Search results “Statistical analysis of research”

Seven different statistical tests and a process by which you can decide which to use.
The tests are:
Test for a mean,
test for a proportion,
difference of proportions,
difference of two means - independent samples,
difference of two means - paired,
chi-squared test for independence and
regression.
This video draws together videos about Helen, her brother, Luke and the choconutties.
There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.

Views: 680130
Dr Nic's Maths and Stats

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: 215812
The Doctoral Journey

The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.

Views: 41955
White Crane Education

Views: 68838
David Russell

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: 146256
YaleUniversity

Views: 14370
Introduction to Research - I

This video is meant to be used as an introductory lesson to Mini Research Writing focusing on Data Analysis and Discussion.
As this is a mini class project, some of the requirements have been made simple due to time constraints. Plus, the focus of this mini research paper is to get students familiarized to the ways of writing an academic paper and the items that needs to be included.
suitable for beginners!

Views: 16898
NurLiyana Isa

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Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family.
Highlights include:
• Data management and organization
• Data filtering and visualization
• Descriptive statistics
• Hypothesis testing and ANOVA
• Regression analysis

Views: 12888
MATLAB

Use simple data analysis techniques in SPSS to analyze survey questions.

Views: 792017
Claus Ebster

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: 1100538
mathtutordvd

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: 186266
Southampton Education School

In this video Dr. Ziene Mottiar, DIT, discusses issues around analyzing data and writing the analysing chapter. The difference between Findings and Analysis chapters is also discussed. This video is useful for anyone who is writing a dissertation or thesis.

Views: 64328
ZieneMottiar

Statistical Analysis of Data by Dr.Shahid,PhD - Research and Thesis
Subscribe this channel and follow all the video in the playlist.If you are currently doing any research or writing a thesis please follow step by step guidelines provided in these videos hope you will get a good
supervision of your quality research.
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Views: 251
Research and Thesis

This A Level / IB Psychology revision video for Research Methods looks at interpreting inferential statistics.

Views: 18658
tutor2u

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: 327739
Erich Goldstein

Through real-world examples, webinar participants learn strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.

Views: 7985
RhoInc1984

In this lecture, I provide a very basic introduction to quantitative data analysis and statistics. We begin by defining what "data" is, what a dataset looks like, and software tools for analyzing data.

Views: 3521
David Russell

A video tutorial from the National Union of Students, introducing the basic principles of quantitative data analysis and applying them to National Student Survey data.

Views: 13050
Kate Little

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: 667661
Kent Löfgren

Table of Contents:
00:25 - Purpose of the Data Analysis Section
01:14 - How to Write it
03:00 - Independent Samples t-test
04:49 - Dependent Samples t-test
05:31 - ANOVA
06:57 - Hypotheses (Predictions)

Views: 2692
Dharma Jairam

Copyright Broad Institute, 2013. All rights reserved.
The presentation above was filmed during the 2012 Proteomics Workshop, part of the BroadE Workshop series. The Proteomics Workshop provides a working knowledge of what proteomics is and how it can accelerate biologists' and clinicians' research. The focus of the workshop is on the most important technologies and experimental approaches used in modern mass spectrometry (MS)-based proteomics.

Views: 6826
Broad Institute

This session will provide information regarding descriptive statistics that are often used when reviewing assessment data. We will cover the statistics available in the Baseline reporting site and we will use example situations to identify which statistics should be used to answer the questions being asked. We will also provide an overview regarding levels of measurement that can help determine what types of statistics you are able to run on your data. - See more at: http://www2.campuslabs.com/support/training/basic-statistics-quantitative-analysis-i-5/#sthash.FDO5HA6i.dpuf

Views: 33989
Campus Labs

This video shows how to use SPSS to conduct a Correlation and Regression Analysis. A simple null hypothesis is tested as well. The regression equation is explained despite the result of the hypothesis conclusion.
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MORE VIDEOS:
Watch Using Excel to find the Correlation Coefficient r here: https://youtu.be/y3bgaLwdm50
Watch ANOVA in SPSS here: https://youtu.be/Bx9ry1vBbTM
Watch Sampling Distribution of Sample Means here: https://youtu.be/anGsd2l5YpM
Watch Using Excel Charts to calculate Regression Equation here: https://youtu.be/qZjTtnyaV70
Watch Using Excel to calculate Regression Equation here: https://youtu.be/LDC0p9iZY8g
Watch ANOVA in Microsoft Excel (One-Way) here: https://youtu.be/WhBkgWL3_3k
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Views: 226809
Agron Kaci

Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/e/types-of-statistical-studies?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/correlation-and-causality?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistical-studies/statistical-questions/v/reasonable-samples?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to KhanAcademy’s Probability and Statistics channel:
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Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 166464
Khan Academy

This video covers a few topics using the data analysis tool.
After this video you should be able to:
a) Find and use data analysis on excel to calculate statistics
b) Calculate the mean, median, mode, standard deviation, range and coefficient variation on a variable set of data in excel.
c) Conduct a confidence interval in excel.
d) Complete a T-test in excel to help complete a hypothesis test.
e) Conduct a linear regression analysis output from excel and create a scatter diagram.

