Search results “Survey analysis using r”

Delivered by Max Richman at the inaugural New York R Conference in New York City at Work-Bench on Friday, April 24th, and Saturday, April 25th.

Views: 5807
Work-Bench

Prepare, clean, wrangle, and analyze political science data in R. Code and walkthrough for students or beginners learning quantitative, statistical analysis in R. This shows you how to do common data cleaning tasks, make a plot of country averages over time, and estimate a basic linear regression model with Eurobarometer data. How important is religion in different European countries? Which variables predict the probability individuals will vote in the European Parliament elections?
Data for this script can be downloaded here: https://www.dropbox.com/s/5bdhel8l7c5r59z/eurobarometer_trends.dta?dl=0
The script can be found here: https://gist.github.com/jmrphy/9020745
Newsletter: https://tinyletter.com/jmrphy
Blog: http://jmrphy.net/blog
Twitter: http://twitter.com/jmrphy
Podcast: http://jmrphy.libsyn.com/
Facebook: https://www.facebook.com/otherlifenow/
Periscope: https://www.pscp.tv/jstnmrphy
Instagram: https://www.instagram.com/jstnmrphy/

Views: 2503
Justin Murphy

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. This talk will provide an introduction to survey statistics and the Survey package. There will be a brief overview of complex designs and some of the theory behind their analysis, followed by a demonstration using the Survey package.
Sebastián Duchêne is a Ph.D. candidate at The University of Sydney, based at the Molecular Phylogenetics, Ecology, and Evolution Lab. His broad area of research is virus evolution. His current projects include an R package for evolutionary analysis, and the development of statistical models for molecular epidemiology. In addition to his PhD studies, he is a reviewer for the PLoS ONE academic journal in the area of evolution and bioinformatics. Before coming to Sydney, he was a data analyst at the National Oceanic and Atmospheric Administration (NOAA) in the USA.

Views: 8120
Jeromy Anglim

This video tutorial will show you how to conduct an Exploratory factor analysis in R. This is an intermediate level video. You should know how to read data into R, conduct and understand PCA before watching this video.

Views: 40852
Ed Boone

Provides illustration of doing cluster analysis with R.
R File: https://goo.gl/BTZ9j7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- Illustrates the process using utilities data
- data normalization
- hierarchical clustering using dendrogram
- use of complete and average linkage
- calculation of euclidean distance
- silhouette plot
- scree plot
- nonhierarchical k-means clustering
Cluster analysis is an important tool related to analyzing big data or working in data science field.
Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 98917
Bharatendra Rai

Published on 8/7/17
Presented on 8/7/17
Presented by Dr. Victor R. Prybutok
The focus of this Webinar is to provide an overview of how to develop and contextualize surveys for use in obtaining quality customer feedback. Covered topics include how surveys are used to develop insight from customers, consumers, business partners, etc. The presentation will provide an overview of survey design and analysis including how item writing or modification of validated items in a construct are verified via exploratory factor analysis & reliability measures. We will also discuss how the model for which the survey is designed is tested via regression, logistic regression, or structural equation modeling. By the end of this webinar, participants should have a basic understanding of survey design and analytical techniques that result in obtaining better customer feedback even when one purchases an existing survey instrument. Suggestions on how to learn more will also be provided.

Views: 1986
ASQStatsDivision

A Regression Tutorial Using the World Values Survey

Views: 5339
Philip Truscott

Join Kaggle data scientist Rachael live as she works on data science projects! Today she's going to be walking through how to analyze survey results with R.

Views: 649
Kaggle

An International Training on Survey Data Analysis Techniques Using R, December 24-28, 2018

Views: 13
ISER Nepal

This video is part of the University of Southampton, Southampton Education School, Digital Media Resources
http://www.southampton.ac.uk/education
http://www.southampton.ac.uk/~sesvideo/

Views: 193929
Southampton Education School

This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidyverse functionality for basic data analysis: https://youtu.be/xngavnPBDO4

Views: 124242
Ralf Becker

http://ytwizard.com/r/JYQr4n
http://ytwizard.com/r/JYQr4n
Introduction to Data Analysis using EXCEL for Beginners
Learn to apply the important concepts and techniques in data analysis using Excel.

