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Data Analysis Interview Questions And Answers
 
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Interview for Data Analysis.What is data analysis?What you know about interquartile range as data analyst?Do you know what is data analysis?What is long-term outcome in data analysis?What is medium-term outcome in data analysis?
Views: 11139 Interview Questions
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
 
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Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 136652 YaleUniversity
Fundamentals of Qualitative Research Methods: Developing a Qualitative Research Question (Module 2)
 
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Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 2. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 80601 YaleUniversity
Part 1 - Using Excel for Open-ended Question Data Analysis
 
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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: 149455 Jacqueline C
Coding Multiple Variables and Open-ended Questions. Part 2 of 3 on Quantitative Coding
 
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A lecture on coding and data entry in quantitative research by Graham R Gibbs taken from a series on quantitative data analysis and statistics given to undergraduate students at the University of Huddersfield. This is part 2 of 3 and examines how to deal with questions with more than one answer and questions with open-ended answers. Credits: Music: Kölderen Polka by Tres Tristes Tangos is licensed under an Attribution-ShareAlike 3.0 International License. http://freemusicarchive.org/music/Tres_Tristes_Tangos/ Image: Ice-ferns by Schnobby, Wikimedia Commons, licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Views: 69414 Graham R Gibbs
Excel and Questionnaires: How to enter the data and create the charts
 
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This is a tutorial on how to enter the results of your questionnaires in Excel 2010. It then shows you how to create frequency tables (using the countif function not the frequency function). The next stage is creating charts.
Views: 336937 Deirdre Macnamara
FAQ Answers -1 : Analytics Interview Q&A Discussion | Data Science
 
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In this video I shall discuss ten important and basic interview questions asked in technical round of Analytics or Data Science interviews. In data science interview you will get questions from probability, statistics to machine learning and deep learning and also questions from SAS and R. You will also get questions on big data tools and programming. Contact : [email protected] ANalytics Study Pack : http://analyticsuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 71357 Analytics University
Analysing Questionnaires
 
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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/
Interview with a Data Analyst
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 269028 Udacity
How to Analyze Satisfaction Survey Data in Excel with Countif
 
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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: 332243 Ann K. Emery
Solving Data Interpretation Problems- Tricks, Techniques, Visualization and Imagination
 
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Dr. Manishika Jain in this video focuses on solving data interpretation problems mainly finding way out for approximations, solving bar graphs, tables and pie charts by imagination and visualization. For more details and elaborate solutions to problems visit https://www.doorsteptutor.com/Exams/ Types of Questions @0:38 Themes for Trick Analysis @0:59 Doing Approximation – Game of Zero’s @3:11 Don’t Simplify Fractions – Until Necessary @10:45 Average @14:11 Pie Diagram @15:56 #Tricks #Imagination #Fractions #Necessary #Interpretation #Approximation #Scatter #Visualization #Manishika #Examrace Examrace is number 1 education portal for competitive and scholastic exam like UPSC, NET, SSC, Bank PO, IBPS, NEET, AIIMS, JEE and more. We provide free study material, exam & sample papers, information on deadlines, exam format etc. Our vision is to provide preparation resources to each and every student even in distant corders of the globe. Dr. Manishika Jain served as visiting professor at Gujarat University. Earlier she was serving in the Planning Department, City of Hillsboro, Hillsboro, Oregon, USA with focus on application of GIS for Downtown Development and Renewal. She completed her fellowship in Community-focused Urban Development from Colorado State University, Colorado, USA. For more information - https://www.examrace.com/About-Examrace/Company-Information/Examrace-Authors.html
Views: 176773 Examrace
How to enter survey data into Excel from a pen-and-paper questionnaire
 
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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: 78761 Kent Löfgren
Data analyst interview questions and answers pdf ebook
 
