02:35.970 --> 00:02:38.790 We have large enough sample that we  don't have to worry about chance,   00:02:38.790 --> 00:02:44.700 we have random assignment here and after  that we can compare the difference after   00:02:44.700 --> 00:02:49.620 receiving the medicine or the  treatment as causal effect.  00:02:49.620 --> 00:02:55.410 The randomization is important because  we want to show that this difference is   00:02:55.410 --> 00:03:03.780 because of the treatment and not because we  chose to assign the groups in a certain way.  00:03:03.780 --> 00:03:08.670 We want to show that there is the treatment  effect instead of a selection effect.  00:03:08.670 --> 00:03:16.200 Then we repeat this a couple of times and when  the study results have been verified independently   00:03:16.200 --> 00:03:22.500 or two times then we can sell our medicine.  And that's how randomized experiments work.  00:03:22.500 --> 00:03:27.240 Of course there are variations to  this design like you can compare,   00:03:27.240 --> 00:03:35.700 how health of an individual increases so  that would be a within individual study.  00:03:35.700 --> 00:03:38.970 This is a between individual  study but this is the base case.  00:03:38.970 --> 00:03:46.740 This is the simplest possible experimental design. Experimental designs are not always theasible   00:03:46.740 --> 00:03:51.990 they can be done in business studies but if we  study organizations then up line treatments to   00:03:51.990 --> 00:03:58.560 organizations could be difficult to organize. We also have a second best option called   00:03:58.560 --> 00:04:03.030 Quasi-experiment and the idea of a  quasi-experiment is that we have some   00:04:03.030 --> 00:04:09.270 elements of experimental approach but we  don't have the full experimental control.  00:04:09.270 --> 00:04:18.900 For example we could have separate sample pretest  and posttest. We have something for example,   00:04:18.900 --> 00:04:23.850 we know that we have a school and  the kids will receive a medicine.  00:04:23.850 --> 00:04:29.770 Everyone gets the medicine on one  day but we can't influence that.  00:04:29.770 --> 00:04:35.890 What we can do is that we randomize the  kids, we measure their health for half   00:04:35.890 --> 00:04:42.040 of the students before the treatment  for other half after the treatment.  00:04:42.040 --> 00:04:48.400 And then we assume that this after the treatment  group is otherwise comparable for them before   00:04:48.400 --> 00:04:52.960 the treatment group except for the treatment. So we assume that there are no time effects.   00:04:52.960 --> 00:04:58.960 And that would allow us to make a causal  claim based on quasi-experimental design.  00:04:58.960 --> 00:05:06.820 We can also have experiments where the choice  between treatment and control is not random.  00:05:06.820 --> 00:05:14.590 Either it would look like random we don't  have control on the randomization in which   00:05:14.590 --> 00:05:19.420 case we would assume that these samples  behave as if they were random samples.  00:05:19.420 --> 00:05:24.310 Or we can do some statistical adjustments  for this non-random selection.  00:05:24.310 --> 00:05:29.680 So that's nonequivalent control group design. Another one is interrupted time series design.  00:05:29.680 --> 00:05:35.740 So we follow some units or some  companies, people over time.  00:05:35.740 --> 00:05:41.290 Then there is an exogenous shock that  happens, so some kind of exogenous event.  00:05:41.290 --> 00:05:48.340 For example new regulation is implemented in  markets independently of these organizations.  00:05:48.340 --> 00:05:53.080 Then we can analyze what is the effect of  that new regulation on company performance.  00:05:53.080 --> 00:05:59.950 Assuming that the implementation doesn't in any  way depend on how these companies are doing.  00:06:00.550 --> 00:06:05.080 That's another quasi-experimental design.  So the idea of quasi-experimental design   00:06:05.080 --> 00:06:10.030 is that we have a treatment but we  don't have the full randomization.  00:06:10.030 --> 00:06:13.900 So something happens, something  is manipulated but we don't really   00:06:13.900 --> 00:06:21.370 have quite a full experimental design. Quasi-experiments are something that people   00:06:21.370 --> 00:06:25.240 overlook when they think about their designs. There's a great article in Organizational   00:06:25.240 --> 00:06:28.720 Research Methods about different  quasi-experimental designs.  00:06:28.720 --> 00:06:33.130 I would recommend that you consider these  when designing your studies because you can   00:06:33.130 --> 00:06:40.090 make really strong claims that are perhaps  more generalizable than lab experiments.  00:06:40.090 --> 00:06:44.390 Because quasi-experiments typically  take place in real-life settings.