A Likert scale (/ˈlɪkərt/ LIK-ərt, commonly mispronounced as /ˈlaɪkərt/ LY-kərt) is a psychometric scale commonly involved in research that employs questionnaires. It is the most widely used approach to scaling responses in survey research, such that the term (or more fully the Likert-type scale) is often used interchangeably with rating scale, although there are other types of rating scales. The scale is named after its inventor, psychologist Rensis Likert. Likert distinguished between a scale proper, which emerges from collective responses to a set of items (usually eight or more), and the format in which responses are scored along a range. Technically speaking, a Likert scale refers only to the former. The difference between these two concepts has to do with the distinction Likert made between the underlying phenomenon being investigated and the means of capturing variation that points to the underlying phenomenon. When responding to a Likert item, respondents specify their level of agreement or disagreement on a symmetric agree-disagree scale for a series of statements. Thus, the range captures the intensity of their feelings for a given item. As such, Likert scales have found application in psychology and social sciences, statistics, business and marketing. A scale can be created as the simple sum or average of questionnaire responses over the set of individual items (questions). In so doing, Likert scaling assumes distances between each choice (answer option) are equal. Many researchers employ a set of such items that are highly correlated (that show high internal consistency) but also that together will capture the full domain under study (which requires less-than perfect correlations). Others hold to a standard by which "All items are assumed to be replications of each other or in other words items are considered to be parallel instruments". By contrast, modern test theory treats the difficulty of each item (the ICCs) as information to be incorporated in scaling items. (Wikipedia).
Scales of Measurement - Nominal, Ordinal, Interval, & Ratio Scale Data
This statistics video tutorial provides a basic introduction into the different forms of scales of measurement such as nominal, ordinal, interval, and ratio scale data. My Website: https://www.video-tutor.net Patreon Donations: https://www.patreon.com/MathScienceTutor Amazon Store: htt
From playlist Statistics
This video shows how to use scale to determine the dimensions of a proportional model. http://mathispower4u.yolasite.com/
From playlist Unit Scale and Scale Factor
This video shows how to use unit scale to determine the actual dimensions of a model and how to determine the dimensions of a model from an actual dimensions. http://mathispower4u.yolasite.com/
From playlist Unit Scale and Scale Factor
Definition of scale variable for SPSS, psychology/behavioral science & finance.
From playlist Types of Variables
Computing z-scores(standard scores) and comparing them
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Computing z-scores(standard scores) and comparing them
From playlist Statistics
Percentiles, Deciles, Quartiles
Understanding percentiles, quartiles, and deciles through definitions and examples
From playlist Unit 1: Descriptive Statistics
What are nominal ordinal and scale in IBM SPSS Statistics? Three levels of measurement explained.
From playlist SPSS
Find the sample size for an experiment testing a difference between paired means using power analysis in Minitab 17.
From playlist Minitab 17 Instructional Videos
Data and Statistics (2 of 4: Brainstorming survey questions that can be asked)
More resources available at www.misterwootube.com
From playlist Data Analysis
Rasa Reading Group: Spot The Bot: A Robust & Efficient Framework for Evaluation of Conversational AI
This week we'll start with the paper "Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems" by Jan Deriu, Don Tuggener, Pius von Daniken, Jon Ander Campos, Alvaro Rodrigo, Thiziri Belkacem, Aitor Soroa, Eneko Agirre and Mark Cieliebak from EM
From playlist Rasa Reading Group
Bitsy Bentley interviewed at Strata Conference + Hadoop World NYC 2012
Bitsy Bentley, Director of Data Visualization, GfK Custom Research
From playlist Strata Conference + Hadoop World NYC 2012
From playlist CS124 - Full Course
Surveys, Forms and Quiz in Excel Part Two - Podcast 2228
A follow-up to episode 2226 - more cool features with Forms and Surveys in Excel. First - some analysis of the responses from episode 2226 0:05 Coke or Pepsi 0:55 Time of Day 1:14 Date of responses 1:52 3D Map of responses 2:16 Drawing winner of book 3:06 Further Discussion of Features in
From playlist New in Excel for 2018
Foundations of ANOVA – Assumptions and Hypotheses for One-Way ANOVA (12-3)
The assumptions for One-Way ANOVA require a scale-level dependent variable and a categorical independent variable, typically with three or more levels. Check for outliers, independence, and normality. The non-parametric alternative is the Kruskal-Wallis One Way ANOVA test. The null hypothe
From playlist WK12 One-Way ANOVA - Online Statistics for the Flipped Classroom
PSPP 1.4.0-2 Tutorial Series (Episode 6): Independent Samples t-Test
In this PSPP tutorial, I show how to set up and analyze the output of an Independent samples t-test and discuss its limitations. PSPP: https://www.gnu.org/software/pspp/ NOTE: This tutorial uses the current build of 1.4.0-2 on MacOS. Also part of this video was my experimental foray into
From playlist PSPP Tutorials
R - Item Response Theory Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers Item Factor Analysis and Item Response Theory from the Beaujean SEM in R book. IRT information also pulled from StatsCamp materials taught by William Skorupski (highly recommend his class!). Both dic
From playlist Structural Equation Modeling
Understanding z-scores(standard scores) as a measure of relative standing
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Understanding z-scores(standard scores) as a measure of relative standing. Given several z-scores, the sample mean, and the sample standard deviation, we find the values of x both with the formula and intuitively.
From playlist Statistics
Statistical Rethinking Fall 2017 - week07 lecture13
Week 07, lecture 13 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapters 10 and 11. Slides are available here: https://speakerdeck.com/rmcelreath/statistical-rethinking-fall-2017-lecture-13 Additional in
From playlist Statistical Rethinking Fall 2017
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2018 This video replaces a previous live in-class video. You will learn about the different types and ways to code regression, along with new data screening procedures (Cook's and Leverage), different types of correlations/e
From playlist Advanced Statistics Videos
Confused about what a z-score is and how it relates to a bell curve? This short video explains in plain English what a z score is and what it's used for. Check out my Statistics Handbook: https://www.statisticshowto.com/the-practically-cheating-statistics-handbook/ Thanks for your support!
From playlist z-test