Views: 25
FX 24

In this tutorial we will show you how to review and report on your survey results.
Sign up for an account at:
http://www.surveygizmo.com/plans-pricing/
Find more tutorials at our tutorials page:
http://www.surveygizmo.com/tutorials/
Make sure to subscribe for more videos!

Views: 2162
SurveyGizmo

Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
-------------------------
Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Views: 504066
Phil Chan

With the recent advances in deep learning, the ability of algorithms to analyse text has improved considerably. Now analysing digital and social media is not restricted to just basic sentiment analysis and count based metrics. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research.
For any queries: [email protected]

Views: 99
ParallelDots Tutorials

Using R Studio to convert text-based survey data into numeric values, and then using those values to create basic visualisations. For more information on this post visit: http://harkive.org/surv_enc_viz/
All data and R scripts used in this video are available via the Harkive GitHub page: https://github.com/harkive/HarkiveSurveyData
Please note: The scripts provided work when I use them, but may return errors for you. Feel free to contact me and I will assist if I can, but I am not an expert R user!

Views: 393
Harkive

Topics: Small sampling fraction, finite population correction, sampling with/without replacement

Views: 210
Dana R Thomson

Topics: Analysis workflow from research question to interpretation of results

Views: 553
Dana R Thomson

Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey
-----
Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.

Views: 359827
Ann K. Emery

Topics: Adjusting for stratification, clustering, oversampling and other complex survey design characteristics in Stata

Views: 5273
Dana R Thomson

DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ
In this final video of the series, we take a look at critical role that our data sources play in the kinds of analysis we can do. What we ask in a survey can directly affect which analytical method we use, so there's a lot to think about. This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center.
Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php
Duke Network Analysis Center: https://dnac.ssir.duke.edu

Views: 222
Mod•U: Powerful Concepts in Social Science

This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.

Views: 18241
CSSLOhioStateU

Topics: Small sampling fraction, finite population correction, sampling with/without replacement

Views: 1512
Dana R Thomson

How to run a correlation analysis using Excel and write up the findings for a report

Views: 295910
Chris Olson

Views: 18187
Ross Avilla

The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends.
The steps are also described in writing below (Click Show more):
STEP 1, reading the transcripts
1.1. Browse through all transcripts, as a whole.
1.2. Make notes about your impressions.
1.3. Read the transcripts again, one by one.
1.4. Read very carefully, line by line.
STEP 2, labeling relevant pieces
2.1. Label relevant words, phrases, sentences, or sections.
2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant.
2.3. You might decide that something is relevant to code because:
*it is repeated in several places;
*the interviewee explicitly states that it is important;
*you have read about something similar in reports, e.g. scientific articles;
*it reminds you of a theory or a concept;
*or for some other reason that you think is relevant.
You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you.
It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds.
STEP 3, decide which codes are the most important, and create categories by bringing several codes together
3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand.
3.2. You can create new codes by combining two or more codes.
3.3. You do not have to use all the codes that you created in the previous step.
3.4. In fact, many of these initial codes can now be dropped.
3.5. Keep the codes that you think are important and group them together in the way you want.
3.6. Create categories. (You can call them themes if you want.)
3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever.
3.8. Be unbiased, creative and open-minded.
3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data.
STEP 4, label categories and decide which are the most relevant and how they are connected to each other
4.1. Label the categories. Here are some examples:
Adaptation (Category)
Updating rulebook (sub-category)
Changing schedule (sub-category)
New routines (sub-category)
Seeking information (Category)
Talking to colleagues (sub-category)
Reading journals (sub-category)
Attending meetings (sub-category)
Problem solving (Category)
Locate and fix problems fast (sub-category)
Quick alarm systems (sub-category)
4.2. Describe the connections between them.
4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study.
STEP 5, some options
5.1. Decide if there is a hierarchy among the categories.
5.2. Decide if one category is more important than the other.
5.3. Draw a figure to summarize your results.
STEP 6, write up your results
6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results.
6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example:
*results from similar, previous studies published in relevant scientific journals;
*theories or concepts from your field;
*other relevant aspects.
STEP 7 Ending remark
Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.)
Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze:
*notes from participatory observations;
*documents;
*web pages;
*or other types of qualitative data.
STEP 8 Suggested reading
Alan Bryman's book: 'Social Research Methods' published by Oxford University Press.
Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE.
Text and video (including audio) © Kent Löfgren, Sweden

Views: 691634
Kent Löfgren

Prepare a survey instrument for distribution, construct a link for your instrument, and convert survey responses to analyzable data files in Qualtrics. The video was recorded as part of a teaching assistantship in EPY 710: Survey Research Methods with Alice Corkill, Ph.D. using Camtasia software.

