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Research Methodology Lecture 30 - Interpretation of Results and Discussion - Dr D S Janbandhu
 
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Research Methodology Lecture 30 - Dr D S Janbandhu Dual Language - English and Marathi School of Architecture, Science and Technology (AST), Yashwantrao Chavan Maharashtra Open University (YCMOU), Nashik - 422222, Maharashtra, India Visit Us Here: https://www.facebook.com/ycmouast/
Views: 2576 YCMOU
Practice 4 - Analyzing and Interpreting Data
 
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Science and Engineering Practice 3: Analyzing and Interpreting Data Paul Andersen explains how scientists analyze and interpret data. Data can be organized in a table and displayed using a graph. Students should learn how to present and evaluate data. Intro Music Atribution Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License
Views: 60846 Bozeman Science
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: 154458 YaleUniversity
Understanding the p-value - Statistics Help
 
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With Spanish subtitles. This video explains how to use the p-value to draw conclusions from statistical output. It includes the story of Helen, making sure that the choconutties she sells have sufficient peanuts. You might like to read my blog: http://learnandteachstatistics.wordpress.com
Views: 753000 Dr Nic's Maths and Stats
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; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 691634 Kent Löfgren
What is ANALYTICAL SKILL? What does ANALYTICAL SKILL mean? ANALYTICAL SKILL meaning & explanation
 
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I MAKE CUTE BABIES - https://amzn.to/2DqiynS What is ANALYTICAL SKILL? What does ANALYTICAL SKILL mean? ANALYTICAL SKILL meaning - ANALYTICAL SKILL definition - ANALYTICAL SKILL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Analytical skill is the ability to visualize, articulate, conceptualize or solve both complex and uncomplicated problems by making decisions that are sensible given the available information. Such skills include demonstration of the ability to apply logical thinking to breaking complex problems into their component parts. In 1999, Richards J. Heuer Jr., explained that: "Thinking analytically is a skill like carpentry or driving a car. It can be taught, it can be learned, and it can improve with practice. But like many other skills, such as riding a bike, it is not learned by sitting in a classroom and being told how to do it. Analysts learn by doing." To test for analytical skills one might be asked to look for inconsistencies in an advertisement, put a series of events in the proper order, or critically read an essay. Usually standardized tests and interviews include an analytical section that requires the examiner to use their logic to pick apart a problem and come up with a solution. Although there is no question that analytical skills are essential, other skills are equally required. For instance in systems analysis the systems analyst should focus on four sets of analytical skills: systems thinking, organizational knowledge, problem identification, and problem analyzing and solving.
Views: 27521 The Audiopedia
Media Research : Statistics or Data Analysis and Interpretation
 
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This Lecture talks about Statistics or Data Analysis and Interpretation
Views: 1828 Cec Ugc
Epidemiological Studies - made easy!
 
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This video gives a simple overview of the most common types of epidemiological studies, their advantages and disadvantages. These include ecological, case-series, case control, cohort and interventional studies. It also looks at systematic reviews and meta-analysis. This video was created by Ranil Appuhamy Voiceover - James Clark -------------------------------------------------------------------------------------------------------- Disclaimer: These videos are provided for educational purposes only. Users should not rely solely on the information contained within these videos and is not intended to be a substitute for advice from other relevant sources. The author/s do not warrant or represent that the information contained in the videos are accurate, current or complete and do not accept any legal liability or responsibility for any loss, damages, costs or expenses incurred by the use of, or reliance on, or interpretation of, the information contained in the videos.
AncestryDNA | You Received Your Results. Now What? Part 1 | Ancestry
 
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You took an AncestryDNA test. You waited the 6-8 weeks for your results. Now they are in. But, what does it all mean? Join Crista Cowan as she walks you through a detailed explanation of your AncestryDNA results - what does it all mean and what to do next. Start Your Journey Today: http://www.ancestry.com/s89204/t38352/rd.ashx Subscribe: http://www.youtube.com/channel/UCsc0AQkAh_2cQmxqwD6VWRw?sub_confirmation=1 About Ancestry: Bringing together science and self-discovery, Ancestry helps everyone, everywhere discover the story of what led to them. Our sophisticated engineering and technology harnesses family history and consumer genomics, combining billions of rich historical records and millions of family trees to over 10 million and counting to provide people with deeply meaningful insights about who they are and where they come from. We’ve pioneered and defined this category, developing new innovations and technologies that have reinvented how people make family history discoveries. And these discoveries can give everyone a greater sense of identity, relatedness, and their place in the world. Connect with Ancestry: Visit Ancestry’s Official Site: https://www.ancestry.com/ Like Ancestry on Facebook: https://www.facebook.com/Ancestry/ Follow Ancestry on Twitter: https://twitter.com/Ancestry Follow Ancestry on Instagram: https://www.instagram.com/ancestry AncestryDNA | You Received Your Results. Now What? Part 1 | Ancestry https://www.youtube.com/user/AncestryCom
Views: 223101 Ancestry
Research Methodology (Part 1 of 3): 5 Steps, 4 Types and 7 Ethics in Research
 
