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. #RanywayzRandom #SPSS #Research
Views: 8956 Ranywayz Random
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: 172555 YaleUniversity
www.pinnacleadvisory.com --- Pinnacle Advisory Group's Quantitative Analyst Sauro Locatelli explains what he does and how it aids the investment process. This is the fifth presentation from our February 25 Inside the Investment Committee event. www.pinnacleadvisory.com
Views: 115914 Pinnacle Advisory Group
The unit of analysis is the phenomenon or entity being studied. It is generally the thing that you are sampling in a quantitative study. It varies depending on what you are interested in. The unit of analysis may differ from the unit of observation, as you use the unit of analysis to explain the relationship set the theoretical level in the unit of observation occurs when you actually sample data. This idea of understanding the unit of observation versus the unit of analysis is important for quantitative research. The unit of analysis is important for both quantitative research and qualitative research, but it is often discussed in quantitative research. It differs from the level of analysis. The level of analysis generally pertains to the level at which you are analyzing a particular mechanism for a unit of analysis. For example, you might use population ecology to study the behavior of firms. Population ecology occurs at the population level (ie. aggregate) of firms but you are really studying firm behavior. It is important to get the right unit of analysis so that the theories you choose hold and are consistent when you analyze your data. For example, if you analyze transaction costs but then you look at firm behavior, you’re likely going to capture something other than transaction costs. Generally, you want to have the unit of analysis at the same level of analysis of your theory. You can also focus on a unit of analysis that cuts across levels of analysis. This is called multi-level theorizing, but it generally is not recommended because unless it is well done, it makes your paper seem very shallow. Most people end up just gathering theories from many different areas to explain the unit of analysis. The reason why you want to ensure that the unit of analysis in the level of analysis are similar is because you are unlikely to explain the phenomenon fully if you choose different levels of analysis, and rule out other possible explanations. Check out: What Is A Level Of Analysis? Nerd-out Wednesday https://youtu.be/MIlQIfrXmN8 What Is A Confirmation Bias? Confirmation Biases - Nerd-Out Wednesday https://youtu.be/72PnUjoSNKY How To Write A Research Question - Nerd-Out Wednesdays https://youtu.be/TQF7H0sEDcA How Do You Analyze Data In Research When Nothing Works? - Nerd-Out Wednesday https://youtu.be/AqRFsv7umhk What is reliability and validity? https://youtu.be/5lmUFIxfuAs **************** David Maslach is a research professor of entrepreneurship, innovation, and business strategy, I discuss topics, such as behavioral science, strategy, innovation, and entrepreneurship, and apply these to my new peer proofreading and editing platform. Topics include the sharing economy, altruism, investing in technology, starting a business, and bounded rationality. My favorite videos pertain to incentives, goal setting, and learning from failure to drive behaviors such as weight loss, stopping telemarketers, creating novel technologies, and creating new movements. https://r3ciprocity.com: Peer proofreading and editing platform A new platform where you can earn credits by editing other people's documents. Use these credits to have your own work edited. If you do a good enough job, you can convert these credits to money. The goal of the platform is to get people to 'pay it forward' and help other people out by creating incentives for people to give back. Check out https://www.r3ciprocity.com Please subscribe to the Youtube channel: https://www.youtube.com/channel/UC5spxk7bNDMGPSHjW_8ndZA
Views: 1482 r3ciprocity Team
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: 769338 Kent Löfgren
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: 21688 CSSLOhioStateU
Narrated slideshow tutorial about quantitative data analysis in psychology. Covers levels of data, measures of central tendency, measures of dispersion, graphs and probability. Further reading: Textbook pages 103-109 (orange Hodder book) http://www.smartpsych.co.uk/wp-content/uploads/2012/02/psych_methods1.pdf
Views: 8746 missowen1
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. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 788958 Dr Nic's Maths and Stats
This is a short practical guide to Qualitative Data Analysis
Views: 138920 James Woodall
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: 393282 Ann K. Emery
Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.
