Difference between revisions of "Iekdensity"
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* To install the package, type <syntaxhighlight lang="Stata" inline>ssc install ietoolkit</syntaxhighlight> in the Stata command box. | * To install the package, type <syntaxhighlight lang="Stata" inline>ssc install ietoolkit</syntaxhighlight> in the Stata command box. | ||
== Overview == | == Overview == | ||
[https://www.worldbank.org/en/research/dime/data-and-analytics DIME Analytics] has developed the <code>[[Ietoolkit|ietoolkit]]</code> package for Stata to simplify the process of [[Data Management|data management]] and [[Data Analysis|analysis]] in impact evaluations. Along with <code>iefieldkit</code>, this package allows [[Impact Evaluation Team|research teams]] to perform highly repetitive but important processes in [[Primary Data Collection|primary data collection]], with an aim to promote high quality [[Reproducible Research|reproducible research]]. | |||
Impact evaluations often involve comparing the effects of an intervention on two groups - '''control group''', and '''treatment group'''. The '''control group''' is the group which does not receive an intervention, while the '''treatment group''' is the group which receives a particular intervention. Whether a particular [[Unit of Observation|unit of observation]] falls in the control group or the treatment group is assigned [[Randomization Inference|randomly]]. This allows the [[Impact Evaluation Team|research team]] to judge the impact of a particular intervention. | |||
In some cases there can be multiple '''treatment arms''' - that is, different groups receive different levels of intervention. For example, in a study to assess the impact of cash transfers on farmer productivity, there could be one control group, and 3 treatment groups - each receiving cash transfers of 20$, 30$, and 40$ respectively. This can help the researchers to assess not only whether cash transfers increase farmer productivity, but also which amount has the maximum positive impact (if any). | |||
The <code>iekdensity</code> command in the <code>ietoolkit</code> package allows the users to easily plot the distribution of a variable based on the treatment group. This allows researchers to easily visualize the impact on a variable of interest quickly using graphs in Stata. | |||
== Options == | == Options == | ||
== Examples == | == Examples == | ||
== Related Pages == | == Related Pages == | ||
== Additional Resources == | == Additional Resources == | ||
Revision as of 14:43, 8 March 2021
Iekdensity is a Stata command, which is part of the ietoolkit package developed by DIME Analytics. Iekdensity allows users to easily plot the distribution of a variable based on treatment groups. It also allows users to include additional information such as descriptive statistics and treatment effects for key variables.
Read First
- DIME Analytics has developed the
ietoolkitpackage for Stata to simplify the process of data management and analysis in impact evaluations. - Stata coding practices lists common best practices for writing reproducible and replicable Stata do-files.
- You can contribute to improving future updates of
ietoolkitusing this Github repository maintained by DIME Analytics. - To install the package, type
ssc install ietoolkitin the Stata command box.
Overview
DIME Analytics has developed the ietoolkit package for Stata to simplify the process of data management and analysis in impact evaluations. Along with iefieldkit, this package allows research teams to perform highly repetitive but important processes in primary data collection, with an aim to promote high quality reproducible research.
Impact evaluations often involve comparing the effects of an intervention on two groups - control group, and treatment group. The control group is the group which does not receive an intervention, while the treatment group is the group which receives a particular intervention. Whether a particular unit of observation falls in the control group or the treatment group is assigned randomly. This allows the research team to judge the impact of a particular intervention.
In some cases there can be multiple treatment arms - that is, different groups receive different levels of intervention. For example, in a study to assess the impact of cash transfers on farmer productivity, there could be one control group, and 3 treatment groups - each receiving cash transfers of 20$, 30$, and 40$ respectively. This can help the researchers to assess not only whether cash transfers increase farmer productivity, but also which amount has the maximum positive impact (if any).
The iekdensity command in the ietoolkit package allows the users to easily plot the distribution of a variable based on the treatment group. This allows researchers to easily visualize the impact on a variable of interest quickly using graphs in Stata.