Data Analysis

Four Weeds of Data Analysis That are Easy to Get Lost In

Every time you analyze data, you start with a research question and end with communicating an answer. In fact, defining that research question is vital to getting to the right answer.

But in between those start and end points are twelve other steps. I call this the Data Analysis Pathway. It’s a framework I put together years ago, inspired by a client who kept getting stuck in Weed #1. But I’ve honed it over the years of assisting thousands of researchers with their analysis.

Member Training: Data Cleaning

by guest

Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.

Eight Data Analysis Skills Every Analyst Needs

by Karen Grace-Martin 1 Comment

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.

Member Training: A Data Analyst’s Guide to Methods and Tools for Reproducible Research

by guest 2 Comments

Have you ever experienced befuddlement when you dust off a data analysis that you ran six months ago?

Ever gritted your teeth when your collaborator invalidates all your hard work by telling you that the data set you were working on had “a few minor changes”?

Or panicked when someone running a big meta-analysis asks you to share your data?

If any of these experiences rings true to you, then you need to adopt the philosophy of reproducible research.

Member Training: Mixture Models in Longitudinal Data Analysis

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This webinar will present the steps to apply a type of latent class analysis on longitudinal data commonly known as growth mixture model (GMM). This family of models is a natural extension of the latent variable model. GMM combines longitudinal data analysis and Latent Class Analysis to extract the probabilities of each case to belong to latent trajectories with different model parameters. A brief (not exhaustive) list of steps to prepare, analyze and interpret GMM will be presented. A published case will be described to exemplify an application of GMM and its complexity.

Finally, an alternative approach to GMM will be presented where the longitudinal model approach is linear mixed effects (also known as hierarchical linear model or multilevel modeling). The idea is the same as in GMM using growth curve modeling, mainly that the latent class membership specifies specific unobserved trajectories. These models are equivalent to GMM and are sometimes referred to heterogeneous linear mixed effects, underlining the idea that the sample may not belong to one single homogeneous population, but potentially to a mixture of distributions.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

On Puzzles, Statistics, Algorithms, and Understanding

by Karen Grace-Martin Leave a Comment

My 8 year-old son got a Rubik’s cube in his Christmas stocking this year.

I had gotten one as a birthday present when I was about 10. It was at the height of the craze and I was so excited.

I distinctly remember bursting into tears when I discovered that my little sister sneaked playing with it, and messed it up the day I got it. I knew I would mess it up to an unsolvable point soon myself, but I was still relishing the fun of creating patterns in the 9 squares, then getting it back to 6 sides of single-colored perfection. (I loved patterns even then). [Read more…] about On Puzzles, Statistics, Algorithms, and Understanding