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This lecture by Dr. Manishika Jain explains the basics of research methodology, steps in scientific research, correlation and experimental research, variables (dependent, independent and confounding) and finally ethics in research. Research @1:44 Perceive Question @2:21 Formulate Hypothesis @2:38 Test Hypothesis @3:22 Draw Conclusion @5:21 Report Results @6:15 Descriptive Methods @9:27 Naturalistic Observation @9:50 Laboratory [email protected]:40 Case Studies @13:43 Surveys @15:41 Ethics @28:45 #Surveys #Naturalistic #Conclusion #Perceive #Hypothesis #Formulate #Ethics #Research #Methodology #Manishika #Examrace For more information visit http://www.examrace.com/Study-Material/Psychology/Psychology-FlexiPrep-Program/Postal-Courses/Examrace-Psychology-Series.htm or email [email protected] To know more about Dr. Jain visit - https://www.examrace.com/About-Examrace/Company-Information/Examrace-Authors.html Research Methodology playlist - https://www.youtube.com/playlist?list=PLW9kB_HKs3_N4-55qIi36fwdW2UaySm9Y
Views: 506305 Examrace
How to Read a CT Scan of the Head - MEDZCOOL
 
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Reading a CT scan in a systematic way in the Emergency Department can help you quickly and thoroughly assess for any neurological pathology. Remember the mnemonic "Blood Can Be Very Bad" Follow Us on Social Media: Facebook: https://www.facebook.com/medzcoolmedia Instagram: https://www.instagram.com/medzcool/ Twitter: https://twitter.com/medzcool CodeHealth: https://codehealth.io/medzcool Support Medzcool in Making More Educational Content: https://www.patreon.com/medzcool
Views: 226425 Medzcool
Results, Discussion Conclusion chapters
 
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This video presentation focuses on writing the results, Discussion and Conclusion chapters of a Masters or PhD thesis.
Views: 66971 cecile badenhorst
Research Methodology (Part 3 of 3): 28 Types of Variables - Independent & Dependent Variables
 
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Dr. Manishika Jain in this lecture explains the meaning of variables and explain the 28 types of variables: Attribute or Quality Differ in magnitude Control of Variables IV, DV, Mediating Variable IV: Characteristic of experiment that is manipulated DV: Variable measured Mediating/intervening – hypothetical concept explain relation b/w variables (Parent’s status - child’s status by education) Confounding – extra variable (effect of activity on obesity – AGE) Dig! Dig! Effect of noise on test score IQ varies with age Quantitative vs. Qualitative Quantitative: Numbers (Interval/ratio) Qualitative: attitude (good or bad) – can be compared not measured (nominal/ordinal) Variables based on Scaling Continuous, Discrete & Categorical Variable Absolute vs. Relative Absolute: Meaning doesn’t imply reference to property of others Relative: Relationship b/w persons and objects Global, Relational & Contextual Global: Only to the level at which they are defined Relational: Relationship of a unit Contextual: Super-unit (all at lower level receive same value) – disaggregation Analytical & Structural: From lower level data – aggregation Active: Can be manipulated – experimental Attribute: Pre-existing quality Binary/Dichotomous: pass/fail (0 - 1) Endogenous Exogenous Dummy – record categorical variable in series of binary variable Latent – cannot be observed (intelligence) Manifest – indicates presence of latent (IQ score) Polychotomous – with 2 or more possible values For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm Variables @0:16 Control of Variables @0:38 IV, DV, Mediating Variable @4:26 Quantitative vs. Qualitative @6:28 Quantitative @6:31 Qualitative @6:53 Variables based on Scaling @7:16 Continuous, Discrete & Categorical Variable @11:10 Absolute vs. Relative @12:28 Global, Relational & Contextual @13:05 #Contextual #Categorical #Categorical #Scaling #Measured #Confounding #Hypothetical #Mediating #Independent #Variables #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm
Views: 70421 Examrace
Analysing your Interviews
 
