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SPSS for questionnaire analysis:  Correlation analysis
 
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Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 498171 Phil Chan
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 476103 Quantitative Specialists
How To... Calculate Pearson's Correlation Coefficient (r) by Hand
 
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Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 398332 Eugene O'Loughlin
Survey Data Analysis using Google Form surveys
 
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Survey Data Analysis using Google Form surveys
Views: 27769 HiMrBogle
Survey Correlation
 
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Alan Jackson, President and CEO of The Jackson Group, talks about the benefits of correlating data from different surveys. Surveying, while important, is only the first step. Knowing what to do with the data and how it impacts and meshes with data from other areas is key.
Views: 938 JacksonGroupInc
Interpreting correlation coefficients in a correlation matrix
 
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/learn how to interpret a correlation matrix. http://youstudynursing.com/ Research eBook: http://amzn.to/1hB2eBd Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23Ac8cOayzxVDVGRl0q7QTjox A correlation matrix displays the correlation coefficients among numerous variables in a research study. This type of matrix will appear in hypothesis testing or exploratory quantitative research studies, which are designed to test the relationships among variables. In order to interpret this matrix you need to understand how correlations are measured. Correlation coefficients always range from -1 to +1. The positive or negative sign tells you the direction of the relationship and the number tells you the strength of the relationship. The most common way to quantify this relationship is the Pearson product moment correlation coefficient (Munro, 2005). Mathematically it is possible to calculate correlations with any level of data. However, the method of calculating these correlations will differ based on the level of the data. Although Pearson's r is the most commonly used correlation coefficient, Person's r is only appropriate for correlations between two interval or ratio level variables. When examining the formula for Person's r it is evident that part of the calculation relies on knowing the difference between individual cases and the mean. Since the distance between values is not known for ordinal data and a mean cannot be calculated, Pearson's r cannot be used. Therefore another method must be used. ... Recall that correlations measure both the direction and strength of a linear relationship among variables. The direction of the relationship is indicated by the positive or negative sign before the number. If the correlation is positive it means that as one variable increases so does the other one. People who tend to score high for one variable will also tend to score high for another varriable. Therefore if there is a positive correlation between hours spent watching course videos and exam marks it means that people who spend more time watching the videos tend to get higher marks on the exam. Remember that a positive correlation is like a positive relationship, both people are moving in the same direction through life together. If the correlation is negative it means that as one variable increases the other decreases. People who tend to score high for one variable will tend to score low for another. Therefore if there is a negative correlation between unmanaged stress and exam marks it means that people who have more unmanaged stress get lower marks on their exam. Remember that A negative correlation is like a negative relationship, the people in the relationship are moving in opposite directions. Remember that The sign (positive or negative) tells you the direction of the relationship and the number beside it tells you how strong that relationship is. To judge the strength of the relationship consider the actual value of the correlation coefficient. Numerous sources provide similar ranges for the interpretation of the relationships that approximate the ranges on the screen. These ranges provide guidelines for interpretation. If you need to memorize these criteria for a course check the table your teacher wants you to learn. Of course, the higher the number is the stronger the relationship is. In practice, researchers are happy with correlations of 0.5 or higher. Also note that when drawing conclusions from correlations the size of the sample as well as the statistical significance is considered. Remember that the direction of the relationship does not affect the strength of the relationship. One of the biggest mistakes people make is assuming that a negative number is weaker than a positive number. In fact, a correlation of -- 0.80 is just as high or just as strong as a correlation of +0.80. When comparing the values on the screen a correlation of -0.75 is actually stronger than a correlation of +0.56. ... Notice that there are correlations of 1 on a diagonal line across the table. That is because each variable should correlate perfectly with itself. Sometimes dashes are used instead of 1s. In a correlation matrix, typically only one half of the triangle is filled out. That is because the other half would simply be a mirror image of it. Examine this correlation matrix and see if you can identify and interpret the correlations. A great question for an exam would be to give you a correlation matrix and ask you to find and interpret correlations. What is the correlation between completed readings and unmanaged stress? What does it mean? Which coefficient gives you the most precise prediction? Which correlations are small enough that they would not be of much interest to the researcher? Which two correlations have the same strength? From looking at these correlations, what could a student do to get a higher mark on an exam? Comment below to start a conversation.
Views: 49622 NurseKillam
Splitting a Continuous Variable into High and Low Values
 