Views: 90761
Me ee

Views: 39960
Jeongeun Kim

Bare Essentials of Research for Students
A great introduction to statistics before students embark on a research project!

Views: 94785
Ranga Lingham

This is Chapter 10 about how to analyze qualitative data

Views: 10606
Qingwen Dong

Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.

Views: 136460
VelactionVideos

This video will explore the statistical tools marketers have at their disposal to make sense of the numbers. These tools are what you will use after conducting a survey so that the information collected can be used to make appropriate decisions.

Views: 2805
Jason Richea

What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.

Views: 991
Microsoft Research

Let's go on a journey through univariate analysis and learn about descriptive statistics in research!

Views: 44430
ChrisFlipp

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: 494960
Phil Chan

What is DESCRIPTIVE STATISTICS? What does DESCRIPTIVE STATISTICS mean? DESCRIPTIVE STATISTICS meaning - DESCRIPTIVE STATISTICS definition - DESCRIPTIVE STATISTICS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Descriptive statistics are statistics that quantitatively describe or summarize features of a collection of information. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, are not developed on the basis of probability theory. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, the proportion of subjects with related comorbidities etc.
Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness.
Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.
For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. For example, a player who shoots 33% is making approximately one shot in every three. The percentage summarizes or describes multiple discrete events. Consider also the grade point average. This single number describes the general performance of a student across the range of their course experiences.
The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot.
In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behavior by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future.
Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable's distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf display.

Views: 10497
The Audiopedia

Learn about managing data in Excel. These are the Video supplements for Workbook of Quantitative Tools and Techniques in Marketing, 2nd Ed. Part of a full MOOC.

Views: 3695
Tim J Smith PhD

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: 785413
Dr Nic's Maths and Stats

This Lecture talks about the use of statistics in research method.

Views: 11766
Cec Ugc

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: 85930
NurseKillam

Subject: Social Work Education
Paper:Research Methods and Statistics
Module: Univariate Analysis & Bivariate Analysis
Content Writer: Dr. Graciella Tavares

Views: 26534
Vidya-mitra

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
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Views: 269803
Examrace

This statistical analysis overview explains descriptive and inferential statistics. Watch more at http://www.lynda.com/Excel-2007-tutorials/business-statistics/71213-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-71213-0101
This specific tutorial is just a single movie from chapter one of the Excel 2007: Business Statistics course presented by lynda.com author Curt Frye. The complete Excel 2007: Business Statistics course has a total duration of 4 hours and 19 minutes and covers formulas and functions for calculating averages and standard deviations, charts and graphs for summarizing data, and the Analysis ToolPak add-in for even greater insights into data
Excel 2007: Business Statistics table of contents:
Introduction
1. Introducing Statistics
2. Learning Useful Excel Techniques
3. Summarizing Data Using Tables and Graphics
4. Describing Data Using Numerical Methods
5. Using Probability Distributions
6. Sampling Values from a Population
7. Testing Hypotheses
8. Using Linear and Multiple Regression
Conclusion

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LinkedIn Learning

This is a short practical guide to Qualitative Data Analysis

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James Woodall

Learn a better way to prepare clinical trial data for reporting and analysis. --
Many companies have made great strides in standardizing and conforming subject data collected in clinical trials, in order to have a more efficient and repeatable way of delivering the data to reporting systems for statistical analysis and regulatory submissions.
Typically, preparing data is made up of the following three steps:
1. Specifying the conformed data
2. Programming the conformed specification
3. Validating the written programs work as expected
Please join BioPharm Systems' Michael Grossman, vice president of clinical data warehousing and analytics, for a complimentary one-hour webinar that will discuss and demonstrate one way to dramatically reduce the effort of specification and validation, as well as remove the programming step in its entirety. The ensuing programs are automatically generated in Oracle Life Sciences Data Hub (LSH) and as standalone SAS. The end result is a significant increase in the productivity of data preparation and conforming for internal use, along with runnable programs that can be sent to regulatory authorities and partners.
To view this webinar in its entirety, please visit: http://www.biopharm.com or https://cc.readytalk.com/r/sldl6locwp3e.
Twitter: http://www.twitter.com/BioPharmSystems
Facebook: http://www.facebook.com/BioPharmSystems
LinkedIn: http://www.linkedin.com/companies/biopharm-systems-inc
Google+: https://plus.google.com/104105608638786200757

Views: 27767
BioPharmSystems

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Dr. Ralph-Christian Ohr has been working in several innovation, division and product management functions for international, technology-based companies. His interest is aimed at organizational and personal capabilities for high innovation performance. He authors the Integrative Innovation Blog. The Biggest Mistakes in Managing a Portfolio. The Biggest Mistakes in Financial Planning Series. by Harvey Jacobson, CHFC, MBA, CLU. Investors who have remained consistent with their risk profiles through volatile markets have seen a substantial recovery in their portfolios since March 2009. Those who are truly behind are those who panicked and are now left with the decision of how to recover their losses. They can, but it is a much slower recovery. This article published originally April 13, 2010, Los Angeles Daily News. Managing an agile portfolio. When the right people on the right teams have the right context, they naturally do the right thing. Set the right context.