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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/
Null Hypothesis, p-Value, Statistical Significance, Type 1 Error and Type 2 Error
 
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SKIP AHEAD: 0:39 – Null Hypothesis Definition 1:42 – Alternative Hypothesis Definition 3:12 – Type 1 Error (Type I Error) 4:16 – Type 2 Error (Type II Error) 4:43 – Power and beta 6:33 – p-Value 8:39 – Alpha and statistical significance 14:15 – Statistical hypothesis testing (t-test, ANOVA & Chi Squared) For the text of this video click here http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/ For my video on Confidence Intervals click here http://www.stomponstep1.com/confidence-interval-interpretation-95-confidence-interval-90-99/
Views: 391377 Stomp On Step 1
Student's t-test
 
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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 446245 Bozeman Science
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 813900 Claus Ebster
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: 220726 Examrace
Research Data Tabulation and Interpretation - Raw Data - Solve the Mean
 
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#rawdatainterpretation #rawdata #meaninterpretation For more information email [email protected]
Views: 687 Teachers in PH
How to Present Your Findings
 
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Watch the full conference on Bigmarker: https://www.bigmarker.com/doctoralnet/room136 How to design and write the chapter to present your findings. Learn how to organize your results so as to make its impact clear.
Views: 26618 BigMarker
How to analyze a case study?
 
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This presentation describes an approach to analyze a case study - especially case studies from management discipline. Dr. Pradeep Racherla, Program Director & Associate Professor Marketing, Woxsen School of Business, elucidates different components of a case study and offers a framework to analyze a case study.
Views: 169942 Sanjay
Choosing a Statistical Test
 
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In common health care research, some hypothesis tests are more common than others. How do you decide, between the common tests, which one is the right one for your research? Thank you to the Statistical Learning Center for their excellent video on the same topic. https://www.youtube.com/rulIUAN0U3w
Views: 349436 Erich Goldstein
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. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 716884 Dr Nic's Maths and Stats
How to Interpret and Use a Relative Risk and an Odds Ratio
 
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RR and OR are commonly used measures of association in observational studies. In this video I will discuss how to interpret them and how to apply them to patient care
Views: 205685 Terry Shaneyfelt
What is a P Value? What does it tell us?
 
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Discussion about the p value... what it means and how to interpret it. If the null were true! reject or fail to reject?
Views: 359151 MrNystrom
Research Methodology, Data Analysis and Interpretation
 
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Research Methodology, Data Analysis and Interpretation- 065
What is DESCRIPTIVE STATISTICS? What does DESCRIPTIVE STATISTICS mean?
 
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✪✪✪ I MAKE CUTE BABIES ✪ https://amzn.to/2DqiynS ✪✪✪ What is DESCRIPTIVE STATISTICS? What does DESCRIPTIVE STATISTICS mean? DESCRIPTIVE STATISTICS meaning - DESCRIPTIVE STATISTICS definition - DESCRIPTIVE STATISTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Descriptive statistics are statistics that quantitatively describe or summarize features of a collection of information. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, are not developed on the basis of probability theory. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, the proportion of subjects with related comorbidities etc. Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness. Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation. For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. For example, a player who shoots 33% is making approximately one shot in every three. The percentage summarizes or describes multiple discrete events. Consider also the grade point average. This single number describes the general performance of a student across the range of their course experiences. The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot. In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behavior by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future. Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable's distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf display.
Views: 13455 The Audiopedia
Things To Consider When Interpreting Research (inspired by Dr. Travis Beck)
 
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NOTE: Many of the thoughts and ideas for this presentation came from or have been inspired by Dr. Travis Beck, University of Oklahoma A great thought provoking and educational narrated powerpoint by Jeremy Loenneke PhD(c) on the importance of critically analyzing research findings. Sorry the audio is a bit droned, had to record the audio portion from a cell phone
Views: 4076 De Novo
Research Methodology, Data Analysis & Interpretation
 
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Research Methodology, Data Analysis & Interpretation- 073
Writing Tip #3: Writing Qualitative Findings Paragraphs
 