Views: 154928 VelactionVideos
If you are having troubles with your research paper, I might have a solution for you. My full course "Research Methods for Business Students" is available on Udemy. Here you can also submit YOUR questions to me and receive FEEDBACK ON YOUR PAPER! As you are my students, the course is only for 9.99 USD with following link: https://www.udemy.com/research-methods-for-business-students/?couponCode=RESEARCH_METHODS_1
Views: 3842 MeanThat
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: 406974 CrashCourse
In this video, I talk briefly about the six most important steps any researcher should follow when doing quantitative research. This video is part of the course "First Steps in Stata - Hands On!". For more information, visit our website www.schoolofresearch.com!
Views: 12231 School of Research
In this video, some recommendations around setting up a learning & teaching research study are described. In particular, a focus on qualitative data collection and the importance of thorough thematic analysis of the data is outlined (based on the approach described by Braun & Clarke (2006)). The video will link to a particular study in which Excel was used to assist in the thematic analysis process. Link to a second YouTube video outlining the method behind the thematic analysis process: https://youtu.be/VIYBEE-1GbA
Views: 499 Bree Bio
Impact evaluations need to go beyond assessing the size of the effects (i.e., the average impact) to identify for whom and in what ways a programme or policy has been successful. This video provides an overview of the issues involved in choosing and using data collection and analysis methods for impact evaluations
Views: 62207 UNICEF Innocenti
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: 432051 ExcelIsFun
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 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 1. Patton M. Qualitative Research and Evaluation Methods, 3rd edition. Sage Publishers; 2002. Curry L, Nembhard I, Bradley E. Qualitative and mixed methods provide unique contributions to outcomes research. Circulation, 2009;119:1442-1452. Crabtree, B. & Miller, W. (1999). Doing qualitative research, 2nd edition. Newbury Park, CA:Sage. Schensul S, Schensul J. and Lecompte M. 2012 Initiating Ethnographic research: A mixed Methods Approach, Altamira press. 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: 228194 YaleUniversity
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 To buy Dr. Philip Adu's new book, 'A Step-by-Step Guide to Qualitative Data Coding', please go to Amazon (https://www.amazon.com/Step-Step-Guide-Qualitative-Coding/dp/1138486876/ref=sr_1_3?ie=UTF8&qid=1543874247&sr=8-3&keywords=Philip+adu)
Views: 44743 Methodology Related Presentations - TCSPP
Quantitative Data vs Qualitative Data Additional Information on Qualitative vs Quantitative Data http://www.moomoomath.com/qualitative-and-quantitative-data.html Data can be divided into two groups called quantitative and qualitative data Quantitative data is numerical Qualitative Data id descriptive data Let’s look at examples of both Examples of quantitative data would be The number of pets, time of day, the temperature outside Quantitative data can be graphed If you count or measure, you are collecting quantitative data There are two types of quantitative data, discrete and continuous Discrete data is usually data you can count and continuous data is usually data you measure. I have a separate video on these two types of data. Qualitative is descriptive or observations and uses words For example, the color of a house, smell of a sock, texture of a shirt Quantitative or Qualitative Consider a cat Quantitative Data would be the cat has 4 legs and weighs 10 pounds Qualitative data would be the cat is yellow, and has soft fur A bookshelf Quantitative would be you have 50 books and is 150 centimeters tall. Qualitative data would be it is multi-color and has a smooth texture You may also enjoy.. Qualitative and Quantitative Data https://www.youtube.com/watch?v=2X-QSU6-hPU Quantitative Qualitative Song https://www.youtube.com/watch?v=-S2EiPD4-W0 -~-~~-~~~-~~-~- Please watch: "Study Skills Teacher's Secret Guide to your Best Grades" https://www.youtube.com/watch?v=f3bsg8gaSbw -~-~~-~~~-~~-~-
Views: 167022 MooMoo Math and Science