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In this video I show you how to create a new categorical variable from a continuous variable (e.g., high and low age). This is also known as a 'median split' approach.
Views: 51869 James Gaskin
Correlation, Positive, Negative, None, and Correlation Coefficient
 
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http://www.gdawgenterprises.com This video demonstrates different categories or types of correlation, including positive correlation, negative correlation, and no correlation. Also, strength of correlation is shown. The graphing calculator is used to quantify the strength of correlation by finding the correlation coefficient, sometimes called R.
Views: 42865 gdawgrapper
SPSS: Analyzing Subsets and Groups
 
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Instructional video on how to analyze subsets and groups of data using SPSS, statistical analysis and data management software. For more information, visit SSDS at https://ssds.stanford.edu.
Correlation and Chi-Square
 
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Correlation and Chi-Square
Views: 1625 Kimberly Rapoza
Correlation Analysis ROI Digital Marketing
 
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In a perfect world, we could calculate ROI from individual activities such as SEO, Social Media, website design, brochures, cooperative marketing efforts, etc. But consumer purchase decisions often take in to account multiple “touch points” before a buying decision is made. Also, the buying cycle may be 3-12 months; our client’s do sell milk, eggs, or bread. How do you connect today’s marketing activities with a purchase 6 months from now? Calculating ROI should be a long-term effort. If clients shared monthly sales data we could correlate sales with various metrics such as (1) Website visits, (2) social media metrics, (3) leads from website forms, and so on.
How To... Calculate a Correlation Coefficient (r) in Excel 2010
 
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Learn how to use the CORREL function and to manually calculate the correlation coefficient (r) in Excel 2010. This allows you to examine is there is a statistical correlation between two variables. Please note: Correlation is NOT causation!
Views: 142455 Eugene O'Loughlin
Correlations Google Doc
 
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Create a correlation matrix between any two stocks using Google Docs. This is a much more flexible platform than excel as data and dates are updated for you.
Views: 3589 Al On Options
Bivariate Analysis: Categorical and Numerical (ANOVA Test)
 
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How to do Bivariate Analysis when one variable is Categorical and the other is Numerical Analysis of Variance ANOVA test My website: http://people.brunel.ac.uk/~csstnns
Views: 7362 Noureddin Sadawi
Finding Correlations with Google Sheets
 
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Spreadsheet used in this video: https://docs.google.com/spreadsheets/d/13j77H4k4q_dJqJEUu2dNaJP_Aat9fbvU4X6cKUNfTMs/copy Created with TechSmith Snagit for Google Chrome™ http://goo.gl/ySDBPJ
Views: 2098 Josh Borzick
Re-Assigned Incidents & Breached Service Level Agreements
 
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Do re-assigned incidents correlate with a higher percentage of SLA breaches?
Nominal, ordinal, interval and ratio data: How to Remember the differences
 
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Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook Page: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Facebook: https://www.facebook.com/laura.killam LinkedIn: http://ca.linkedin.com/in/laurakillam Quantitative researchers measure variables to answer their research question. The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information. In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level. To remember these levels of measurement in order use the acronym NOIR or noir. The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories. The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair. Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories. Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical. Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order. While there is an order, it is also unknown how much distance is between each category. Values in an ordinal scale simply express an order. All nominal level tests can be run on ordinal data. Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured. To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode. Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known. Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement. For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81. If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy. Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero. Typically this level of measurement is only possible with physical measurements like height, weight and length. Any statistical tests can be used with ratio level data as long as it fits with the study question and design.
Views: 319872 NurseKillam
Testing for correlations in data with Excel
 
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Learn how to carry out tests for correlations in data using Microsoft Excel, including the Spearman’s rank correlation, and Pearson’s product moment correlation. https://global.oup.com/academic/product/research-methods-for-the-biosciences-9780198728498 This video relates to section 9.5 in the book Research Methods for the Biosciences third edition by Debbie Holmes, Peter Moody, Diana Dine, and Laurence Trueman. The video is narrated by Laurence Trueman. © Oxford University Press
Creating Correlation Table Using Data Analysis in Excel
 