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This video presents a "formula" for writing qualitative findings paragraphs in research reports. It presents the Setup-Quote-Comment model (SQC).
Data analysis and interpretation - part 1 (Antonio Ghezzi)
 
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Video related to Polimi Open Knowledge (POK) http://www.pok.polimi.it This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). http://creativecommons.org/licenses/by-nc-sa/4.0/
Correlation & Regression: Concepts with Illustrative examples
 
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We can use scatter plots to understand the relationships between variables, but it is applied only for obvious relationships like Temperature and Viscosity. Sometimes, it is not possible to comment about relationship between variables only looking at the graph. “CORRELATION & REGRESSION” are very important mathematical concepts to define relationship between variables. This is the topic for video. I have tried to explain these concepts with the help of practical examples which will be very easy to understand. I have also explained the procedure about how to create a “CORRELATION & REGRESSION ANALYSIS” in Microsoft Excel. Everything is with steps, snapshots and examples, which will be very easy to understand. I have also covered statistics part like how to read and understand “SIGNIFICANCE F and P-values” I am sure, you will liked it. The next important point is about communication for launching of my website: I am very glad to announce a launching of my website related to Lean, Six Sigma and Personality development coaching. Please visit to my website by clicking on this link and add your valuable views and comments regarding it. https://www.learnandapply.org/ I have also created recommendation page to share secret of my success with you. You can visit this link to review it. https://www.learnandapply.org/recommondations For the best audio recording experience, you can visit Samson product by visiting this link: https://amzn.to/2HkvFva For better management of your time and skills, you must read 'Managing Oneself' book: https://amzn.to/2Hmf0Ht I am sure you will like this video as well as initiatives I have started.
Views: 109324 LEARN & APPLY
Research Methodology, Data Analysis & Interpretation Training
 
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Research Methodology, Data Analysis & Interpretation Training
What is GROUNDED THEORY? What does GROUNDED THEORY mean? GROUNDED THEORY meaning & explanation
 
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I MAKE CUTE BABIES - https://amzn.to/2DqiynS What is GROUNDED THEORY? What does GROUNDED THEORY mean? GROUNDED THEORY meaning - GROUNDED THEORY definition - GROUNDED THEORY explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Grounded theory (GT) is a systematic methodology in the social sciences involving the construction of theory through the analysis of data. Grounded theory is a research methodology which operates almost in a reverse fashion from social science research in the positivist tradition. Unlike positivist research, a study using grounded theory is likely to begin with a question, or even just with the collection of qualitative data. As researchers review the data collected, repeated ideas, concepts or elements become apparent, and are tagged with codes, which have been extracted from the data. As more data are collected, and as data are re-reviewed, codes can be grouped into concepts, and then into categories. These categories may become the basis for new theory. Thus, grounded theory is quite different from the traditional model of research, where the researcher chooses an existing theoretical framework, and only then collects data to show how the theory does or does not apply to the phenomenon under study. Grounded theory combines diverse traditions in sociology, positivism and symbolic interactionism as it is according to Ralph, Birks & Chapman (2015) "methodologically dynamic". Glaser's strong training in positivism enabled him to code the qualitative responses, however Strauss's training looked at the "active" role of people who live in it. Strauss recognized the profundity and richness of qualitative research regarding social processes and the complexity of social life, Glaser recognized the systematic analysis inherent in quantitative research through line by line examination, followed by the generation of codes, categories, and properties. According to Glaser (1992), the strategy of Grounded Theory is to take the interpretation of meaning in social interaction on board and study "the interrelationship between meaning in the perception of the subjects and their action". Therefore, through the meaning of symbols, human beings interpret their world and the actors who interact with them, while Grounded Theory translates and discovers new understandings of human beings' behaviors that are generated from the meaning of symbols. Symbolic interactionism is considered to be one of the most important theories to have influenced grounded theory, according to it understanding the world by interpreting human interaction, which occurs through the use of symbols, such as language. According to Milliken and Schreiber in Aldiabat and Navenec, the grounded theorist's task is to gain knowledge about the socially-shared meaning that forms the behaviors and the reality of the participants being studied. Once the data are collected, grounded theory analysis involves the following basic steps: 1. Coding text and theorizing: In grounded theory research, the search for the theory starts with the very first line of the very first interview that one codes. It involves taking a small chunk of the text where line by line is being coded. Useful concepts are being identified where key phrases are being marked. The concepts are named. Another chunk of text is then taken and the above-mentioned steps are being repeated. According to Strauss and Corbin, this process is called open coding and Charmaz called it initial coding. Basically, this process is breaking data into conceptual components. The next step involves a lot more theorizing, as in when coding is being done examples are being pulled out, examples of concepts together and think about how each concept can be related to a larger more inclusive concept. This involves the constant comparative method and it goes on throughout the grounding theory process, right up through the development of complete theories. 2. Memoing and theorizing: Memoing is when the running notes of each of the concepts that are being identified are kept. It is the intermediate step between the coding and the first draft of the completed analysis. Memos are field notes about the concepts in which one lays out their observations and insights. Memoing starts with the first concept that has been identified and continues right through the process of breaking the text and of building theories. 3. Integrating, refining and writing up theories: Once coding categories emerges, the next step is to link them together in theoretical models around a central category that hold everything together.
Views: 18453 The Audiopedia
Understanding Confidence Intervals: Statistics Help
 