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BEST MATERIALS FOR DATA ANALYST JOB INTERVIEW: 1. Interview questions and answers ebook: http://jobinterview247.com/free-ebook-177-interview-questions-with-best-answers 2. Top 10 interview secrets to win every job interview: http://interviewquestionsaz.blogspot.com/2013/07/top-10-secrets-to-win-every-job.html 3. Download free pdf ebook: Top 14 common mistakes in job interviews http://interviewquestionsaz.blogspot.com/2013/07/top-12-common-mistakes-in-job-interviews.html Checklist for YOUR JOB INTERVIEW: + How to answer behavioral interview questions or competency based interview questions + Dress code for Data analyst interview + Interview thank you letter samples for Data analyst positions + Data analyst interview case studies + Data analyst interview follow up questions + Data analyst interview preparation tips + Data analyst interview strengths and weaknesses + Data analyst interview tell me about yourself? + Data analyst interview process + Data analyst interview closing questions + Data analyst phone interview + Data analyst second interview + Data analyst situational interview + Data analyst group interview etc Job positions related: Data analyst assistant, Data analyst administrator, Data analyst associate, Data analyst clerk, Data analyst coordinator, Data analyst director, Data analyst executive, Data analyst team leader, Data analyst officer, Data analyst supervisor, Data analyst specialist, Data analyst consultant, Data analyst head, Data analyst engineer, Data analyst representative etc Will update to: 2018, 2019, 2020 …
Views: 84378 WaiterCareer247
Statistical questions | Data and statistics | 6th grade | Khan Academy
 
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What makes a question a "statistical question"? Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6-statistical-questions/e/statistical-questions?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/histograms-intro?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-plot/v/frequency-tables-and-dot-plots?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 423152 Khan Academy
Qualitative analysis of interview data: A step-by-step guide
 
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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; *it surprises you; *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. 3.10. 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 This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that 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. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 641623 Kent Löfgren
Sociology Research Methods: Crash Course Sociology #4
 
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Today we’re talking about how we actually DO sociology. Nicole explains the research method: form a question and a hypothesis, collect data, and analyze that data to contribute to our theories about society. Crash Course is made with Adobe Creative Cloud. Get a free trial here: https://www.adobe.com/creativecloud.html *** The Dress via Wired: https://www.wired.com/2015/02/science-one-agrees-color-dress/ Original: http://swiked.tumblr.com/post/112073818575/guys-please-help-me-is-this-dress-white-and *** Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark, Les Aker, Robert Kunz, William McGraw, Jeffrey Thompson, Jason A Saslow, Rizwan Kassim, Eric Prestemon, Malcolm Callis, Steve Marshall, Advait Shinde, Rachel Bright, Kyle Anderson, Ian Dundore, Tim Curwick, Ken Penttinen, Caleb Weeks, Kathrin Janßen, Nathan Taylor, Yana Leonor, Andrei Krishkevich, Brian Thomas Gossett, Chris Peters, Kathy & Tim Philip, Mayumi Maeda, Eric Kitchen, SR Foxley, Justin Zingsheim, Andrea Bareis, Moritz Schmidt, Bader AlGhamdi, Jessica Wode, Daniel Baulig, Jirat -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 289565 CrashCourse
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 772628 Claus Ebster
4. How to Handle Ranking questions with MS Excel
 
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This video shows how to handle ranking questions with sample data in MS Excel. In this video, I have used transpose and rank function.
Views: 3807 Asha Chawla
Analyzing and Presenting Open-Ended Questions
 
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Looks at how to present open-ended questions in a research paper.
Views: 7063 Robin Kay
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 87142 James Woodall
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 809402 David Langer
Choosing which statistical test to use - statistics help
 
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Seven different statistical tests and a process by which you can decide which to use. The tests are: Test for a mean, test for a proportion, difference of proportions, difference of two means - independent samples, difference of two means - paired, chi-squared test for independence and regression. This video draws together videos about Helen, her brother, Luke and the choconutties.
Views: 646342 Dr Nic's Maths and Stats
Likert Scales and Data Analysis
 
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Advice on gathering and analyzing data in organizations, tips on using Likert scales, and a case study on leveraging data to help the bottom line. McMillan Interview http://videos.asq.org/influencing-public-policy-with-data-analysis Full Case Study by S. Pandravada and T. Gurun https://secure.asq.org/perl/msg.pl?prvurl=http://asq.org/2017/02/statistical-process-control/fresh-foods-ordering-process.pdf
Views: 6300 ASQ
3 Correlational Analysis - Writing Research Questions
 
