Search results “What is interpretation in research”

Views: 19660
Prof. LK Soni

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: 2280
YCMOU

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: 711687
Dr Nic's Maths and Stats

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: 145520
YaleUniversity

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: 666389
Kent Löfgren

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: 197648
Terry Shaneyfelt

This Lecture talks about Statistics or Data Analysis and Interpretation

Views: 1775
Cec Ugc

Dr. Joe Brown discusses labs and Blood work (WHITE BLOOD CELLS, ANEMIA, PROTEINS, CHOLESTEROL, KIDNEY AND LIVER FUNCTION, HORMONES) and how to read and understand your blood work. He gives descriptions, definitions, and explanations in detail.
Visit: Dr. Joe Brown at http://www.DoctorJoeBrown.com - CANCER SURVIVOR !!!
-CLICK SUBSCRIBE TO RECEIVE DR. JOE BROWN'S DAILY INFORMATION VIDEOS

Views: 251175
DrJoe Brown

Get the full course at: http://www.MathTutorDVD.com
The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more.
In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.

Views: 1093686
mathtutordvd

This tutorial provides an overview of statistical analyses in the social sciences. It distinguishes between descriptive and inferential statistics, discusses factors for choosing an analysis procedure, and identifies the difference between parametric and nonparametric procedures.

Views: 215349
The Doctoral Journey

#rawdatainterpretation
#rawdata
#meaninterpretation
For more information email [email protected]

Views: 629
Teachers in PH

This video will discuss how to interpret the information contained in a typical forest plot.

Views: 155170
Terry Shaneyfelt

Systematic reviewers have to decide whather or not studies are homogeneous enough to combine. This video will describe what heterogeneity is and some of the tests used to investigate it.

Views: 75196
Terry Shaneyfelt

Research Methodology, Data Analysis & Interpretation- 073

Views: 218
Bangkok School of Management

Interpreting & Translation Research Group, UWS
3rd Annual Research Symposium: "Technology and the Future of Translation" - Friday 16 and Saturday 17 July 2010
Closing Debate: “Technology is de-skilling the translation profession”
Passionate participants will debate whether technology lowers the entry threshold to the profession or on the contrary, allows professionals to translate faster, be more productive and subsequently more successful.
For the affirmative: Professor Anthony Pym (University Rovira I Virgili, Spain); Uli Priester (Manager, Anglo-German Communications); Suzan Piper, (Winner of the 2009 AUSIT
National Excellence in Translating Award).
For the negative: Sam Berner (Manager, Arabic Language Experts, President of AUSIT); Claudia Koch-McQuillan (Manager, McQuillan & Associates Pty Ltd and freelance English/German translator); Lachlan Simpson (Translation and Interpreting Technology Manager, Monash University)
Moderated by Frank Coletta, Senior Journalist and News Presenter, Channel TEN

Views: 272
AUSITEvents

Part 1 of a video correcting common interpreting mistakes.
Body Positioning 0:37
Speaking in First Person 2:35
Never add, omit, or substitute 4:29
Handling Side Conversations 7:19
Part 2 that finishes the conversation can be found at https://youtu.be/9e_nIDJV-Lk

Views: 428680
Clarity Interpreting

Research Methodology, Data Analysis and Interpretation- 065

Views: 155
Bangkok School of Management

Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling
Research Methodology
Population & Sample
Systematic Sampling
Cluster Sampling
Non Probability Sampling
Convenience Sampling
Purposeful Sampling
Extreme, Typical, Critical, or Deviant Case: Rare
Intensity: Depicts interest strongly
Maximum Variation: range of nationality, profession
Homogeneous: similar sampling groups
Stratified Purposeful: Across subcategories
Mixed: Multistage which combines different sampling
Sampling Politically Important Cases
Purposeful Sampling
Purposeful Random: If sample is larger than what can be handled & help to reduce sample size
Opportunistic Sampling: Take advantage of new opportunity
Confirming (support) and Disconfirming (against) Cases
Theory Based or Operational Construct: interaction b/w human & environment
Criterion: All above 6 feet tall
Purposive: subset of large population – high level business
Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow
Advantages of Sampling
Increases validity of research
Ability to generalize results to larger population
Cuts the cost of data collection
Allows speedy work with less effort
Better organization
Greater brevity
Allows comprehensive and accurate data collection
Reduces non sampling error. Sampling error is however added.
Population & Sample @2:25
Sampling @6:30
Systematic Sampling @9:25
Cluster Sampling @ 11:22
Non Probability Sampling @13:10
Convenience Sampling @15:02
Purposeful Sampling @16:16
Advantages of Sampling @22:34
#Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace
For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm
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