▼SUBSCRIBE To My Channel For More Research Videos▼ https://goo.gl/8f64I9 Types of research 0:12 - 2:17 Research designs 2:18 - 7:18 Data collection instruments 7:20 - 12:38 Sampling 13:26 - 18:00 To cite this video (APA): Zhang, R. (2016). Video summary on research types, research designs, data collection instruments, and sampling. [Video File]. Retrieved from https://youtu.be/WY9j_t570LY My other research videos: Zhang, R. (2017). When to use a qualitative research design? 4 things to consider. [Video File]. Retrieved from https://youtu.be/4FJPNStnTvA Zhang, R. (2017). What is a good Central Research Question? [Video File]. Retrieved from https://youtu.be/I4MfCDy7wDw Zhang, R. (2017). Research aim, research objective, research question, and investigative question. [Video File]. Retrieved from https://youtu.be/ujKIM59hy9I Please LIKE this video if you enjoyed it. Otherwise, there is a thumb-down button, too... :P ▼MY SOCIAL MEDIA PAGES▼ https://www.facebook.com/ranywayz https://nl.linkedin.com/in/ranywayz https://www.twitter.com/ranywayz #ResearchDesign #Thesis #RanywayzRandom
Views: 117232 RanYwayZ
Data Analysis For #Quantitative Research: #Qualitative #DataAnalysis is an iterative process of individual and group level review and #interpretation of narrative data. Purpose of Data Analysis Section: 1. Convince readers of your knowledge of the data and how you will use it. 2. Convince readers of your capability as a #researcher. 3. Convince readers of the analysis and results. How to write it? 1. Provide a Level-2 Heading. 2. Provide a roadmap of your data and #analysis. 3. Provide the data you have and what you do with it. Let's quickly talk about T-Test and Anova for Now A) Independent Sample T-Tests 1. State what data you are comparing. 2. Convey experimental group and control group. 3. Mention both groups at beginning and end of study. B) Dependent Sample T-Tests. 1. State what data you are comparing. 2. Convey that you are comparing at different times. 3. Mention the testing times for Comparision. C) Anova 1. State the type of data you have. 2. Describe the results or effects of the comparison. 3. Describe the interaction effects. D) #Hypothesis 1. Restate your hypothesis or predictions Contact Information INDIA: +91-8754446690 UK: +44-1143520021 Email: [email protected] Visit: http://www.statswork.com/
Views: 61 Stats Work
This session will provide information regarding descriptive statistics that are often used when reviewing assessment data. We will cover the statistics available in the Baseline reporting site and we will use example situations to identify which statistics should be used to answer the questions being asked. We will also provide an overview regarding levels of measurement that can help determine what types of statistics you are able to run on your data. - See more at: http://www2.campuslabs.com/support/training/basic-statistics-quantitative-analysis-i-5/#sthash.FDO5HA6i.dpuf
Views: 36327 Campus Labs
Coding your qualitative data, whether that is interview transcripts, surveys, video, or photographs, is a subjective process. So how can you know when you are doing it well? We give you some basic tips.
Views: 78399 Mod•U: Powerful Concepts in Social Science
Dr. Manishika Jain in this lecture explains factor analysis. Introduction to Factor Analysis: Factor Loading, Factor Scoring & Factor Rotation. NET Psychology postal course - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Psychology-Series.htm NET Psychology MCQs - https://www.doorsteptutor.com/Exams/UGC/Psychology/ IAS Psychology - https://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm IAS Psychology test series - https://www.doorsteptutor.com/Exams/IAS/Mains/Optional/Psychology/ Steps in Research Proposal @0:24 Research Topic @0:43 Review of Literature @0:56 Rationale and Need for the Study @1:18 Definition of Terms @1:24 Assumptions @3:03 Method, Sample and Tools @4:06 Probability Sampling @4:23 Non - Probability Sampling @4:34 Significance of Study @5:13 Technique for Data Analysis @5:18 Bibliography @5:42 Budget @6:28 Chapterisation @6:39 #Expenditure #Tabulate #Significance #Assumption #Literature #Rationale #Constitutive #Phenomena #Elucidate #Literature #Manishika #Examrace Factor Analysis and PCA Reduce large number of variables into fewer number of factors Co-variation is due to latent variable that exert casual influence on observed variables Communalities – each variable’s variance that can be explained by factors Types of Factoring • PCA – maximum variance for 1st factor; removes that and uses maximum for 2nd factor and so on… • Common Factor Analysis – Same as factor analysis (only common variance – used in CFA) • Image Factoring – correlation matrix; uses OLS regression matrix • Maximum Likelihood Method – on correlation matrix • Alpha Factoring • Weight Square Estimate communalities - each variable’s variance that can be explained by factor. See factors are retained Factor rotation - Procedure in which the eigenvectors (factors) are rotated in an attempt to achieve simple structure. Factor loading - Relation of each variable to the underlying factor. Output of a simple factor analysis looking at indicators of wealth, with just six variables and two resulting factors 6 variables: Income, education, occupation, house value, public parks and crimes 2 factors: individual socioeconomic status and neighborhood socioeconomic status Factor Score – if value of variables are given then factor values can be predicted Interpretation