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This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6
Views: 713227 Dr Nic's Maths and Stats
Young people in the design, conduct, interpretation of research
 
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Young people in the design, conduct, interpretation of research IAYMH 2015
What is INTERPRETIVISM? What does INTERPRETIVISM mean? INTERPRETIVISM meaning & explanation
 
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✪✪✪ I MAKE CUTE BABIES ✪ https://amzn.to/2DqiynS ✪✪✪ What is INTERPRETIVISM? What does INTERPRETIVISM mean? INTERPRETIVISM meaning - INTERPRETIVISM definition - INTERPRETIVISM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Interpretivism is a school of thought in contemporary jurisprudence and the philosophy of law. The main claims of interpretivism are that: 1. Law is not a set of given data, conventions or physical facts, but what lawyers aim to construct or obtain in their practice. This marks a first difference between interpretivism and legal positivism. But the refusal that law be a set of given entities opposes interpretivism to natural law too. 2. There is no separation between law and morality, although there are differences. This is not in accordance with the main claim of legal positivism. 3. Law is not immanent in nature nor do legal values and principles exist independently and outside of the legal practice itself. This is the opposite of the main claim of natural law theory. In the English speaking world, interpretivism is usually identified with Ronald Dworkin's theses on the nature of law as discussed in his text titled Law's Empire, which is sometimes seen as a third way between natural law and legal positivism. The concept also includes continental legal hermeneutics and authors such as Helmut Coing and Emilio Betti. Legal hermeneutics can be seen as a branch of philosophical hermeneutics, whose main authors in the 20th century are Heidegger and Gadamer, both drawing on Husserl's phenomenology. Hermeneutics has now expanded to many varied areas of research in the social sciences as an alternative to a conventionalist approach. In a wider sense, interpretivism includes even the theses of, in chronological order, Josef Esser, Theodor Viehweg, Chaim Perelman, Wolfgang Fikentscher, Castanheira Neves, Friedrich Müller, Aulis Aarnio and Robert Alexy.
Views: 12917 The Audiopedia
How to do visual (formal) analysis in art history
 
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Giovanni Bellini, Madonna of the Meadow, c. 1500, oil and egg on synthetic panel, transferred from wood, 67.3 x 86.4 cm (The National Gallery) Speakers: Dr. Steven Zucker and Dr. Beth Harris
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: 343196 CrashCourse
Fundamentals of Qualitative Research Methods: What is Qualitative Research (Module 1)
 
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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 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: 202000 YaleUniversity
Fundamentals of Qualitative Research Methods: Focus Groups (Module 4)
 
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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 4. Morgan D. Focus groups. Annual Review Sociology 1996;22:129-152. 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: 79288 YaleUniversity
How I Got Into Dream Interpretation & Dream Research
 
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In this more personal video, I answer a question viewers have asked for awhile now - How and why did I get into dream research and interpretation. For more info on dream interpretation and lucid dreaming visit us at http://www.philosophyofdreams.com
Research & Evaluation for Testing & Interpretation of Evidence in Publicly Funded Forensic Labs
 