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In this video, I will show you how to use Data Analysis tool in MS Excel to create correlation table of multiple numerical variables. We use Boston Housing dataset for demonstration. Please let me know if you have any questions. Thanks.
Views: 3509 IT_CHANNEL
Correlation Analysis More Than Two Variables: Urdu / Hindi
 
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This video shows how to perform correlation analysis between two or more than two variables. Correlation analysis shows the degree of association between two variables
How to find correlation in Excel with the Data Analysis Toolpak
 
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Click this link for more information on correlation coefficients plus more FREE Excel videos and tips: http://www.statisticshowto.com/what-is-the-pearson-correlation-coefficient/
Views: 30445 Stephanie Glen
SPSS
 
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SPSS Statistics is a software package used for statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions (2014) are officially named IBM SPSS Statistics. Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services). The software name stands for Statistical Package for the Social Sciences (SPSS), reflecting the original market, although the software is now popular in other fields as well, including the health sciences and marketing. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 166 Audiopedia
Analysis of Covariance (ANCOVA) - SPSS (part 1)
 
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I demonstrate how to perform an analysis of covariance (ANCOVA) in SPSS. The first part of the series is relevant to the ANCOVA tested through the conventional approach to doing so by getting SPSS to estimate adjusted means through the GLM univariate utility. In the second part of the series, I demonstrate the exact correspondence between ANCOVA and multiple regression. NB: The results of the analysis in this series found that males appear to have larger cranial capacities than females, even after controlling for the effects of body size. However, it should be important to emphasize that research has found that there are little to no general mean differences in IQ between males and females. Furthermore, there is neuroanatomical research to suggest that female brains appear to have more neurons per cubic cm than male brains. Thus, the difference in cranial capacity/brain size between the sexes may be counteracted by the differences in neuronal density.
Views: 224103 how2stats
Using Excel to Create a Correlation Matrix  || Correlation Matrix Excel
 
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http://alphabench.com/data/excel-correlation-matrix-tutorial.html This tutorial demonstrates how to create a correlation matrix in Excel. The example used in the video is for stock price changes over a one year period. The spreadsheet in the is example can be downloaded by visiting: http://www.alphabench.com/resources.html
Views: 48516 Matt Macarty
Keeping Track of  Qualitative Research Data using Excel
 
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This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.
Views: 36850 tamuwritingcenter
Using twitter to predict heart disease | Lyle Ungar | TEDxPenn
 
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Can Twitter predict heart disease? Day in and day out, we use social media, making it the center of our social lives, work lives, and private lives. Lyle Ungar reveals how our behavior on social media actually reflects aspects about our health and happiness. Lyle Ungar is a professor of Computer and Information Science and Psychology at the University of Pennsylvania and has analyzed 148 million tweets from more than 1,300 counties that represent 88 percent of the U.S. population. His published research has been focused around the area of text mining. He has published over 200 articles and holds eleven patents. His current research deals with statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 3852 TEDx Talks
Ways with Words | Big Data || Radcliffe Institute
 
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PANEL 2: BIG DATA The Internet, social media, and data mining have changed language and our ability to analyze usage, and increased sensitivities to the power of the words we use. This panel will explore how these new forms of discourse and analysis expand our understanding of the interplay of gender, personal narrative, and language, as well as data scraping that enables a statistical study of language usage by demographics. Ben Hookway (7:43), Chief Executive Officer, Relative Insight Lyle Ungar (20:53), Professor and Graduate Group Chair, Computer and Information Science, University of Pennsylvania Alice E. Marwick (36:19), Assistant Professor, Department of Communication and Media Studies, and Director, McGannon Center for Communication Research, Fordham University Moderator: Rebecca Lemov, Associate Professor of the History of Science, Harvard University Q&A (52:02)
Views: 964 Harvard University
Principal Components Analysis - SPSS (part 1)
 
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I demonstrate how to perform a principal components analysis based on some real data that correspond to the percentage discount/premium associated with nine listed investment companies. Based on the results of the PCA, the listed investment companies could be segmented into two largely orthogonal components.
Views: 188892 how2stats
Using Formulas with Google Form Responses
 