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The correlational analysis video series is available for FREE as an iTune book for download on the iPad. The ISBN is 978-1-62847-042-6. The title is "Correlational Analysis". Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series. The book can be accessed at: https://itunes.apple.com/us/book/correlational-analysis/id656763624?ls=1 This video examines the process for writing research questions for correlational analysis. Emphasis is also given to writing hypotheses and aligning questions, hypotheses, and methodology.
Views: 3374 Lee Rusty Waller
Developing a Quantitative Research Plan: Research Questions
 
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http://thedoctoraljourney.com/ This tutorial focuses on the development of research questions. For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
Views: 7870 The Doctoral Journey
SPSS for questionnaire analysis:  Correlation analysis
 
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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: 486689 Phil Chan
SPSS: How To Enter, Code, And Analyze Multiple Choice Data
 
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0:08 Multiple choice item vs. Likert scale item 1:33 Multiple choice questions with one correct answer 3:27 Multiple choice questions with multiple correct answers 6:03 "Multiple response set" in SPSS 7:52 How to pronounce "Likert"? This video discusses how to best enter and code multiple choice type data in SPSS as well as how to analyze such data using descriptive stats and multiple response sets. Please LIKE this video if you enjoyed it. Otherwise, there is a thumb-down button, too... :P ▶ Please SUBSCRIBE to see new videos (almost) every week! ◀ ▼MY OTHER CHANNEL (MUSIC AND PIANO TUTORIALS)▼ https://www.youtube.com/ranywayz ▼MY SOCIAL MEDIA PAGES▼ https://www.facebook.com/ranywayz https://nl.linkedin.com/in/ranywayz https://www.twitter.com/ranywayz Animations are made with Sparkol. Music files retrieved from YouTube Audio Library. All images used in this video are free stock images or are available in the public domain. The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated. #SPSS #Statistics #DataEntry
Views: 36706 Ranywayz Random
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT 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 likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 415797 Quantitative Specialists
Qualitative Analysis: Coding and Categorizing Data by Philip Adu, Ph.D.
 
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Data analysis is all about data reduction. But how do you reduce data without losing the meaning? What is the coding process? What coding strategies can you use? How do you make sure the categories or themes address your research question(s)? How do you present your qualitative findings in a meaningful manner? If you want answers to these questions, watch this video. To access the PowerPoint slides, please go to:https://www.slideshare.net/kontorphilip/qualitative-analysis-coding-and-categorizing
The Coding Data Matrix and Variable Types. Part 1 of 3 on Quantitative Coding and Data Entry
 
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A lecture on coding and data entry in quantitative research by Graham R Gibbs taken from a series on quantitative data analysis and statistics given to undergraduate students at the University of Huddersfield. This is part 1 of 3 and covers the basic principles of coding quantitative data from questionnaires and the types of variables that can be used. Credits: Music: Kölderen Polka by Tres Tristes Tangos is licensed under an Attribution-ShareAlike 3.0 International License. http://freemusicarchive.org/music/Tres_Tristes_Tangos/ Image: Ice-ferns by Schnobby, Wikimedia Commons, licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Views: 16712 Graham R Gibbs
Qualitative vs. Quantitative
 
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Let's go on a journey and look at the basic characteristics of qualitative and quantitative research!
Views: 650305 ChrisFlipp
SPSS for newbies: Data entry for multiple response questions, "Tick boxes that apply" ✔
 
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Questionnaire data entry in SPSS: multiple response questions - frequency and graph CONTENT 1:58 Examples of questions with multiple (many) answers - dichotomies and categories 6:53 Dichotomies type question - data entry and summary stats: data entry, frequencies , % of response v % Cases 18:15 Multiple response sets (why use and how to create them) 22:25 Dealing with questions with no ticks in boxes but that are not missing values 25:34 How to make bar charts in SPSS Date: 8 August, 2013
Views: 278188 Phil Chan
5 Basic (SPSS) Quantitative Data Analyses For Bachelor's Research
 
