<p>If you dont want to used DID, a simple cross sectional analysis would do with the addition of a dummy variable to indicate whether the individual was treated in period $t+1$ or not (implying they were treated in period $t$</p>

<p>for example if you were trying to see if  test scores of students change depending on when they have their class on monday or tuesday. your data set would be:</p>

<p>$$
\begin{array}{c|lcr}
n &amp; \text{Test score} &amp; \text{IQ} &amp; \text{Class on monday} \\
\hline
1 &amp; 60 &amp; 80 &amp;0\\
2 &amp;80 &amp;65 &amp; 1 \\
3 &amp; 90&amp; 70 &amp; 0
\end{array}
$$</p>

<p>By the incorporation of dummies you can just use regression methods for cross sectional data.</p>

CC BY-SA 3.0
2017-11-03T17:04:19.043Z
2017-11-03T19:48:21.700Z
2017-11-03T19:48:21.700Z
EconJohn
<p>I am trying to study the effects of a program, where everyone gets treated. I have 50% of the population in <strong>t</strong> and the rest of the 50% in <strong>t+1</strong> period. I understand this gives me a slight opportunity of creating a control group for just one time period and run something like DID. Or I can run a Segmented TS analysis.</p>

<p>I was wondering if there is any better way?</p>

CC BY-SA 3.0
2017-11-04T11:11:09.833Z
Welcome to the site.  I think the terminology of this question might be improved.  Is there a null hypothesis you want to test?  Is 'everyone' really your population of interest, or is it just a sample about which you happen to have data?
Adam Bailey
CC BY-SA 3.0
2017-11-06T14:58:30.687Z
@AdamBailey Thank you for the comment, I am new to the site. My dataset is from a survey which has very specific questions that I require for my research question/s. Hence everyone is my population of interest. My question maybe answered if I just consider a single point in time, but I feel there may be some time trends to the impact as well. So my wonder as to if there is any way to study the impact when the treatment (the survey) affects everyone in the dataset. I have quite a few pre treatment waves of data.
FightMilk
<econometrics>
Best econometric models to fit when there is no control group?
FightMilk
# Best econometric models to fit when there is no control group?

(Asked by: username_0 on 2017-11-03)

I am trying to study the effects of a program, where everyone gets treated. I have 50% of the population in **t** and the rest of the 50% in **t+1** period. I understand this gives me a slight opportunity of creating a control group for just one time period and run something like DID. Or I can run a Segmented TS analysis.

I was wondering if there is any better way?

**Tags:** econometrics
> **Comments:**
> **username_1**: Welcome to the site.  I think the terminology of this question might be improved.  Is there a null hypothesis you want to test?  Is 'everyone' really your population of interest, or is it just a sample about which you happen to have data?  
> **username_0**: @AdamBailey Thank you for the comment, I am new to the site. My dataset is from a survey which has very specific questions that I require for my research question/s. Hence everyone is my population of interest. My question maybe answered if I just consider a single point in time, but I feel there may be some time trends to the impact as well. So my wonder as to if there is any way to study the impact when the treatment (the survey) affects everyone in the dataset. I have quite a few pre treatment waves of data.  

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## 1 answers

### Answer by username_2 on 2017-11-03. Score: 1

If you dont want to used DID, a simple cross sectional analysis would do with the addition of a dummy variable to indicate whether the individual was treated in period $t+1$ or not (implying they were treated in period $t$

for example if you were trying to see if test scores of students change depending on when they have their class on monday or tuesday. your data set would be:

$$
\begin{array}{c|lcr}
n & \text{Test score} & \text{IQ} & \text{Class on monday} \\
\hline
1 & 60 & 80 &0\\
2 &80 &65 & 1 \\
3 & 90& 70 & 0
\end{array}
$$

By the incorporation of dummies you can just use regression methods for cross sectional data.


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