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This video is brought to you by Profound Cloud Google Forms are one of the easiest ways to collect data from your friends, family, colleagues and more. A great way to make Forms even more powerful is by taking actions upon the responses in a Google Spreadsheet. A lot of people get frustrated when they insert a formula into the responses Sheet, because the function doesn't seem to carry over for every new entry. It can be very time consuming, and frankly pretty irritating to manually extend the function for every new response. This video covers a really easy way to use the QUERY function in conjunction with Google Form responses. You will have to create a new tab within the Responses Sheet, and it's definitely a time saver. To watch the updated video and read the full article on the BetterCloud Monitor, visit: https://www.bettercloud.com/monitor/the-academy/using-formulas-with-google-form-responses/
Views: 64185 The Gooru
Find themes and analyze text in NVivo 9 | NVivo Tutorial Video
 
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Learn how to use NVivo's text analysis features to help you identify themes and explore the use of language in your project. For more information about NVivo visit: http://bit.ly/sQbS3m
Views: 101100 NVivo by QSR
UW Allen School Colloquium: Tim Althoff (Stanford University)
 
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Data Science for Human Well-being Abstract: The popularity of wearable and mobile devices, including smartphones and smartwatches, has generated an explosion of detailed behavioral data. These massive digital traces provides us with an unparalleled opportunity to realize new types of scientific approaches that provide novel insights about our lives, health, and happiness. However, gaining valuable insights from these data requires new computational approaches that turn observational, scientifically "weak" data into strong scientific results and can computationally test domain theories at scale. In this talk, I will describe novel computational methods that leverage digital activity traces at the scale of billions of actions taken by millions of people. These methods combine insights from data mining, social network analysis, and natural language processing to generate actionable insights about our physical and mental well-being. Specifically, I will describe how massive digital activity traces reveal unknown health inequality around the world, and how personalized predictive models can target personalized interventions to combat this inequality. I will demonstrate that modelling how fast we are using search engines enables new types of insights into sleep and cognitive performance. Further, I will describe how natural language processing methods can help improve counseling services for millions of people in crisis. I will conclude the talk by sketching interesting future directions for computational approaches that leverage digital activity traces to better understand and improve human well-being. Bio: Tim Althoff is a Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research advances computational methods to improve human well-being, combining techniques from Data Mining, Social Network Analysis, and Natural Language Processing. Prior to his PhD, Tim obtained M.S. and B.S. degrees from Stanford University and University of Kaiserslautern, Germany. He has received several fellowships and awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German Academic Exchange Service scholarship, the German National Merit Foundation scholarship, and a Best Paper Award by the International Medical Informatics Association. Tim's research has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times. April 17, 2018 This video is CC.
Unique Scientific Opportunities for the PMI National Research Cohort - April 28-29 - Day 2
 
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NIH hosted a public workshop on the NIH campus in Bethesda, Maryland, April 28-29, 2015, to consider visionary biomedical questions that could be addressed by the proposed national research cohort of one million or more volunteer participants. The workshop will result in a series of use cases describing the distinctive science that the cohort could enable in the near term and longer term. This workshop is one of four that is being convened by the Precision Medicine Initiative Working Group of the Advisory Committee to the (NIH) Director to help inform the vision for building the PMI national participant group that they have been tasked to develop. For more information on the workshop and PMI, visit http://www.nih.gov/precisionmedicine Agenda and time codes: Welcome - Bray Patrick Lake - 00:01 Near-Term Use Cases - Dr. Kathy Hudson - 02:54 Longer-Term Use Cases - Dr. Sachin Kheterpal - 1:35:45 Recap and Next Steps - Dr. Rick Lifton - 2:51:25
Stanford Webinar: Using Genomics, Wearables and Big Data to Manage Health and Disease
 
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Through genome sequencing, in combination with other omic information such as microbiome, methylome, metabolome, etc., data can be used to genetically predict disease risk. This information, combined with data collected through technology such as wearables, can help people manage disease and maintain healthy lives. Join Dr. Michael Snyder and Dr. Barry Starr as they explore the advances in genomic sequencing and how it can be used to predict, diagnose, and treat disease. You will learn: How genomics can be used to predict disease The poser of longitudinal profiling What data is collected from wearables and how they’re valuable to monitoring health How genome sequencing and big data can impact your health About the Speaker Michael Snyder is the Stanford Ascherman Professor and Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. Dr. Snyder received his Ph.D. training at the California Institute of Technology and carried out postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics that have been used for characterizing genomes, proteomes and regulatory networks.
Views: 3890 stanfordonline
Machine Learning Analytics Software Platform Podcast - Episode 233
 