Views: 2385
Marissa Nichols, Ph.D.

This tutorial will deep dive into data analysis using 'R' language. By the end of this tutorial you would have learnt to perform Sentiment Analysis of Twitter data using 'R' tool. To learn more about R, click here: http://goo.gl/uHfGbN
This tutorial covers the following topics:
• What is Sentiment Analysis?
• Sentiment Analysis use cases
• Sentiment Analysis tools
• Hands-On: Sentiment Analysis in R
The topics related to ‘R’ language are extensively covered in our ‘Mastering Data Analytics with R’ course.
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Views: 43660
edureka!

This video describes how to prepare raw survey data in an Excel sheet for running an analysis of variance (ANOVA) test.

Views: 14095
Chris Olson

This video describes the procedure of tabulating and analyzing the likert scale survey data using Microsoft Excel. This video also explains how to prepare graph from the tabulated data.
Photo courtesy: http://littlevisuals.co/

Views: 90312
Edifo

How to analyze the results of your survey in SurveyMonkey

Views: 17873
Urbana MidSchool

Analyse data
Export data
Conversion
Depending on your BOS questionnaire type, you might need to look into this video:
https://www.youtube.com/watch?v=bw-yvzORo7w

Views: 1753
TheKaKaNow

Completing data analysis on open-ended questions using Excel.
For analyzing multiple responses to an open-ended question see Part 2: https://youtu.be/J_whxIVjNiY
Note: Selecting "HD" in the video settings (click on the "gear" icon) makes it easier to view the data entries

Views: 159579
Jacqueline C

Nonresponse to a survey occurs when a selected unit does not provide the requested information. This is out of control of the research and affects the quality of the survey estimates.
For more methods resources see:
http://www.methods.manchester.ac.uk

Views: 10964
methodsMcr

Get the template with all formulas: http://www.smarthelping.com/2017/03/using-excel-to-perform-survey-analysis.html
Explore all of smarthelping's financial models: http://www.smarthelping.com/p/excel.html
This is a explanation of the various ways excel can be used to isolate various sub-groups and questions in a survey in order to develop a deeper understanding of responses as well as perform analysis.

Views: 3794
smarthelping

http://ytwizard.com/r/rcYwDM
http://ytwizard.com/r/rcYwDM
Do statistical analysis of your survey data

Views: 8
Health

Checkout the full article and download the file at: http://www.excelcampus.com/pivot-tables/analyze-survey-data-in-excel/
Learn how to use Power Query to transform multiple choice survey data in Excel. This survey data has been exported to Excel in a format that is not easy to use for a pivot table. In this video you will learn how to use the Unpivot feature in Power Query to transform or normalize the data. This will make it easier to analyze with a pivot table and chart.
Please subscribe to my free email newsletter to get more Excel tips and tutorials like this. http://www.excelcampus.com/newsletter
PART 2: https://youtu.be/h-sKEPEvwZ8
PART 3: https://youtu.be/NBgL8ItVdKY

Views: 35527
Excel Campus - Jon

I show my technique of entering raw data into Microsoft Excel that has been collected via a pen-and-paper survey. This includes both questions with fixed responses and open-ended questions.
Copyright: Text and video © Kent Löfgren, Sweden.

Views: 96547
Kent Löfgren

The release of Tableau 8.1 included capability for connecting Tableau to R for performing complex statistical analysis right within Tableau. This video will focus on predictive analytics, specifically multivariate regression. See how R can be used in conjunction with Tableau for performing regression analysis using multiple variables for better predictive modeling.

Views: 49864
ThorogoodBI

https://www.datacracker.com/ DataCracker is web-based survey data analysis software. Easily analyze your survey data. It provides you with statistical test results on your data without requiring expert knowledge in market research. Upload your data, and DataCracker will automatically discover significant results and write a basic report with tables and charts. You can dig deep into your data using advanced data analysis tools that allow you to discover segments and perform predictive analytics.

Views: 3059
DataCracker

Cursory review quantitative elements of the survey data.

