• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

Eight Data Analysis Skills Every Analyst Needs

by Karen Grace-Martin 2 Comments

It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.

It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.

Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.

Statistical knowledge is necessary to even get started developing these skills. And as you develop the skills, you’ll find the statistics make more sense. But more statistical knowledge isn’t a substitute. So what are these important skills?

1. Planning the Data Analysis

Like most projects, data analysis projects are more efficient and have fewer issues if you have a plan. A plan requires you to think ahead on critical decisions that will be time-consuming to redo.

The correct statistical analysis depends on the research question, the study design, the variables, and any data issues. Considering how these work together before you start can save a lot of time and headaches during the analysis.

2. Managing the Data Analysis Project

Even if you are the only person running the data analysis you still have to manage the project itself. You need to keep track of files, allot enough time to each step, and find the resources you’ll need along the way.

3. Cleaning, coding, formatting, and structuring data

You’ve heard the term GIGO, right? Garbage In, Garbage Out. In data analysis, it’s the data that is going in. And it needs to be clean. Sparkling.

And once it’s clean, you need to code and format the variables. Then you need to structure it correctly for the planned data analysis.

This step often takes much, much longer and requires more statistical software skills than the data analysis itself.

4. Running analyses in an efficient order

There is a specific order in which to run the steps of your analysis and there are decisions to make at every step.

If you do them out of order or hit a roadblock you can’t solve, the analysis will be slower and more frustrating. More importantly, you’re likely to make mistakes.

5. Checking assumptions & dealing with violations

Yes, every statistical test and model has its own assumptions. So the content of this skill will differ depending on what you’re running. But the general approach to checking assumptions is the same. And there is a lot of skill in reading uncertain situations and drawing conclusions.

6. Recognizing and dealing with data issues

Real data are messy data.

Real data have issues that make the analysis hard. Outliers, small sample sizes, and truncated distributions happen in all types of data sets. Recognizing when a data issue is happening, knowing whether it’s bad enough to cause problems, and knowing what do to about it are important skills.

7. Detecting problematic results and troubleshooting

Sometimes you get weird results — really weird results — despite cleaning data, checking assumptions, and looking for data issues.

There are so many possible causes: typos in the data set, glitches in the software, missing data.

The skill here is being able to recognize when something is wrong and how to investigate the issue and the solutions.

8. Interpreting, Presenting, and Communicating Results

This set of skills may be the most important of all. It includes interpreting results and writing them so the audience understands. It requires creating useful, appropriate, and accurate graphs and tables.

This also means knowing how to get your stat software to do the heavy lifting for you. If you can create the tables the way you need, you don’t have to spend hours reformatting them.

How to develop these skills

No data analyst starts out with these skills, no matter how many statistics classes they’ve taken. There is only one way to develop these skills: it takes experience analyzing real data sets.

But the skill development comes easier if you have specific training on these skills and someone to mentor you as you gain experience. Think of yourself as an apprentice of a skilled trade. Yes, you can do it on your own. But you will improve your skills faster and produce better work along the way with some guidance.

 

The Pathway: Steps for Staying Out of the Weeds in Any Data Analysis
Get the road map for your data analysis before you begin. Learn how to make any statistical modeling – ANOVA, Linear Regression, Poisson Regression, Multilevel Model – straightforward and more efficient.

Tagged With: checking assumptions, Data Analysis, data anlyst, data cleaning, data issues, graphs, interpreting, Research Question, researcher, results, Study design

Related Posts

  • Four Weeds of Data Analysis That are Easy to Get Lost In
  • What to Do When You Can’t Run the Ideal Analysis 
  • Best Practices for Data Preparation
  • Member Training: Data Cleaning

Reader Interactions

Comments

  1. Oyeleye Bolarinwa says

    September 27, 2020 at 10:06 am

    Thanks very much for simplifying presentation to enhance understanding. You are highly appreciated.

    Reply
    • Karen Grace-Martin says

      March 2, 2021 at 3:40 pm

      Thanks, Oyeleye!

      Reply

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Please note that, due to the large number of comments submitted, any questions on problems related to a personal study/project will not be answered. We suggest joining Statistically Speaking, where you have access to a private forum and more resources 24/7.

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Analyzing Pre-Post Data

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT