What is CROSS-SECTIONAL DATA? What does CROSS-SECTIONAL DATA mean? CROSS-SECTIONAL DATA meaning - CROSS-SECTIONAL DATA definition - CROSS-SECTIONAL DATA explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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Cross-sectional data, or a cross section of a study population, in statistics and econometrics is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the same point of time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects.
For example, if we want to measure current obesity levels in a population, we could draw a sample of 1,000 people randomly from that population (also known as a cross section of that population), measure their weight and height, and calculate what percentage of that sample is categorized as obese. This cross-sectional sample provides us with a snapshot of that population, at that one point in time. Note that we do not know based on one cross-sectional sample if obesity is increasing or decreasing; we can only describe the current proportion.
Cross-sectional data differs from time series data, in which the same small-scale or aggregate entity is observed at various points in time. Another type of data, panel data (or longitudinal data), combines both cross-sectional and time series data ideas and looks at how the subjects (firms, individuals, etc.) change over time. Panel data differs from pooled cross section data across time, because it deals with the observations on the same subjects in different times whereas the latter observes different subjects in different time periods. Panel analysis uses panel data to examine changes in variables over time and differences in variables between the subjects.
In a rolling cross-section, both the presence of an individual in the sample and the time at which the individual is included in the sample are determined randomly. For example, a political poll may decide to interview 1000 individuals. It first selects these individuals randomly from the entire population. It then assigns a random date to each individual. This is the random date that the individual will be interviewed, and thus included in the survey.
Cross-sectional data can be used in cross-sectional regression, which is regression analysis of cross-sectional data. For example, the consumption expenditures of various individuals in a fixed month could be regressed on their incomes, accumulated wealth levels, and their various demographic features to find out how differences in those features lead to differences in consumers behavior.

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The Audiopedia

Views: 37796
Stata Learner

Asset Pricing with Prof. John H. Cochrane
PART II. Module 2. Classic Linear Models
More course details: https://faculty.chicagobooth.edu/john.cochrane/teaching/asset_pricing.htm

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UChicago Online

This video provides an introduction to time series data by a comparison of this data with cross-sectional data. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 80202
Ben Lambert

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Elizabeth Lynch

This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.

Views: 1098
Udacity

http://www.stomponstep1.com/cohort-case-control-meta-analysis-cross-sectional-study-designs/
Based on the types of bias that are inherent in some study designs we can rank different study designs based on their validity. The types of research studies at the top of the list have the highest validity while those at the bottom have lower validity. In most cases if 2 studies on the same topic come to different conclusions, you assume the trial of the more valid type is correct. However, this is not always the case. Any study design can have bias. A very well designed and executed cohort study can yield more valid results than a clinical trial with clear deficiencies.
• Meta-analysis of multiple Randomized Trials (Highest Validity)
• Randomized Trial
• Prospective Cohort Studies
• Case Control Studies or Retrospective Cohort
• Case Series (Lowest Validity)
Meta-analysis is the process of taking results from multiple different studies and combining them to reach a single conclusion. Doing this is sort of like having one huge study with a very large sample size and therefore meta-analysis has higher power than individual studies.
Clinical trials are the gold standard of research for therapeutic and preventative interventions. The researchers have a high level of control over most factors. This allows for randomization and blinding which aren't possible in many other study types. Participant's groups are assigned by the researcher in clinical trials while in observational studies "natural conditions" (personal preference, genetics, social determinants, environment, lifestyle ...) assign the group. As we will see later, the incidence in different groups is compared using Relative Risk (RR).
Cohort Studies are studies where you first determine whether or not a person has had an exposure and then you monitor the occurrence of health outcomes overtime. It is the observational study design with the highest validity. Cohort is just a fancy name for a group, and this should help you remember this study design. You start with a group of people (some of whom happen to have an exposure and some who don't). Then you follow this group for a certain amount of time and monitor how often certain diseases or health outcomes arise. It is easier to conceptually understand cohort studies that are prospective. However, there are retrospective cohort studies also. In this scenario you identify a group of people in the past. You then first identify whether or not these people had the particular exposure at that point in time and determine whether or not they ended up getting the health outcomes later on. As we will see later, the incidence in different groups in a cohort study is compared using Relative Risk (RR).
Case-Control Studies are retrospective and observational. You first identify people who have the health outcome of interest. Then you carefully select a group of controls that are very similar to your diseased population except they don't have that particular disease. Then you try to determine whether or not the participants from each group had a particular exposure in the past. I remember this by thinking that in a case control study you start off knowing whether a person is diseased (a case) or not diseased (a control). There isn't a huge difference between retrospective cohort and case-control. You are basically doing the same steps but in a slightly different order. However, the two study designs are used in different settings. As we will see later, the incidence in different groups in a case-control study is compared using Odds Ratio (OR).
A Case-Series is a small collection of individual cases. It is an observational study with a very small sample size and no control group. Basically you are just reviewing the medical records for a few people with a particular exposure or disease. A study like this is good for very rare exposures or diseases. Obviously the small sample size and lack of a control group limits the validity of any conclusions that are made, but in certain situations this is the best evidence that is available.
Cross Sectional Studies are different from the others we have discussed. While the other studies measure the incidence of a particular health outcome over time, a cross-sectional study measures Prevalence. In this observational study the prevalence of the exposure and the health outcome are measured at the same time. You are basically trying to figure out how many people in the population have the disease and how many people have the exposure at one point in time. It is hard to determine an association between the exposure and disease just from this information, but you can still learn things from these studies. If the exposure and disease are both common in a particular population it may be worth investing more resources to do a different type of study to determine whether or not there is a causal relationship.