Views: 2318
moriartp1

See how to summarize survey results (Psychology test) with:
1) Array Formula with SUM, COUNTA and IF functions.
2) VLOOKUP and Data Validation Drop Down List.

Views: 32695
ExcelIsFun

Explains how to cluster and analyze survey ranking question responses in Python.

Views: 1051
badassdatascience

Learn about Likert Scales in SPSS and how to copy labels from one variable to another in this video. Entering codes for Likert Scales into SPSS is also covered.
Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and 'Unlimited' members, get our text for free! (Only $4.99 otherwise, but will likely increase soon.)
Lots more Likert & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT
Likert scale SPSS video.
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Channel Description: For step by step help with statistics and SPSS. Both descriptive and inferential statistics covered. Subscribe today!
Video Transcript: In this video we'll take a look at how to enter value labels for a variable which will be review since we've done that before. But then I also want to show you how to apply value labels that were entered for one variable to a number of different variables which can be really useful as it's a great time saver. Here in this data set notice that I have 10 people and I have the variables gender, item 1, 2, 3, 4, and 5. And they answered on what's known as a Likert scale. Now you very well may have heard of a Likert scale before and the first thing is you may have heard of it called LIKE-ERT scale which is very common to call it that but it's actually Likert, so it's pronounced LICK-ERT instead of LIKE-ERT and it was developed by Rensis Likert in the early to middle 1900s he developed the scale. And it's used so commonly, it's used in this 5-point option as you see here, 5 to 1, and we'll talk about that in just a moment. You'll also see it in a 7-point option, it's very commonly used that way. And less commonly so but you'll see it in other ways like 9-point scale and so forth. And it's used with many different kinds of descriptions like definitely true, somewhat true, and so forth; not just agree as you see here. So, in the most traditional use of this scale, which is what we see right here, we have a 5=strongly agree, a 4=agree, 3 is neither agree nor disagree - this is sometimes called neutral - 2 is disagree and then 1 is strongly disagree. On item 1 they would read the following statement: I can turn to others for support when needed. And then what they do is they read that item, they look at these 5 options, and if it's someone who has a lot of support in their network or friendships or what have you, they might answer 5, strongly agree, or 4, agree. And if it's someone who doesn't experience a lot of social support, they might answer a 1 for strongly disagree or a 2 for disagree and so on. So, the first person here in row 1, notice for item 1 they answered a 4, so they answered agree. Item 2 they answered a 5 for strongly agree and so on. If we look down item 1, did anyone answer strongly disagree - let's take a look at that. We're looking for a 1 here, and notice that participant number 9, they answered a 1 on item 1, so they answered strongly disagree, and so on. So what I want to do here is go ahead and enter the value labels for item 1 so we're going to enter these into SPSS that you see here. And then I want to show you how to apply those to the remaining items in a very quick way. First of all, notice that we have gender, if I click on my value labels button here as a review, gender is already coded, I already entered those. But what I don't have entered is item 1, item 2, 3, 4, and 5. And I'd like to go ahead and enter those to have them in the dataset, so if I go back and look at this file at a later time, I'll remember that a 5 corresponded to strongly agree and a 1 corresponded to strongly disagree, so in other words I'll know which direction this scale is scored, and what I mean by that is higher scores indicate greater social support because people strongly agreed with a given item. Whereas lower scores indicated less social support. Since we're looking at entering value labels, let's begin with item 1. So I could either double-click on item 1 or I could go to the variable view tab. Let's go ahead and double-click on item 1 right at the column heading here that's "name". So I double-click on that and notice it takes me to the variable view window. So that's a quick way to get there if you want to access the variable view window. And then we'll go to the "values" column here, click on the "None" cell and then notice the 3 dots appear. So I click on that and then here let's start with
Lifetime access to SPSS videos: http://tinyurl.com/m2532td
Video on adding Likert items together to create a total score: http://youtu.be/7jxpSLZCBsw
Likert Scales
Likert
Strongly Agree to Strongly Disagree
Likert in SPSS

Views: 170552
Quantitative Specialists

Published on Nov 6, 2016(Today)
Survey data calculate mean, alpha value, correlate, regression for a research.
Watch this video: https://youtu.be/-43Nq7egT2I
Calculator is presented as a public service of Creative Research Systems ....SPSS...

Views: 1502
Anisur Rahman

Describes the process of categroizing and rating open-ended comments from a survey

Views: 11137
Robin Kay

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