Views: 267570
Examrace

Video transcript:
"Have we discovered a new particle in physics?
Is a manufacturing process out of control?
What percentage of men are taller than Lebron James? How about taller than Yao Ming?
All of these questions can be answered using the concept of standard deviation.
For any set of data, the mean and standard deviation can be calculated. For example, five people may have the following amounts of money in their wallets: 21, 50, 62, 85, and 90. The mean is $61.60 and the standard deviation is $28.01.
How much does the data vary from the average? Standard deviation is a measure of spread, that is, how spread out a set of data is.
A low standard deviation tells us that the data is closely clustered around the mean (or average), while a high standard deviation indicates that the data is dispersed over a wider range of values.
It is used when the distribution of data is approximately normal, resembling a bell curve.
Standard deviation is commonly used to understand whether a specific data point is “standard” and expected or unusual and unexpected. Standard deviation is represented by the lowercase greek letter sigma. A data point’s distance from the mean can be measured by the number of standard deviations that it is above or below the mean. A data point that is beyond a certain number of standard deviations from the mean represents an outcome that is significantly above or below the average. This can be used to determine whether a result is statistically significant or part of expected variation, such as whether a bottle with an extra ounce of soda is to be expected or warrants further investigation into the production line.
The 68-95-99.7 rule tells us that about 68% of the data fall within one standard deviation of the mean. About 95% of data fall within two standard deviations of the mean. And about 99.7% of data fall within 3 standard deviations of the mean.
The average height of an American adult male is 5’10, with a standard deviation of 3 inches. Using the 68-95-99.7 rule, this means that 68% of American men are 5’10 plus or minus 3 inches, 95% of American men are 5’10 plus or minus 6 inches, and 99.7% of American men are 5’10 plus or minus 9 inches. So, this means only about .3% of American men deviate more than 9 inches from the average, with .15% taller than 6’7 and .15% shorter than 5’1. This reasoning suggests that Lebron James is 1 in 2500 and Yao Ming is 1 in 450 million.
In particle physics, scientists have what are called 5-sigma results, results that are five standard deviations above or below the mean. A result that varies this much can signify a discovery as it has only a 1 in 3.5 million chance that it is due to random fluctuation.
In summary, standard deviation is a measure of spread. Along with the mean, the standard deviation allows us to determine whether a value is statistically significant or part of expected variation."

Views: 769717
Jeremy Jones

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: 192949
Examrace

Research Methodology, Data Analysis & Interpretation Training

Views: 444
Bangkok School of Management

This is a brief lesson on the nature and value of qualitative data: What are qualitative data and why do they seem less science-y than numbers?
Main point: Qualitative data is not of lesser value than quantitative data.
Goal: To explain the importance of qualitative data (and its interpretation) for scholar practitioners. Related Goal: Explain the difference between data and evidence

Views: 28
Peter Williams

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: 388765
Bozeman Science

This brief video explains the components of LP Sensitivity Analysis using an Excel Solver Report. A few questions were also answered based on the following Linear Programming model.
Max 50A + 60B + 55C
s.t.
A + B + C ≤ 100
2A + 3B + 2C ≤ 300
2A + 3B + 2C ≥ 250
A + 2C ≥ 60
1. State the optimal solution
2. What is the optimal objective function value?
3. What would happen to the optimal solution if
a) the unit profit on B decreases by 20?
b) the unit profit on C decreases to 45?
c) the unit profits on A & C change to 53 (Simultaneous Changes-100% Rule)?
4. Interpret the reduced cost for A
5. What would happen to the objective function if
a) the RHS of constraint 1 increases by 5?
b) the RHS of constraint 2 decreases to 250?
c) the RHS of constraint 4 changes to 44?
6. Which constraints are binding?
7. What are the slack/surplus values?

Views: 70055
Joshua Emmanuel

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: 678406
Dr Nic's Maths and Stats

I explain what Cronbach's alpha is, how to interpret it, and discuss guidelines for acceptable levels.

Views: 120395
how2stats

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

Views: 185887
Southampton Education School

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.

Views: 194740
Let's Learn Public Health

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: 326657
Erich Goldstein

statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!

Views: 352220
statslectures

This Lecture talks about Research Methodology.

Views: 185811
Cec Ugc

Get the full course at: http://www.MathTutorDVD.com
In this lesson, you'll learn about the concept of variance in statistics. We'll discuss how variance is derived and what the equations of variance means. We will also cover how variance is very closely related to standard deviation in statistics.