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Stomp On Step 1

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What is CROSS-SECTIONAL STUDY? What does CROSS-SECTIONAL STUDY mean? CROSS-SECTIONAL STUDY meaning - CROSS-SECTIONAL STUDY definition - CROSS-SECTIONAL STUDY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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In medical research and social science, a cross-sectional study (also known as a cross-sectional analysis, transversal study, prevalence study) is a type of observational study that analyzes data collected from a population, or a representative subset, at a specific point in time—that is, cross-sectional data.
In economics, cross-sectional studies typically involve the use of cross-sectional regression, in order to sort out the existence and magnitude of causal effects of one or more independent variables upon a dependent variable of interest at a given point in time. They differ from time series analysis, in which the behavior of one or more economic aggregates is traced through time.
In medical research, cross-sectional studies differ from case-control studies in that they aim to provide data on the entire population under study, whereas case-control studies typically include only individuals with a specific characteristic, with a sample, often a tiny minority, of the rest of the population. Cross-sectional studies are descriptive studies (neither longitudinal nor experimental). Unlike case-control studies, they can be used to describe, not only the odds ratio, but also absolute risks and relative risks from prevalences (sometimes called prevalence risk ratio, or PRR). They may be used to describe some feature of the population, such as prevalence of an illness, or they may support inferences of cause and effect. Longitudinal studies differ from both in making a series of observations more than once on members of the study population over a period of time.

Views: 19076
The Audiopedia

Cross-sectional data, or a cross section of a study population, in statistics and econometrics is a type data collected by observing many subjects (such as individuals, firms, countries, or regions) at the same point of time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects.
For example, if we want to measure current obesity levels in a population, we could draw a sample of 1,000 people randomly from that population (also known as a cross section of that population), measure their weight and height, and calculate what percentage of that sample is categorized as obese. This cross-sectional sample provides us with a snapshot of that population, at that one point in time. Note that we do not know based on one cross-sectional sample if obesity is increasing or decreasing; we can only describe the current proportion.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video

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Audiopedia

Dr. S. PSY100-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.

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Kelsey Horn

Discretionary accruals, earnings management, Jones model 1991, cross-sectional regression
تقدير ادارة الارباح باستخدام نموذج جونز 1991 cross-sectionally ، المستحقات
cross-sectionally التقديرية باستخدام نموذج جونز 1991

Views: 4804
Abobaker Mohammed

A brief introduction to the structure of the data that we will use this semester. Most of our examples will use either cross-sectional data or time-series data. If things go well then we may cover the chapter on panel data at the end of the semester.