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Source: https://www.spreaker.com/user/dabcc/machine-learning-analytics-software-plat In episode 233, Douglas Brown interviews Jerry Melnick, Chief Operating Officer at SIOS Technology Corp. Jerry and Douglas discuss the new SIOS iQ machine learning analytics software platform. Jerry does a great job diving deep in to SIOS iQ, what it is, how it works, why we should care and much more! Truly a much listen to episode! About SIOS iQ SIOS iQ is a machine learning analytics software platform designed to be your primary resource for IT operations information and issue resolution. SIOS iQ optimizes VMware environments to ensure business critical application environments are optimized for performance, efficiency, reliability, and capacity. Major features of the standard edition of SIOS iQ include: Performance Root Cause Analysis learns the relationships of objects and their normal patterns of behavior in a VMware infrastructure (hosts, VMs, application, network, storage, etc.); proactively identifies anomalies in behavior and the root causes of performance problems in any application; and recommends specific changes to resolve those problems. SIOS PERC Dashboard™ enables IT managers to quickly and easily ensure their VMware environment is optimized along four key quality of service dimensions: performance, efficiency, reliability and capacity (PERC). Provides mobile application ease of-use. The standard edition of SIOS iQ includes a variety of user enhancements, including the ability to expand charts to drill deeply into specific PERC areas, color-coded status indicators showing the criticality of issues - critical, warning and informational, and the inclusion of performance impact analysis showing all applications, VMs, hosts and data stores associated with a detected performance problem. Specialized Analytics for SQL Server provides advanced insight into performance issues associated with SQL Server deployments in VMware. SIOS iQ standard edition correlates interactions between SQL and infrastructure resources in the VMware environment to identify the deep root cause of performance issues. Enhanced Host Based Caching feature helps IT staff to easily determine how to improve storage performance for applications by using server side storage and host based caching (HBC). It analyzes the environment, including all blocks written to disk, and identifies the read ratio and the load profile to identify the VMs (and their disks) that will benefit most from HBC. SIOS iQ makes specific configuration recommendations such as how much cache to add and what cache block size to configure. It predicts the added performance that will be achieved if recommendations are implemented and shows the results in a single, easy-to-read chart. SIOS iQ Resource Optimization features. New standard edition of SIOS iQ provides an enhanced user interface for optimizing VMware resources by identifying and eliminating idle VMs and snapshot sprawl. SIOS iQ identifies under-used virtual machines and unnecessary snapshots and predicts the potential monthly savings that can be realized by eliminating them. Download a free version and/or trial Follow on Twitter account @SIOSTECH email: [email protected]
Views: 55 IT News
LAK18 #LAK18 - Learning Analytics and Knowledge Conference, Sydney, edQuire - CEO Dr Michael Cejnar
 
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From the LAK18 conference “Learning Analytics and Knowledge Conference ‘Schools Day’. Dr Michael Cejnar CEO from edQuire highlights the results from an 8 week trial of student computer learning in the classroom. The process involved using edQuire to collect, analyse and display powerful data to teachers and students (with outstanding results).
Open Data + Robust Workflow: Towards Reproducible Empirical Research on Organic Data
 
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Heng Xu, thought leader on information sciences and big data, and Nan Zhang, thought leader on robustness and reliability in web data analytics, discuss organic data. This presentation is recorded as part of the University of Florida Warrington College of Business' Reliable Research in Business initiative. To watch more videos about reliable research practices, please sign up here: https://warrington.ufl.edu/reliable-research-in-business/best-practices-for-reliable-research/.
Views: 86 UFWarrington
Convert Text to Numbers or Numbers to Text
 
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Check out my Blog: http://exceltraining101.blogspot.com This video covers how to convert or change text to numbers using several methods. Also how to convert or change numbers to text using some different methods. #exceltips #exceltipsandtricks #exceltutorial #doughexcel
Views: 400090 Doug H
12/7/18 Census Scientific Advisory Committee (CSAC) Meeting (Day 2)
 