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1. Descriptives: 1:32 2. T test: 2:52 3. Correlation: 4:41 4. Chi square: 5:39 5. Linear regression: 6:45 This video discusses the basic statistical analytical procedures that are required for a typical bachelor's thesis. Five stats are highlighted here: descriptives, T test, correlation, Chi square, and linear regression. For requirements on reporting stats, please refer to the appendix of your research module manuals -- Frans Swint and I wrote an instructional text on APA reporting of stats. There is no upper limit in terms of how advanced your stats should be in your bachelor's dissertation. This video covers the basic procedures and is not meant to replace the instructions of your own research supervisor. Please consult your own research advisor for specific questions regarding your data analyses. Please LIKE this video if you enjoyed it. Otherwise, there is a thumb-down button, too... :P ▶ Please SUBSCRIBE to see new videos (almost) every week! ◀ ▼MY OTHER CHANNEL (MUSIC AND PIANO TUTORIALS)▼ https://www.youtube.com/ranywayz ▼MY SOCIAL MEDIA PAGES▼ https://www.facebook.com/ranywayz https://nl.linkedin.com/in/ranywayz https://www.twitter.com/ranywayz Animations are made with Sparkol. Music files retrieved from YouTube Audio Library. All images used in this video are free stock images or are available in the public domain. The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated.
Views: 2223 Ranywayz Random
Introduction to Quantitative Data Analysis and Statistics
 
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In this lecture, I provide a very basic introduction to quantitative data analysis and statistics. We begin by defining what "data" is, what a dataset looks like, and software tools for analyzing data.
Views: 3203 David Russell
Conducting Qualitative Analysis Using NVivo 11 (Part3) by Philip Adu, Ph.D.
 
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Subtitle: 'Presenting Qualitative Findings Using NVivo 11 Output to Tell the Story' Communicating qualitative findings involves presenting an information that represents the data, addresses the research question(s), and makes sense to your audience. In this presentation, we addressed the following questions: How do you present a background information about participants and related context? How do you communicate the data analysis process to ensure the transparency of the data reduction and transformation process? How do you ensure credibility of the findings as you communicate the results? What is the role of NVivo Outputs (i.e. diagrams and tables) in communicating the results? To access the PowerPoint slides, please go to: https://www.slideshare.net/kontorphilip/presenting-qualitative-findings-using-nvivo-output-to-tell-the-story
ED693 - Discussion 1: Survey Data Analysis Techniques
 
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Survey Data 1. Do the survey data come from nominal, ordinal or numerical scales or measures? 2. How many independent and dependent variables are there? 3. What statistical methods are potentially appropriate? 4. Do the survey data fit the requirements of the methods? 5. Which research question(s) will these results address?
SPSS for newbies: Questionnaire data entry - your questions answered
 
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This is a follow up video to "SPSS: Questionnaire data entry" Topic: Questionnaire/Survey data entry into SPSS 0:43 Ordinal variable goes from 1 to 7. Can I treat it as scale? 4:55 Is it possible to see the labels instead of the code? 6:37 I want to move a variable up the list. 8:49 Is there a quick way to reach the data entry for my variables? 9:50 I am using a variable that has more than 1 type of missing value. Please explain.
Views: 191914 Phil Chan
T8 Analyzing Data (see revised version https://youtu.be/MkNQJUzmkrI)
 
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2018 version can be found at https://youtu.be/MkNQJUzmkrI Analyzing data is an essential part of doing action research. This video and the activities and resources on https://www.actionresearchtutorials.org/8 , helps novice action researchers think about how to analyze the data they have collected to explore their research questions. Table of Contents: 00:00 - Opening 00:33 - overview 00:55 - 1. Organize Data 04:29 - 2 explore the data 09:55 - 3. Display the Data 11:54 - Ending & Tutorial 9
Views: 2854 Margaret Riel
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 748370 Dr Nic's Maths and Stats
Keeping Track of  Qualitative Research Data using Excel
 
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This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.
Views: 32939 tamuwritingcenter
Coding Part 1: Alan Bryman's 4 Stages of qualitative analysis
 