Views: 40907
Matthew Rafferty

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.
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Khan Academy

This video is on Panel Data Analysis. Panel data has features of both Time series data and Cross section data. You can use panel data regression to analyse such data, We will use Fixed Effect Panel data regression and Random Effect panel data regression to analyse panel data. We will also compare with Pooled OLS , Between effect & first difference estimation
For Analytics study packs visit : https://analyticuniversity.com
Time Series Video : https://www.youtube.com/watch?v=Aw77aMLj9uM&t=2386s
Logistic Regression using SAS: https://www.youtube.com/watch?v=vkzXa0betZg&t=7s
Logistic Regression using R : https://www.youtube.com/watch?v=nubin7hq4-s&t=36s
Support us on Patreon : https://www.patreon.com/user?u=2969403

Views: 72853
Analytics University

This video introduces the concept of a pooled cross section model, explaining its difference to cross sectional and time series models.
Check out http://www.oxbridge-tutor.co.uk/undergraduate-econometrics-course for course materials, and information regarding updates on each of the courses. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 21105
Ben Lambert

what is the difference between cross section and panel data???
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Kokab Manzoor is Certified Trainer, Speaker & Career Counsellor. He has trained thousands of students & Professionals about Leadership & Management skills, Motivation, Personality Grooming, Career selection and about variety of other life skills. Has a sound understanding of needed traits for workplace success and a strong ability to train employees in improving those characteristics.
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Kokab Manzoor

Example of the steps involved in analytic cross-sectional study design is explained

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Zelalem Hailu

In this video tutorial you will learn Types of data and sources of data for empirical analysis. In types of data there are three types, which we discussed in this tutorial. The time series data, cross sectional data and pooled data are discussed one by one. Some of the sources for collecting the data are also discussed in this tutorial. For more details log on to http://economicsguider.com/.

Views: 9187
Economics Guider

-- Created using PowToon -- Free sign up at http://www.powtoon.com/youtube/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.

Views: 32912
Brian Owczarski

This is the third in a series of lectures covering hierarchical linear models, also known as multilevel models, mixed models, random effects models, and variance components models. The material in this video walks step-by-step through one cross-sectional and one longitudinal example. The emphasis is on interpreting both the fixed effects and the variance components returned by HLM software. Complement your learning by setting up a session with one of our statistical consultants. Just contact us at 734-544-8038, by email at [email protected], or visit our website, http://methodsconsultants.com.

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Methods Consultants of Ann Arbor

Statistics and Data Series presentation by Dr. Youngki Shin, Nov 21, 2012 at The University of Western Ontario: "Introduction to Panel Data Analysis." The presentation introduced basic techniques of analysis of panel data, which are typically collected over time for the same individuals. Regression models, with both fixed and random effects, were discussed and illustrated using Stata. A simple statistical test for choosing between the fixed effect estimation and the random effect estimation was also covered. Slides for this presentation are online at the RDC website.
The Statistics and Data Series is a partnership between the Centre for Population, Aging and Health and the Research Data Centre. This interdisciplinary series promotes the enhancement of skills in statistical techniques and use of quantitative data for empirical and interdisciplinary research. More information at http://rdc.uwo.ca

Views: 76514
Western University

Download file: https://people.highline.edu/mgirvin/ExcelIsFun.htm
1. Terminology for Data sets
2. Element = Primary Key
3. Variable = Field = Column Header
4. Observation = Record
5. PivotTable to Create List of Elements or "Unique List"
6. Alt Keyboard shortcut for PivotTable on New Sheet: Alt, N, V, T, Enter
7. Excel Table Feature For Dynamic Ranges
8. Cross Sectional Data = One Date, Many Categories
9. Time Series data= Many Times, One or More Categories
This is for the Highline Community College Busn 210 Statistical Analysis for Business and Economics taught by Michael Girvin.

Views: 24384
ExcelIsFun

Handling missing values using a simple procedure described in Hawthorne and Elliot 2005 (PMID: 15996139)

Views: 5847
Scott Parrott

Teaching material for MSc & PhD students. Course on Event History Analysis (Longitudinal or survival data analysis) for demographers, epidemiologists, sociologists, etc. This video explains the difference between cross-sectional data analysis and event history analysis (EHA).

Views: 510
Philippe Bocquier

The terminal cross-section analyzer is designed to detect the quality of crimping terminal, it includes the following modules: terminal fixture, cutting and grinding, corrosion cleaning, cross-section image acquisition, measurement and data analysis, generate data reports. It only takes about 5 minutes to complete the cross-section analysis of a terminal.