Views: 439930
mathtutordvd

For additional information visit http://www.cancerquest.org/roberd-bostick-interview.html
Dr. Roberd Bostick is a professor of epidemiology at Rollins School of Public Health and Professor of Hematology and Medical Oncology at Emory University's School of Medicine and Winship Cancer Institute. Dr. Bostick is interested in a wide range of topics surrounding the epidemiology of cancer and cancer biomarkers. He is working to help develop simple tests that would indicate a person was at risk for developing cancer. In this interview he talks with us about his research into the relationship between diet and cancer. He also discussed the important topic of how the public should interpret medical information presented in the news and popular press.
To learn more about cancer and watch additional interviews, please visit the CancerQuest website at http://www.cancerquest.org

Views: 29
CancerQuest

Please watch: "logistic regression case study"
https://www.youtube.com/watch?v=M9Reulcqb2g --~--
Learn Basic statistics for Business Analytics
Business Analytics and Data Science is almost same concept. For both we need to learn Statistics. In this video I tried to create value on most used statistical methods for Data Science or Business Analytics for Statistical model Building.
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics any can handle a scientific, industrial, or societal problem. I value your time and effort that is why I have capture almost 20 statically concept in this video.
Learn Basic statistics for Business Analytics
Here I have capture how to learn Mean, how to learn Mode, How to learn median, Concept of Sleekness, Concept of Kurtosis, learn Variables, concept of Standard deviation, Concept of Covariance, Concept of correlation, Concept of regression, How to read regression formula, how to read regression graph, Concept of Intercept, Concept of slope coefficient, Concept of Random Error, Different types of regression Analysis, Concept ANOVA (Analysis of Variance), How to read ANOVA table, How to learn R square (Interpreted R square), Concept of Adjusted R Square, Concept of F test, Concept of Information Value, Concept of WOE, Concept of Variable inflation Factors.
Learn Basic statistics for Business Analytics
By this video you can Start Learn statistics for Data Science and Business analytics easily and effectively.
These statistics are useful when at the time of running linear regression, Logistic regression statistics models.
For Statistical Data Exploration you may need to see Meager of central tendency and Data Spread in Statistics. By Understanding Mean, Mode, Median, Sleekness, Kurtosis, Variance, Standard deviation.
Learn Basic statistics for Business Analytics
To understand statistical relationship between variables you can use Covariance, Correlation coefficient, Regression , ANOVA (Analysis of Variance) .
Learn Basic statistics for Business Analytics
To understand Strength of stastical relationship between variables you can use R square, Adjusted R square, F test.
If you want to understand variable importance in your stastical model you can use Information value (IV) and Weight of evidence (WOE) Concept. Information value and Weight of evidence mostly used in Logistic Regression Analysis.
Learn Basic statistics for Business Analytics
Variable inflation factors (VIF) is used for understanding, It is the stastical method to understand variable importance. What is the importance of this variable statically in the Regression model? By VIF we check Correlation between variable.
Learn Basic statistics for Business Analytics
At last I have explained when to use ANOVA, When to Use Linear regression and when to use Logistic regression.
Learn Basic statistics for Business Analytics
Thank you So much for watching this video, Hope I can add some value in your Journey as a Statistician, Business Analytics professional and Data Scientist professional.
Blogger :
http://koustav.analyticsanalysis.busi...
google plus:
https://plus.google.com/u/0/115750715
facebook link:
https://www.facebook.com/koustav.biswas.31945?ref=bookmarks
website:
https://www.analyticsanalysisbusiness.com

Views: 49662
Analytics Analysis Business

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: 85582
NurseKillam

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: 164945
Sanjay

Ruth Kastner, PhD, is a philosopher exploring the foundations of physics. She is on the faculty of the physics department at the State University of New York at Albany. She is also a research associate at the University of Maryland. She is author of The Transactional Interpretation of Quantum Mechanics: The Reality of Possibility and also Understanding Our Unseen World: Solving Quantum Riddles.
Here she points out that there are several interpretations of quantum mechanics that are very different from each other. She notes that there are many disagreements about the interpretation of the interpretations. She reviews the perspectives of great physicists such as Neils Bohr, Max Born, Ludwig Boltzmann, and David Bohm. She briefly describes the lesser known “transactional interpretation”. Then she focuses on the philosophical status of the crucial distinction between empirical and sub-empirical reality.
New Thinking Allowed host, Jeffrey Mishlove, PhD, is author of The Roots of Consciousness, Psi Development Systems, and The PK Man. Between 1986 and 2002 he hosted and co-produced the original Thinking Allowed public television series. He is the recipient of the only doctoral diploma in "parapsychology" ever awarded by an accredited university (University of California, Berkeley, 1980). He is a past vice-president of the Association for Humanistic Psychology; and is the recipient of the Pathfinder Award from that Association for his contributions to the field of human consciousness. He is also past-president of the non-profit Intuition Network, an organization dedicated to creating a world in which all people are encouraged to cultivate and apply their inner, intuitive abilities.
(Recorded on August 23, 2016)