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An overview of the process of qualitative data analysis based on Alan Bryman's four stages of analysis. Reference Bryman, A (2001) Social Research Methods, Oxford: Oxford University Press This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) http://creativecommons.org/licenses/by-nc-sa/4.0/
Views: 188285 Graham R Gibbs
Nominal, ordinal, interval and ratio data: How to Remember the differences
 
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Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook Page: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Facebook: https://www.facebook.com/laura.killam LinkedIn: http://ca.linkedin.com/in/laurakillam Quantitative researchers measure variables to answer their research question. The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information. In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level. To remember these levels of measurement in order use the acronym NOIR or noir. The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories. The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair. Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories. Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical. Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order. While there is an order, it is also unknown how much distance is between each category. Values in an ordinal scale simply express an order. All nominal level tests can be run on ordinal data. Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured. To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode. Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known. Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement. For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81. If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy. Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero. Typically this level of measurement is only possible with physical measurements like height, weight and length. Any statistical tests can be used with ratio level data as long as it fits with the study question and design.
Views: 307538 NurseKillam
What is action research?
 
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Here's a short description of action research. TRANSCRIPT: Teaching is a craft. It’s both an art and a science, which is why great teachers always experiment and make tons of mistakes. But how do you know what’s actually working? One option is action research. Here you can identify a question or problem, test out a strategy, gather data, and determine if it works. The end result is something dynamic, innovative, and tied directly to your classroom. Action research dissolves the barrier between participants and researchers. In other words, the teacher actively participates in the situation while conducting the research. There are many action research frameworks, but they generally follow a similar process: You start out in phase one, planning for research. Phase One: Planning for Research It starts with an inquiry process, where you define a specific research question. It needs to be something you can actually test. Next, you conduct a literature review to gain a deeper understanding of the related research. Finally, you move into the design process, where you determine your data methods, consider ethical issues, get required permissions, create deadlines and set up systems. This is where you engage in multiple cycles of experimentation and data collection. Your data collection might include qualitative data, like observations, artifacts, and interviews or quantitative data like rubric scores, surveys, or achievement data. Phase Three: Analysis You will often start by organizing data with charts or graphs and looking for trends. You might also discuss it with peers, free write in a journal, or create a cluster map before eventually writing out your results. Phase Four: Conclusion This is often where you share your research with the world and reflect on your own practice. This will ultimately lead to new questions . . . and the cycle will continue again as you refine your craft as a better, more creative teacher.
Views: 81042 John Spencer
How to do UGC NET Data Interpretation Questions
 
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How to do UGC NET Data Interpretation Questions DOWNLOAD ALL SUBJECTS UGC NET OLD PAPERS - https://kumarbharat.com/qualifynetjrf/Qualify-NET-JRF-Solved-Papers/index.php ONLINE FREE MOCK TESTS ALL SUBJECTS - https://kumarbharat.com/qualifynetjrf/Qualify-NET-JRF-Mock-Tests/index.php
Views: 47893 Kumar Bharat
Interviewing with McKinsey: Case study interview
 
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Learn what to expect during the case study interview. Hear what some recent hires did - and did not - do to prepare.
Views: 507295 McKinsey & Company
Enter data from a questionnaire, Ex 3: Multi-response (tick all that apply)
 
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Enter and define variables from a questionnaire in SPSS. This example looks at a multiple response question in which a participant can 'tick all that apply'. ASK SPSS Tutorial Series
Views: 70889 BrunelASK
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
 