Views: 278
KINGSING

Fixed Effects and Random Effects Models in R
https://sites.google.com/site/econometricsacademy/econometrics-models/panel-data-models

Views: 80539
econometricsacademy

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: 261534
Let's Learn Public Health

An introduction to basic panel data econometrics. Also watch my video on "Fixed Effects vs Random Effects". As always, I am using R for data analysis, which is available for free at r-project.org
My Website: http://www.burkeyacademy.com/
Link to the data: http://www.burkeyacademy.com/my-forms/Panel%20Data.xlsx
Link to previous video: http://www.youtube.com/watch?v=ySTb5Nrhc8g
Support this project on Patreon! https://www.patreon.com/burkeyacademy
Or, a one-time donation on PayPal is appreciated! http://paypal.me/BurkeyAcademy
My Website: http://www.burkeyacademy.com/
Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/

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BurkeyAcademy

This mini-tutorial identifies the key features of a cross sectional study. It also identifies the type of data measure calculated; and discusses the strengths and weaknesses of this study design.

Views: 24366
PACE MySPH

A basic introduction to the analysis of complex survey data in Stata. Created using Stata 13; new features available in Stata 14. Copyright 2011-2017 StataCorp LLC. All rights reserved.

Views: 31615
StataCorp LLC

What is CROSS-SECTIONAL REGRESSION? What does CROSS-SECTIONAL REGRESSION mean? CROSS-SECTIONAL REGRESSION meaning - CROSS-SECTIONAL REGRESSION definition - CROSS-SECTIONAL REGRESSION explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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In statistics and econometrics, a cross-sectional regression is a type of regression in which the explained and explanatory variables are associated with one period or point in time. This type of cross-sectional analysis is in contrast to a time-series regression or longitudinal regression in which the variables are considered to be associated with a sequence of points in time.
For example, in economics a regression to explain and predict money demand (how much people choose to hold in the form of the most liquid assets) could be conducted with either cross-sectional or time series data. A cross-sectional regression would have as each data point an observation on a particular individual's money holdings, income, and perhaps other variables at a single point in time, and different data points would reflect different individuals at the same point in time. In contrast, a regression using time series would have as each data point an entire economy's money holdings, income, etc. at one point in time, and different data points would be drawn on the same economy but at different points in time.

Views: 2019
The Audiopedia

In longitudinal studies, subjects are followed for a period of time and outcomes along with other characteristics for each subject are measured at multiple pre-specified time points. Unlike data collected from cross-sectional studies where outcomes and other characteristics are measured at only one time point for each subject, data collected from longitudinal studies allow investigators to study changes in response over time. However, designing and analyzing longitudinal studies require careful considerations. Two major issues that arise in longitudinal studies are the correlation between measurements from the same subject and potential missing data due to loss of follow-up. In this talk, we will introduce common statistical analysis methods that account for the correlation structure of longitudinal data. We will also introduce missing data mechanism and approaches to address this important issue.

Views: 10927
BiostatisticsMCW

This is lecture 7 in my Econometrics course at Swansea University. Watch the lecture Live on The Economic Society Facebook page Every Monday 2:00 pm (UK time) between October 2nd and December 2017.
http://facebook.com/TheEconomicSociety/
In this lecture, I covered an introduction to panel data models:
Nature of Panel Data: (aka longitudinal data) have both time series and cross-sectional dimensions. They arise when we measure the same collection of people or objects over a period of time.
This lecture covered the following models:
- Pooled OLS regression
- Fixed Effect Model
- Least Squares Dummy Variable LSDV
- Within Group Estimator
- Between Estimator
- Random Effects Estimator
- Hausman Test

Views: 10175
Hanomics

Panel Data combined features of time series and cross section. Panel data regression is used to analyse data that has both cross section and time series features.
In this video, we have discussed Fixed effect and Random effect Panel data model apart from with in effect and between effect panel data models.
This also covers the definition of cross section, time series & panel data and how they are different.
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Time Series Theory : https://goo.gl/54vaDk
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Data Science Case Study : https://goo.gl/KzY5Iu
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Views: 5529
Analytics University

International Methods Colloquium talk, February 6th 2015.