Views: 15692 Examrace
http://thedoctoraljourney.com/ Learn how to choose a research design for your quantitative research plan. For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
Views: 95929 The Doctoral Journey
Triangulation is a method used by qualitative researchers to check and establish validity in their studies by analysing a research question from multiple perspectives is to arrive at consistency across data sources or approaches, in fact, such inconsistencies should not be seen as weakening the evidence, but should be viewed as an opportunity to uncover deeper meaning in the data. Reference: http://www.nova.edu/ssss/QR/QR8-4/golafshani.pdf Reference: http://edis.ifas.ufl.edu/fy394 Reference: Patton, M. Q. (2002). Qualitative evaluation and research methods (3rd ed.). Thousand Oaks, CA: Sage Publications, Inc
Views: 109097 B2Bwhiteboard
In this qualitative methods podcast, I focus on the concept of triangulation by defining and explaining what triangulation is, discussing the different types of triangulation, addressing the advantages and disadvantages of using triangulation, and offering examples of using triangulation in qualitative research. This podcast was created in QUAL 9400 - Advanced Seminar in Qualitative Research taken at The University of Georgia during the fall of 2014.
Views: 35441 Danny G.
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: 524680 Phil Chan
A lecture given by Dr Victoria Clarke at the University the West of England, Bristol, UK, in November 2017. The lecture is entitled "Thematic analysis: What is it, when is it useful, and what does 'best practice' look like?" In this hour lecture, Victoria Clarke maps out different approaches to thematic analysis, and different conceptualisations of the 'theme', addresses common misconceptions and confusions about thematic analysis, and highlights the flexibility thematic analysis offers the qualitative researcher. Victoria Clarke is co-author with Virginia Braun of the highly cited paper 'Using Thematic Analysis in Psychology' (2006), and is widely regarded as a leading authority on thematic analysis.
Views: 26889 Victoria Clarke
This video is a one-hour lecture that Roberta E. Goldman, PHD delivered as part of the Harvard Catalyst lecture series in 2011. The lecture presents an overview of qualitative research methods that can be used in combination with each other, and in combination with quantitative methods for mixed methods primary care and public health study designs.
Views: 26502 Brown University
Thematic analysis is the most common form of analysis in qualitative research. It emphasizes pinpointing, examining, and recording patterns within data. Themes are patterns across data sets that are important to the description of a phenomenon and are associated to a specific research question. The themes become the categories for analysis. Thematic analysis is performed through the process of coding in six phases to create established, meaningful patterns. These phases are: familiarization with data, generating initial codes, searching for themes among codes, reviewing themes, defining and naming themes, and producing the final report. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 15936 Audiopedia
In this video I have explained that how we study relative gene expression by using quantitative Real Time PCR. To explain the concept real experimental data is used in this video. By watching this video you will learn following things - 1. Basic principle of quantitative real time PC 2. About SYBR Green and Taqman probe 3. How to study relative gene expression data by analyzing quantitative real time PCR data Please write to me in case of any question or doubt at - [email protected] Currently we are making a 60 Hrs video on complete cancer biology and also bigger video series on stem cell biology, immunology etc. For this cause we need your support, to donate please contact on whatsapp - +918698684792. For donation you can also contact on email - [email protected]
Views: 1866 Logical biology