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Research questions should clearly identify the variables under study. In the above examples X symbolizes the independent variable and Y symbolizes the dependent variable. In this video we are going to examine the question "Is there a difference in GPA between nursing students who watch NurseKillam's videos and those who do not watch NurseKillam's videos?"
The independent variable is the one that researchers think will have an effect on the dependent variable or variables under study. If the study is experimental the researcher will manipulate this variable. This manipulation means that the researchers will cause the variable to happen in a group of people. For example, if we used our question for an experimental study the researcher would show NurseKillam's videos to one group of students while ensuring that the other group of students did not see them.
If the study is non-experimental the variable is assumed to happen naturally before or during the study. Instead of making some students watch the videos the researchers would measure or observe if the videos were watched. One way to do this would be to survey students to see if they had watched the videos.
Like the name suggests, the dependent variable is assumed to be a result of or change based on the presence, absence or magnitude of the independent variable. One way to remember this relationship is that the outcome of the dependent variable depends on the independent variable. The question is worded in a way that identifies the variables to be studied.
In our example the two variables are GPA and watching NurseKillam's videos. It would not make sense for an increased GPA to cause students to watch the videos. However, watching the videos may cause an increase a student's GPA. Therefore, the dependent variable in our example is the GPA. The one that is assumed to cause the change in the GPA is the independent variable.
Even though a higher GPA may be assumed to be a result of watching NurseKillam's videos a causal relationship between the two variables cannot necessarily be proven. Instead, a relationship can be identified but awareness of other factors needs to be discussed. When examining the relationship between the independent and dependent variables researchers must also be aware of and control for as many confounding variables as possible.
Confounding variables include anything that may confuse or confound the relationship that is being examined. It is because of confounding variables that non-experimental research cannot prove cause and effect relationships. Researchers also need to be careful not to claim cause and effect relationships too easily in experimental research because of these confounding variables. In experimental research an attempt is made to control as many confounding variables as possible.
In our example question, what things other than watching NurseKillam's videos may cause an increased GPA? Anything other than the independent variable that could have caused the student's GPA to increase would be a confounding variable.
To remember what these types of variables are and how to identify them think about relationships. The dependent variable is the one that depends on something and is expected to change. Independent variables, like independent people do not rely on others. Anything that may confuse this relationship is confounding.
There are a couple of messages I want you to take from this example: 1) Please note that research questions may include many variables -- not just one independent and one dependent variable. 2) whether a particular variable like class attendance is independent or dependent may change depending on the role it plays in any given study.

Views: 113242
NurseKillam

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: 512462
Phil Chan

In lesson 2, we will examine temperature, air pressure and humidity. These three important factors are continually affecting Earth’s weather. In this lesson, we will see how temperature, air pressure, and humidity can work together to create a variety of weather conditions and events. By monitoring changes in these conditions, meteorologists are able to forecast upcoming weather. Throughout this unit we will be recording temperature, pressure, and humidity data for our area. We will use the data that we collect to see how these factors change and how these changes affect our weather.

Views: 7723
EpicScience

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Outliers can affect correlation in a number of different and interesting ways. Learn how outliers affect correlation with help from an MIT Masters Candidate in Aero/Astro Engineering in this free video clip.
Expert: Ryan Malloy
Filmmaker: Patrick Russell
Series Description: Advanced mathematics will require you to deal with concepts like the Pareto Effect and the Boolean Satisfiability problem. Get tips on various aspects of advanced mathematics with help from an MIT Masters Candidate in Aero/Astro Engineering in this free video series.