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Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx All Excel Files for All Video files: http://people.highline.edu/mgirvin/excelisfun.htm. Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More: (00:08) Introduction to class (00:49) Cells, Worksheets, Workbooks, File Names (02:54) Navigating Worksheets & Workbook (03:58) Navigation Keys (04:15) Keyboard move Active Sheet (05:40) Ribbon Tabs (06:25) Add buttons to Quick Access Tool Bar (07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis & Charting (08:55) Introduction to Data Analysis (10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells (11:16) Keyboard Enter puts content in cell and move selected cell down (13:00) Data Type DEFAULT Alignments (13:11) First Formula. Entering Cell References in formulas (13:35) Keyboard Ctrl + Enter puts content in cell & keep cell selected (14:45) Why we don’t override DEFAULT Alignments (15:05) Keyboard Ctrl + Z is Undo (17:05) Proper Data Sets & Raw Data (24:21) How To Enter Data & Data Labels (24:21) Stylistic Formatting (26:35) AVERAGE Function (27:31) Format Formulas Differently than Raw Data (28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste (29:59) Use Eraser remove Formatting Only (29:19) Keyboard Ctrl + B adds Bold (29:57) Excel’s Golden Rule (31:43) Keyboard F2 puts cell in Edit Mode (32:01) Violating Excel’s Golden Rule (34:12) Arrow Keys to put cell references in formulas (35:40) Full Discussion about Formulas & Formulas Elements (37:22) SUM function Keyboard is Alt + = (38:22) Aggregate functions (38:50) Why we use ranges in functions (40:56) COUNT & COUNTA functions (42:47) Edit Formula & change cell references (44:18) Absolute & Relative Cell References (45:52) Use Delete Key, Not Right-click Delete (46:40) Fill Handle & Angry Rabbit to copy formula (47:41) Keyboard F4 Locks Cell Reference (make Absolute) (49:45) Keyboard Tab puts content in Cell and move selected Cell to right (50:55) Order of Operation error (52:17) Range Finder to find formula errors (52:34) Lock Cell Reference after you put cell in Edit Mode (53:58) Quickly copy an edited formula down a column (53:07) F2 key in last cell to find formula errors (54:15) Fix incorrect range in function (54:55) SQRT function & Fractional Exponents (57:20) STDEV.P function (58:10) Navigate Large Data Sets (58:48) Keyboard Ctrl + Arrow jumps to bottom of data set (59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range) (01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up (01:02:55) Counting with conditions or criteria: COUNTIFS function (01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell (01:05:31) Counting between an upper & lower limit with COUNTIFS (01:07:36) COUNTIFS copied down column (01:10:08) Joining Comparative Operator with Cell Reference in formula (01:12:50) Data Analysis features in Excel (01:13:44) Sorting (01:16:59) Filtering (01:20:39) Introduction to PivotTables (01:23:39) Create PivotTable dialog box (01:24:33) Dragging & dropping Fields to create PivotTable (01:25:31) Dragging Field to Row area creates a Unique List (01:26:17) Outline/Tabular Layout (01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name (01:28:12) 2nd & 3rd PivotTable examples (01:31:23) What is a Cross Tabulated Report? (01:33:04) Create Cross Tabulated Report w PivotTable (01:35:05) Show PivotTable Field List (01:36:48) How to Pivot the Report (01:37:50) Summarize Survey Data with PivotTable. (01:38:34) Keyboard Alt, N, V opens PivotTable dialog box (01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN (01:43:25) Count & Count Number calculations in a PivotTable (01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data (01:47:00) #1 Rule for Charts: No Chart Junk! (01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart (01:51:34) Create Column Chart using Recommended Chart feature (01:53:00) Remove Field Buttons from Pivot Chart (01:54:10) Chart Formatting Task Pane (01:54:45) Vary Fill Color by point (01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable (01:56:02) Add Data Labels to Chart (01:57:28) Copy Chart & Create Bar Chart (01:57:48) Change Chart Type (01:58:15) Change Gap Width. (01:59:17) Create Pie Chart (01:59:23) Do NOT use 3-D Pie (01:59:42) Add % Data Labels to Pie Chart (02:00:25) Create Line Chart From PivotTable (02:01:20) Link Chart Tile to Cell (02:02:20) Move a Chart (02:02:33) Create an X-Y Scatter Chart (02:03:35) Add Axis Labels (02:05:27) Number Formatting to help save time (02:07:24) Number Formatting is a Façade (02:10:27) General Number Format (02:10:52) Percentage Number Formatting (02:14:03) Don’t Multiply Relative Frequency by 100 (02:17:27) Formula for % Change & End Amount
Views: 393556 ExcelIsFun
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
 
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There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention. This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship. For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?" The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out." Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout." The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary. In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables. Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis. A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one. A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it. To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred. A type II error happens when you decide your prediction is wrong when you are actually right. One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules. It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study. When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level. Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists. The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...
Views: 82210 NurseKillam