A Step-by-Step Guide to Data Analysis with Stata: Case of the Entry Poll#

Here’s a practical walkthrough of our entry poll dataset.

Preparing Your Dataset#

  1. Download the Dataset: First, ensure you have the dataset by right-clicking this link and choosing to download the .csv file containing entry-poll results to your machine.

  2. Import the Dataset in Stata: Use Stata’s graphical interface at Menu > File > Import > Text data (delimited, *.csv), or execute the command below. Remember to adjust the file path to where you’ve saved the .csv file on your computer.

import delimited "/Users/d/Downloads/entry_poll.csv", bindquote(strict) varn(1) clear //edit file path as needed

  • Understanding the Options:

    • bindquote(strict) ensures that quotes within your data do not disrupt the import process.

    • varn(1) treats the first row as variable names, making your data more readable.

    • clear clears any existing data in memory, preventing any data merging issues.

These options were determined through trial and error, highlighting the importance of experimenting to understand the nuances of data importation.

Inspecting and Cleaning Your Data#

  1. Inspect Your Imported Data:

list in 1/3
di c(N)
di c(k)
ds

This will give you a preliminary view of your data, including the number of observations and variables.

  1. Removing Redundant Rows: If you did not use varn(1), the first row might still contain your variable names, not data. Remove it with:

drop in 1

  1. Conduct Basic Descriptive Statistics:

codebook

This command provides a summary of each variable, including the number of missing values and unique categories.

Refining Your Dataset#

  1. Renaming Variables for Clarity: Some variables may have names that are too long or not intuitive. Rename them to make your dataset easier to navigate.

rename (howwillyouusestatafrom0326202405 whatoperatingsystemwillyouuseloc doyouhaveanyexperienceusingstata) (location os skill)

This more efficient syntax renames multiple variables in a single command, saving time and effort.

  1. Addressing Anomalies with Encoding:

desc
encode location, generate(local_remote)
encode os, generate(mac_windows)
encode skill, generate(experience)

Encoding converts text data into numeric data, facilitating statistical analysis. It also helps in simplifying the categories within variables.

  1. Data Cleaning Continued:

tabulate local
tabulate local, nolabel

These commands help in understanding the distribution of responses, both with and without labels.

Visualizing and Understanding Your Data#

After cleaning, it’s crucial to visualize your data to uncover any patterns or insights. Use Stata’s graphing capabilities to explore your data further.

histogram local
histogram mac
histogram experi

Let’s rotate the graphs so that they lay horizontally:

histogram local, horizontal
histogram mac, horizontal
histogram experi, horizontal

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Leveraging GPT-4 for Analysis#

GPT-4 Assisted Analysis

GPT-4 produced this analysis in less than a minute. It’s virtually impossible for you to replicate that speed and accuracy, as we’ve just experienced