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​This webinar provided details and guidance for potential applicants to NIJ’s solicitation, “Research and Evaluation for the Testing and Interpretation of Physical Evidence in Publicly Funded Forensic Laboratories.” The intent of this research and evaluation effort is to direct findings toward the forensic community, offering best practices for the most efficient, accurate, reliable, and cost-effective methods for the identification, analysis, and interpretation of physical evidence in publicly funded crime laboratories. Through this work, we aim to have a direct and immediate impact on laboratory efficiency and assist in making laboratory policy decisions. (Opinions or points of view expressed represent the speaker and do not necessarily represent the official position or policies of the U.S. Department of Justice. Any product or manufacturer discussed is presented for informational purposes only and do not constitute product approval or endorsement by the U.S. Department of Justice.)
Fundamentals of Qualitative Research Methods: Scientific Rigor (Module 6)
 
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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 6. Mays N, Pope C. Qualitative research: rigour and qualitative research. British Medical Journal 1995; 311:109-112. Barbour R. Checklists for improving rigour in qualitative research: a case for the tail wagging the dog? British Medical Journal 2001; 322:1115-1117. 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: 32806 YaleUniversity
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1517235 ExcelIsFun
What is BIOSTATISTICS? What does BIOSTATISTICS mean? BIOSTATISTICS meaning, definition & explanation
 
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✪✪✪ I MAKE CUTE BABIES ✪ https://amzn.to/2DqiynS ✪✪✪ What is BIOSTATISTICS? What does BIOSTATISTICS mean? BIOSTATISTICS meaning - BIOSTATISTICS pronunciation - BIOSTATISTICS definition - BIOSTATISTICS explanation - How to pronounce BIOSTATISTICS? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Biostatistics is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch of this is medical biostatistics, which is exclusively concerned with medicine and health. Almost all educational programmes in biostatistics are at postgraduate level. They are most often found in schools of public health, affiliated with schools of medicine, forestry, or agriculture, or as a focus of application in departments of statistics. In the United States, where several universities have dedicated biostatistics departments, many other top-tier universities integrate biostatistics faculty into statistics or other departments, such as epidemiology. Thus, departments carrying the name "biostatistics" may exist under quite different structures. For instance, relatively new biostatistics departments have been founded with a focus on bioinformatics and computational biology, whereas older departments, typically affiliated with schools of public health, will have more traditional lines of research involving epidemiological studies and clinical trials as well as bioinformatics. In larger universities where both a statistics and a biostatistics department exist, the degree of integration between the two departments may range from the bare minimum to very close collaboration. In general, the difference between a statistics program and a biostatistics program is twofold: (i) statistics departments will often host theoretical/methodological research which are less common in biostatistics programs and (ii) statistics departments have lines of research that may include biomedical applications but also other areas such as industry (quality control), business and economics and biological areas other than medicine. The advent of modern computer technology and relatively cheap computing resources have enabled computer-intensive biostatistical methods like bootstrapping and resampling methods. Furthermore, new biomedical technologies like microarrays, next generation sequencers (for genomics) and mass spectrometry (for proteomics) generate enormous amounts of (redundant) data that can only be analyzed with biostatistical methods. For example, a microarray can measure all the genes of the human genome simultaneously, but only a fraction of them will be differentially expressed in diseased vs. non-diseased states. One might encounter the problem of multicolinearity: Due to high intercorrelation between the predictors (in this case say genes), the information of one predictor might be contained in another one. It could be that only 5% of the predictors are responsible for 90% of the variability of the response. In such a case, one would apply the biostatistical technique of dimension reduction (for example via principal component analysis). Classical statistical techniques like linear or logistic regression and linear discriminant analysis do not work well for high dimensional data (i.e. when the number of observations n is smaller than the number of features or predictors ). As a matter of fact, one can get quite high R2-values despite very low predictive power of the statistical model. These classical statistical techniques (esp. least squares linear regression) were developed for low dimensional data (i.e. where the number of observations n is much larger than the number of predictors ). In cases of high dimensionality, one should always consider an independent validation test set and the corresponding residual sum of squares (RSS) and R2 of the validation test set, not those of the training set. In recent times, random forests have gained popularity. This technique, invented by the statistician Leo Breiman, generates a lot of decision trees randomly and uses them for classification (In classification the response is on a nominal or ordinal scale, as opposed to regression where the response is on a ratio scale). Decision trees have of course the advantage that you can draw them and interpret them (even with a very basic understanding of mathematics and statistics).
Views: 13285 The Audiopedia

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