Articles by

Y is for Ys, Y-hats, and Residuals

April 28, 2018 |

Y is for Ys, Y-hats, and Residuals When working with a prediction model, like a linear regression, there are a few Ys you need to concern yourself with: the ys (observed outcome variable), the y-hats (predicted outcome variables based on the equation), and the residuals (y minus y-hat). Today, I'll ...
[Read more...]

X is for By

April 27, 2018 |

X is for ByToday's post will be rather short, demonstrating a set of functions from the psych package, which allows you to conduct analysis by group. These commands add "By" to the end of existing functions. But first, a word of caution: With great power comes great responsibility. This function ...
[Read more...]

W is for (Meta-Analysis) Weights

April 26, 2018 |

Weights in Meta-AnalysisYesterday, I talked about the variance calculation for meta-analysis, which is based on sample size - the exact calculation varies by the type of effect size you're using. It's a good idea to inspect those variance calculations. There are many places where your numbers for the meta-analysis can ...
[Read more...]

V is for (Meta-Analysis) Variance

April 25, 2018 |

Variance in Meta-Analysis For the letter E, I introduced the metafor package to compute effect sizes. That is, you provide a data frame with the study information and the data needed to compute the effect size(s), and metafor does that for you. But what I didn't mention is that ...
[Read more...]

U is for (Data From) URLs

April 24, 2018 |

Working with URLs in RUp to now, we've been working with files saved locally on your computer. But that limits you to files that can be easily saved to your computer and, up to now, structured data. As we move from pure statistics to a data science approach, more and ... [Read more...]

T is for tibble

April 23, 2018 |

T is for TibbleFor the letter D, I introduced data frames, a built-in R object type. But as I've learned more about R and, in particular, the tidyverse - most recently when I finally started reading Text Mining with R: A Tidy Approach - I learned about a more modern ...
[Read more...]

Statistics Sunday: Using semPlot

April 22, 2018 |

using semPlot with Facebook ModelsToday's post will be mostly demonstration, but I'll build on some of the things I covered in yesterday's semPlot post. This month, I've blogged about two SEM models: confirmatory factor analysis and latent variable path analysis. Using the models from those posts, I'll show how to ...
[Read more...]

S is for semPlot Package

April 21, 2018 |

S is for semPlotToday's post is on my favorite, "where have you been all my statistical life?" package, semPlot. When I took my first structural equation modeling course 11 years ago, for our final project, we had to use one of the models we learned in the course on a set ...
[Read more...]

R is for R Origin Story

April 20, 2018 |

An important place in the history of statistics is AT&T Bell Laboratories. And one of the key parts of that story is the development of a language for statistical computing called S.Prior to 1975 or so, statistical researchers at Bell Labs used For...
[Read more...]

Q is for qplot

April 19, 2018 |

Q is for qplotYou may have noticed that I frequently use the ggplot2 package and the ggplot function to produce graphics for my posts. ggplot2, which is part of the so-called tidyverse, is called gg to refer to the "grammar of graphics". That is, it uses standard functions and arguments ...
[Read more...]

P is for Principal Components Analysis (PCA)

April 18, 2018 |

P is for Principal Components Analysis This month, I've talked about some approaches for testing the underlying latent variables (or factors) in a set of measurement data. Confirmatory factor analysis is one method of examining latent variables when you know ahead of time which observed variables are associated with which ... [Read more...]

O is for Overview Reports

April 17, 2018 |

O is for overview Reports with dataMaidOne of the best things you can do is to create a study codebook to accompany your dataset. In it, you should include information about the study variables and how they were created/computed. It's also nice to have a summary of your dataset, ... [Read more...]

N is for N (Sample Size) Estimation: Power Analysis in R

April 16, 2018 |

N Estimation We're pushing forward in the Blogging A to Z Challenge! Today, I'll talk about conducting power analyses in R with the pwr package. It's amazing the number of studies designed and conducted without the aid of power analysis. Once I learned how to do them in grad school, ...
[Read more...]

Statistics Sunday: Fit Statistics in Structural Equation Modeling

April 15, 2018 |

Fit Measures In my video on interpreting confirmatory factor analysis output, I promised a post on the various fit statistics. And here we are! As I said in the video, when you conduct structural equation modeling, the program is comparing the observed data - specifically the observed covariance matrix - ... [Read more...]

M is for R Markdown Files

April 14, 2018 |

Today's A to Z of R will be a bit different from previous ones in that the focus is not on how to code something in R, but how to use a feature in R Studio to create documents, such as HTML and PDF. Either of these types of documents ...
[Read more...]

L is for Latent Variable Path Analysis

April 13, 2018 |

LVPAFor the letter F, I introduced the lavaan package with confirmatory factor analysis. You may have noticed, during my video on interpreting output that there are two functions for analysis: cfa and sem. When the model you specify is a confirmatory factor analysis, it doesn't really matter which of these ...
[Read more...]

K is for Cohen’s Kappa

April 12, 2018 |

Last April, during the A to Z of Statistics, I blogged about Cohen's kappa, a measure of interrater reliability. Cohen's kappa is a way to assess whether two raters or judges are rating something the same way. And thanks to an R package called irr, it's very easy to compute. ...
[Read more...]

J is for jsonlite Package

April 11, 2018 |

J is for jsonlite packageToday I'm going to introduce a new method of storing and exchanging data: JSON or JavaScript Object Notation. Up to now, we've been working with delimited text files and R data frames. But JSON (pronounced "Jason") is another way we can store data that can be ...
[Read more...]

I is for (Classical) Item Analysis or I Must Be Flexible

April 10, 2018 |

I is for ITEMAN Back when I worked at HMH, I discovered an R package called ITEMAN, which is used for classical item analysis. I've mentioned classical test theory before, which focuses on the overall test or measure, as opposed to individual items. Tests and measures developed with classical test ... [Read more...]
1 2 3 4

Copyright © 2022 | MH Corporate basic by MH Themes

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)