Version 1.1 of the likert Package Released to CRAN. Categorized as: R, R-Bloggers After some delay, we are happy to finally get version 1.1 of the likert package on CRAN.
Although labeled 1.1, this is actually the first version of the package released to CRAN. After receiving some wonderful feedback from useR! This year, we held back releasing until we implemented many of the feature suggestions. The NEWS file details most of what is in this release, but here are some highlights: Simplify analyzing and visualizing Likert type items using R’s familiar print, summary, and plot functions. There are four demos available: likert - Shows most of the features of the package using data from the Programme of International Student Assessment (PISA).
The useR! Install.packages('likert',repos=' require(likert) The package is hosted on Github at [ You can always download the latest development version using devtools. require(devtools) install_github('likert','jbryer') Oh Ordinal data, what do we do with you? Common Approaches for Analyzing Likert Scales and Other Categorical Data. Which hypothesis test for Likert scale?
Drinking, sex, eating: Why don't we tell the truth in surveys? 27 February 2013Last updated at 13:56 GMT By Brian Wheeler BBC News Magazine Many people are under-reporting how much alcohol they are drinking.
But what else are we fibbing to researchers about and why do we do it? "I have the occasional sweet sherry. Purely medicinal. " It is a classic British sitcom scene. But the tendency to paint a less-than-honest picture about your unhealthy habits and lifestyle is not just restricted to alcohol.
It is understandable that people want to present a positive image of themselves to friends, family and colleagues. After all, the man or woman from the Office for National Statistics or Ipsos Mori can't order you to go on a diet or lay off the wine. Video: Survey Package in R. Sebastián Duchêne presented a talk at Melbourne R Users on 20th February 2013 on the Survey Package in R.
Talk Overview: Complex designs are common in survey data. In practice, collecting random samples from a populations is costly and impractical. Therefore the data are often non-independent or disproportionately sampled, and violate the typical assumption of independent and identically distributed samples (IDD). The Survey package in R (written by Thomas Lumley) is a powerful tool that incorporates survey designs to the data. Standard statistics, from linear models to survival analysis, are implemented with the corresponding mathematical corrections.
If you increase the sample size to 100 people, your margin of error falls to 10%. This webpage calculates the sample size required for a desired confidence interval, or the confidence interval for a given sample size: Creative Research Systems, 2003. How Many Subjects Do I Need for a Statistically Valid Survey? By Daryle Gardner-Bonneau, Ph.D.
Office of Research Michigan State University/Kalamazoo Center for Medical Studies Reprinted from Usability Interface, Vol 5, No. 1, July 1998 Beware of people who give quick, pat answers in response to the question - "I’m doing a survey. An intro to power and sample size estimation. + Author Affiliations Correspondence to: Dr S R Jones, Emergency Department, Manchester Royal Infirmary, Oxford Road, Manchester M13 9WL, UK; firstname.lastname@example.org Abstract.
What is a large enough random sample? With the well deserved popularity of A/B testing computer scientists are finally becoming practicing statisticians.
One part of experiment design that has always been particularly hard to teach is how to pick the size of your sample. The two points that are hard to communicate are that: The required sample size is essentially independent of the total population size.The required sample size depends strongly on the strength of the effect you are trying to measure. These things are only hard to explain because the literature is overly technical (too many buzzwords and too many irrelevant concerns) and these misapprehensions can’t be relieved unless you spend some time addressing the legitimate underlying concerns they are standing in for. As usual explanation requires common ground (moving to shared assumptions) not mere technical bullying.