Views: 762
Methods Colloquium

Cross-sectional studies
Introduction
In a cross-sectional study a sample is chosen and data on each individual is collected at one point in time. Note that this may not be exactly the same time point for each subject. For example, a survey of primary care consultations may be conducted over a week – each patient will fill in the survey once but different subjects will fi ll out their survey on different days depending on when they came to the surgery.
When to use a cross-sectional study
• Surveys of prevalence, such as a survey to ascertain the prevalence of asthma
• Surveys of attitudes or views, such as: studies of patient satisfaction, patient/professional knowledge; studies of behavior, such as alcohol use and sexual behavior
• When inter-relationships between variables are of interest, for
example a study to determine the characteristics of heavy drinkers, a cross-sectional study allows comparisons by sex, age, and so on
Cautions in interpreting cross-sectional study data
Temporal effects
Since the data on each individual are collected at one time point, care is needed in inferring temporal effects unless the exposure is constant, such as with a congenital or genetic factor (e.g. blood groups). For example, if a relationship is observed between a disease and blood group then we can safely assume that this is a true association since the blood group of the subjects would not be changed by the disease process. The same could
not be assumed if a cross-sectional study showed an association between a disease and blood pressure since the disease might have led to the rise in blood pressure rather than the other way around.
Repeated cross-sectional studies
Sometimes cross-sectional studies are repeated at different times and/or in different places to look at the variability in findings. For example, many cross-sectional studies have estimated the prevalence of asthma in schoolchildren.
Comparisons of prevalence in different places is straightforward
but comparisons of the prevalence at different times is less so because each cross-sectional survey is likely to have included a slightly different sample of children at the different time points, and so interpretation of changes must be made cautiously.
Cross-sectional studies that appear to be longitudinal
Cross-sectional studies can be misinterpreted as if they were longitudinal studies. For example, a cross-sectional study in a sample of fetuses where the gestational age of the fetuses spans a range, say 22–28 weeks. Some researchers have used data such as these to estimate growth trends.
This is dubious because each fetus is measured just once and so the trend is being estimated from different fetuses. Thus differences between fetuses are likely to contribute to some of the differences observed by gestational age.

Views: 2627
Denis Otundo

Includes application examples, scales of measurement (nominal, ordinal, interval & ratio), qualitative versus quantitative data, cross-sectional versus time-series data, experimental versus observational data, and descriptive statistics versus statistical inference.

Views: 31399
Bharatendra Rai

This video explains the difference between panel and pooled cross sectional data.
Check out http://www.oxbridge-tutor.co.uk/undergraduate-econometrics-course for course materials, and information regarding updates on each of the courses. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 32068
Ben Lambert

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What is LONGITUDINAL STUDY? What does LONGITUDINAL STUDY mean? LONGITUDINAL STUDY meaning - LONGITUDINAL STUDY definition - LONGITUDINAL STUDY explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
A longitudinal survey is a correlational research study that involves repeated observations of the same variables over long periods of time, often many decades. It is often a type of observational study, although they can also be structured as longitudinal randomized experiments. Longitudinal studies are often used in psychology, to study developmental trends across the life span, and in sociology, to study life events throughout lifetimes or generations. The reason for this is that unlike cross-sectional studies, in which different individuals with the same characteristics are compared, longitudinal studies track the same people and so the differences observed in those people are less likely to be the result of cultural differences across generations. Longitudinal studies thus make observing changes more accurate and are applied in various other fields.
In medicine, the design is used to uncover predictors of certain diseases. In advertising, the design is used to identify the changes that advertising has produced in the attitudes and behaviors of those within the target audience who have seen the advertising campaign.
When longitudinal studies are observational, in the sense that they observe the state of the world without manipulating it, it has been argued that they may have less power to detect causal relationships than experiments. However, because of the repeated observation at the individual level, they have more power than cross-sectional observational studies, by virtue of being able to exclude time-invariant unobserved individual differences and also of observing the temporal order of events. Some of the disadvantages of longitudinal study include the fact that they take a lot of time and are very expensive. Therefore, they are not very convenient.
Longitudinal studies allow social scientists to distinguish short from longterm phenomena, such as poverty. If the poverty rate is 10% at a point in time, this may mean that 10% of the population are always poor or that the whole population experiences poverty for 10% of the time. It is impossible to conclude which of these possibilities is the case by using one-off cross-sectional studies.
Types of longitudinal studies include panel studies and cohort studies. Cohort studies sample a cohort, defined as a group experiencing some event (typically birth) in a selected time period, performing a cross-section at intervals through time. Panel studies also use cross-sectional data and compare the same group of individuals at intervals through time, but the sample is not necessarily a cohort, as it can be a group of people that do not share a common event. Therefore, a cohort study can be considered a panel study, but a panel study is not always a cohort study.
A retrospective study is a longitudinal study that looks back in time. For instance, a researcher may look up the medical records of previous years to look for a trend.

Views: 12691
The Audiopedia

This is muhmmad saeed aas khan meo
please visit my you-tube chanel for more video and my blog for research tips and tricks
www.saeedmeo.blogspot.com

Views: 12106
Meo School Of Research

Hello researchers,
This video will help you making a panel dataset in R from cross-section and time-series data available.