Views: 15180
New Thinking Allowed with Jeffrey Mishlove

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

Views: 157938
Southampton Education School

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: 456729
Quantitative Specialists

This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode.
Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?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/mean-and-median/v/mean-median-and-mode?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/histograms/v/interpreting-histograms?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 is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan AcademyÂÃÂªs 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: 1848277
Khan Academy

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: 783599
Dr Nic's Maths and Stats

A step-by-step approach for choosing an appropriate statistcal test for data analysis.

Views: 368781
TheRMUoHP Biostatistics Resource Channel

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: 202868
Medzcool

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: 315415
CrashCourse

Google Tech Talk
January 6, 2011
Presented by Ron Garret.
ABSTRACT
Richard Feynman once famously quipped that no one understands quantum mechanics, and popular accounts continue to promulgate the view that QM is an intractable mystery (probably because that helps to sell books). QM is certainly unintuitive, but the idea that no one understands it is far from the truth. In fact, QM is no more difficult to understand than relativity. The problem is that the vast majority of popular accounts of QM are simply flat-out wrong. They are based on the so-called Copenhagen interpretation of QM, which has been thoroughly discredited for decades. It turns out that if Copenhagen were true then it would be possible to communicate faster than light, and hence send signals backwards in time. This talk describes an alternative interpretation based on quantum information theory (QIT) which is consistent with current scientific knowledge. It turns out that there is a simple intuition that makes almost all quantum mysteries simply evaporate, and replaces them with an easily understood (albeit strange) insight: measurement and entanglement are the same physical phenomenon, and you don't really exist.
Slides are available here:
https://docs.google.com/a/google.com/present/edit?id=0AelF4upZ7VjZZGM1eGttOGJfNDNkenFtNnFkaw&hl=en
Link to the paper:
http://www.flownet.com/ron/QM.pdf
About the speaker:
Dr. Ron Garret was an AI and robotics researcher at the NASA Jet Propulsion Lab for fifteen years before taking a year off to work at Google in June of 2000. He was the lead engineer on the first release of AdWords, and the original author of the Google Translation Console. Since leaving Google he has started a new career as an entrepreneur, angel investor and filmmaker. He has co-founded three startups, invested in a dozen others, and made a feature-length documentary about homelessness.

Views: 1533989
GoogleTechTalks

To help support this ministry click here: http://www.patreon.com/inspiringphilosophy
The Book of Job is one of the most misunderstood books in the Bible. I attempt to clear that up by offering a different perspective than most people know today. The traditional surface understanding doesn't make sense with scripture or the actual text. This new approach will clear up several misconceptions.
In regards to Job 2:3. It is interesting you brought this up, because this was something I had planned to talk about but left it because I thought I was getting to technical. Commentators like John MacArthur note the Hebrew is the same expression for what Satan uses in Job 1:9, "for nothing." Basically God has turned Satan's own words against him. God is pointing out Satan was wrong that Job only worshipped his stuff. Which is something I pointed out at the end that Satan, Job, and Job's friends were all wrong in this. God is pointing out here Job is not suffering because Satan was right about him in this, but there is deeper thing taking place. Job didn't have his stuff taken away for the cause Satan had thought.
*If you are caught excessively commenting, insulting, or derailing then your comments will be removed. If you do not like it you can watch this video:
http://www.youtube.com/watch?v=mn0Hq-sy3Wg&feature=plcp
"Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use."

Views: 733353
InspiringPhilosophy

Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 633971
statisticsfun

Term paper writing service

Annotated bibliography example mla format 2010 dodge

My best paper writing service

Jack in the box jobs applications

© 2018 Essay about my trip to

There are a number of causes of male hair loss, including: Male pattern baldness Alopecia Fungal infections Psychological disorders Chemotherapy side effects Nutrient deficiencies Hormonal imbalances Lack of circulation on the scalp Stress. It is important to note that hair loss occurs in women as well, for some similar reasons, and some different ones. We will cover hair loss in women in a separate article. Men predominantly suffer from this health condition, particularly male pattern baldness and early onset hair loss from age 20-40. However, there are ways to slow down the rate of male hair loss or baldness, and even stimulating the hair follicles to reproduce hair. A few home remedies for hair loss and baldness issue are discussed below.