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12/7/18 Census Scientific Advisory Committee (CSAC) Meeting (Day 2) 8:30AM - 2PM
Views: 57 uscensusbureau
how to create a bell curve for performance appraisal
 
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how to create a bell curve for performance appraisal
Views: 18781 mugas2002
The Human Microbiome: Emerging Themes at the Horizon of the 21st Century (Day 2)
 
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The Human Microbiome: Emerging Themes at the Horizon of the 21st Century (Day 2) Air date: Thursday, August 17, 2017, 8:15:00 AM Category: Conferences Runtime: 07:32:24 Description: The 2017 NIH-wide microbiome workshop will strive to cover advances that reveal the specific ways in which the microbiota influences the physiology of the host, both in a healthy and in a diseased state and how the microbiota may be manipulated, either at the community, population, organismal or molecular level, to maintain and/or improve the health of the host. The goal will be to seek input from a trans-disciplinary group of scientists to identify 1) knowledge gaps, 2) technical hurdles, 3) new approaches and 4) research opportunities that will inform the development of novel prevention and treatment strategies based on host/microbiome interactions over the next ten years. Author: NIH Permanent link: https://videocast.nih.gov/launch.asp?23423
Views: 1668 nihvcast
Toward data driven ontologies for mental function
 
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Event Date: August 20, 2018 Presenter: Russell A. Poldrack, Ph.D. The National Institutes of Health (NIH) Office of Behavioral and Social Science Research (OBSSR) hosts the 2017-2018 OBSSR Director’s Webinar Series. Abstract Psychological science has long been focused on the discovery of novel behavioral phenomena and the mechanistic explanation of those phenomena, which has led to a lack of cumulative conceptual progress. Dr. Poldrack will argue that the development of ontologies is essential for progress, but that these need to be tied directly to empirical data. He will provide an example from the domain of self-regulation, where we have used data-driven ontology development to describe the psychological structure of this domain and characterize its predictive validity with respect to real-world outcomes. Biography Russell A. Poldrack, Ph.D. Albert Ray Lang Professor of Psychology Professor (by courtesy) of Computer Science Stanford University Russell A. Poldrack is the Albert Ray Lang Professor in the Department of Psychology and Professor (by courtesy) of Computer Science at Stanford University, and Director of the Stanford Center for Reproducible Neuroscience. His research uses neuroimaging to understand the brain systems underlying decision making and executive function. His lab is also engaged in the development of neuroinformatics tools to help improve the reproducibility and transparency of neuroscience, including the Openneuro.org and Neurovault.org data sharing projects and the Cognitive Atlas ontology.
Measuring the Economy in a Digital Age
 
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Experts discuss methods of economic measurement. Speakers: Diana Farrell, Chief Executive Officer and President, JPMorgan Chase Institute Matthew D. Shapiro, Lawrence R. Klein Collegiate Professor of Economics, University of Michigan; Research Associate, National Bureau of Economic Research Hal R. Varian, Chief Economist, Google Presider: Sebastian Mallaby, Paul A. Volcker Senior Fellow for International Economics, Council on Foreign Relations This symposium is presented by the Maurice R. Greenberg Center for Geoeconomic Studies and is made possible through the generous support of Stephen C. Freidheim.
Predictive analytics
 
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Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 126 Audiopedia
Research to Care 2017 - Morning Session Presentations
 
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Watch the morning session of the Research to Care 9/11 Community Engagement Event at NYU Langone Medical Center and hear what we've learned so far about 9/11 health effects from the researchers themselves.
APS Award Address: Bringing Intelligence to Life
 
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At the 2015 APS Annual Convention, APS James McKeen Cattell Fellow Ian J. Deary discussed using the Scottish Mental Surveys, how intelligence test scores relate to aspects of people’s lives and stories of participants from the studies.
Views: 1799 PsychologicalScience
Mod-01 Lec-07B Exploratory Data Analysis – Part B
 
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Statistics for Experimentalists by Dr. A. Kannan,Department of Chemical Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 834 nptelhrd
ASC Science Sundays: Matthew Sullivan - Understanding Ocean Viruses
 
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The Ohio State University Science Sundays series presents Matthew Sullivan - Understanding ocean viruses may just save the earth and help cure your next ailment.

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