Views: 11817
Sarveshwar Inani

Topic 7. Multilevel modeling of cross-sectional data.
Recorded presentation at Johns Hopkins University, March 17, 2009.
Link to handouts associated with this segment:
http://www.statmodel.com/download/Topic7Handout.pdf
NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.

Views: 1038
Mplus

Mayo Clinic CTSC 5310 Clinical Epidemiology II - a brief tutorial on how to estimate sample size for a cross-sectional study with dichotomous predictor and dichotomous outcome variables using Russ Lenth's online app

Views: 2982
2chocolategirls

Multiple Linear Regression Model in R; Fitting the model and interpreting the outcomes!
Practice Dataset: (https://bit.ly/2rOfgEJ); Linear Regression Concept and with R (https://bit.ly/2z8fXg1)
More Statistics and R Programming Tutorial (https://goo.gl/4vDQzT)
Learn how to fit and interpret output from a multiple linear regression model in R and produce summaries.
▶︎ You will learn to use "lm", "summary", "cor", "confint" functions.
▶︎ You will also learn to use "plot" function for producing residual and QQ plots in R.
▶︎ We recommend that you first watch our video on simple linear regression concept (https://youtu.be/vblX9JVpHE8) and in R (https://youtu.be/66z_MRwtFJM)
▶︎▶︎Download the dataset here: https://statslectures.com/r-scripts-datasets
▶︎▶︎Like to support us? You can Donate https://statslectures.com/support-us or Share our Videos and help us reach more people!
◼︎ Table of Content:
0:00:07 Multiple Linear Regression Model
0:00:32 How to fit a linear model in R? using the "lm" function
0:00:36 How to access the help menu in R for multiple linear regression
0:01:06 How to fit a linear regression model in R with two explanatory or X variables
0:01:19 How to produce and interpret the summary of linear regression model fit in R
0:03:16 How to calculate Pearson's correlation between the two variables in R
0:03:26 How to interpret the collinearity between two variables in R
0:03:49 How to create a confidence interval for the model coefficients in R? using the "confint" function
0:03:57 How to interpret the confidence interval for our model's coefficients in R
0:04:13 How to fit a linear model using all of the X variables in R
0:04:27 how to check the linear regression model assumptions in R? by examining plots of the residuals or errors using the "plot(model)" function
►► Watch More:
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
►R Tutorials for Data Science https://bit.ly/1A1Pixc
►Getting Started with R (Series 1): https://bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg
►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi
►Linear Regression in R (Series 5): https://bit.ly/1iytAtm
►ANOVA Concept and with R https://bit.ly/2zBwjgL
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►Statistics & R Tutorials: Step by Step https://bit.ly/2Qt075y
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
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These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 218771
MarinStatsLectures- R Programming & Statistics

Designing for Citizen Data Analysis: A Cross-Sectional Case Study of a Multi-Domain Citizen Science Platform
Ramine Tinati, Max Van Kleek, Elena Simperl, Markus Luczak-Rösch, Robert Simpson, Nigel Shadbolt
Abstract:
Designing an effective and sustainable citizen science (CS)project requires consideration of a great number of factors. This makes the overall process unpredictable, even when a sound, user-centred design approach is followed by an experienced team of UX designers. Moreover, when such systems are deployed, the complexity of the resulting interactions challenges any attempt to generalisation from retrospective analysis. In this paper, we present a case study of the largest single platform of citizen driven data analysis projects to date, the Zooniverse. By eliciting, through structured reflection, experiences of core members of its design team, our grounded analysis yielded four sets of themes, focusing on Task Specificity, Community Development, Task Design and Public Relations and Engagement, supported by two-to-four specific design claims each. For each, we propose a set of design claims (DCs), drawing comparisons to the literature on crowdsourcing and online communities to contextualise our findings.
ACM DL: http://dl.acm.org/citation.cfm?id=2702420
DOI: http://dx.doi.org/10.1145/2702123.2702420

Views: 423
Association for Computing Machinery (ACM)

Views: 44541
Sarveshwar Inani

This video shows how cross-tabulation can be used to examine the relationship between two categorical variables.
You can download the data set used in these videos here: http://goo.gl/GpXK4D
Information on the origins, license and permissions for this data set can be downloaded here: http://goo.gl/bhc07L

Views: 112660
patrickkwhite