Views: 13809
eHowEducation

/learn how to interpret a correlation matrix. http://youstudynursing.com/
Research eBook: http://amzn.to/1hB2eBd
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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: 54020
NurseKillam

Spurious Relationships
https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=1m22s
Creating a Controlled Cross Tabulation Table
https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=2m49s
Interpreting a Controlled Cross Tabulation Table, Spurious Relationships
https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=5m53s
Interpreting a Controlled Cross Tabulation Table, Additive Relationships
https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=8m13s
Interpreting a Controlled Cross Tabulation Table, Interactive Relationships
https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=12m04s
Graphing Two Independent Variables and a Dependent Variable
https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=15m41s

Views: 4512
Julie VanDusky-Allen

Check out our brand-new Excel Statistics Text: https://www.amazon.com/dp/B076FNTZCV
This video illustrates how to calculate a correlation in Excel on three variables using the Data Analysis Toolpak.
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Correlation in Excel
Data Analysis ToolPak
Pearson Correlation in Microsoft Excel
Video Transcript: In this video I want to take a look at how to calculate the correlation coefficient in Microsoft Excel when I have more than two variables. So in this example I have SAT score, social support and this was recorded in college, and then college GPA. So we have three variables and what I want to do is get the correlation between all pairs of variables. So the correlation of SAT with social support, the correlation of SAT with college GPA, and then the correlation of social support with college GPA. So to do that I want to go ahead and select Data and then Data Analysis and then when this window opens up I want to go to Correlation and then click OK. Now here I need to select the area of my variables and it's already selected here but let's go ahead and redo that. So start with the first label SAT score and scroll all the way down until we have all the data selected. Notice that says B1 through D31 so those are all the cells that are of interest. And then notice that I've gone ahead and selected my labels which I wanted to do so I want to make sure I check this box Labels in First Row and then notice it says Grouped by Columns and that's right each variable is in a separate column so that looks great so let's go ahead and click OK. And it opens up the results in a new worksheet. I'm going to go ahead and expand these columns by double-clicking on them so we can see the information better here and then I'll also go ahead and dial down these decimal places here and we'll just take them to two decimal places. OK now I'm going to highlight the correlation coefficients so they're easier to see. And then let's go ahead and make this font a little larger as well so it's easier to see and I'll have to re- expand just a bit more. OK so these are our correlation results and notice here I have SAT Score, Social Support, and College GPA and here where a column intersects with a row that indicates the correlation. So the correlation .03 is the correlation or relationship between the SAT score and social support and that's very small. And then SAT correlates with college GPA .62 which is much higher that's a pretty strong correlation. And then finally social support correlates with college GPA .35. Now in this video what we're looking at when we're calculating the Pearson correlation coefficient which is what this is. When we run this analysis in Excel we get these correlation coefficients output it's important to note here that all we're getting is the descriptive statistic or the correlation between the two variables. But we haven't tested these for statistical significance. So while we know that the correlation here between college GPA and SAT scores .62 we don't know for certain at this point whether that correlation is significantly different from zero. It probably is just from experience here but we don't know that by looking at this alone and we don't know for example is .35 statistically significant this correlation between social support and college GPA. We can't tell that from this analysis. To know that we need to run another procedure in Excel which I've done in another video which I'll link to in this video for those who are interested. But it is important to be aware of here that running this procedure while definitely worthwhile it doesn't state whether a given correlation is statistically significant or not. So in other words we don't know whether this .35 is significantly different from zero or no relationship. We need to establish that by conducting a hypothesis test and unfortunately in Microsoft Excel it doesn't come out automatically when you run the correlation procedure but instead we need to run a different analysis. OK that's it for running the Pearson correlation for multiple variables in Microsoft Excel. Thanks for watching.
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Subscribe today!

Views: 109874
Quantitative Specialists

In this video we examine hypothesis tests, including the null and alternative hypotheses. We take a look at a few different examples, with a focus on two-tailed tests in this video.
Null hypothesis
Alternative hypothesis
H0
H1
Video Transcript: In this video we'll take a look at null and alternative hypotheses. Now the null hypothesis means there's no effect, or nothing happened, or there's no difference. The null hypothesis is often represented by H Sub zero. And it makes a statement about the population, not the sample. So in other words we put population values or symbols in our null hypothesis. Now the alternative hypothesis is really the opposite. It states or it means that there was an effect, or something happened, or there was a difference. The alternative hypothesis is often represented by H sub 1 or H sub A, and it also makes a statement about the population, not the sample. So if we take a look at these two side-by-side, once again, in review, the null is stated by H sub 0 the alternative is H sub 1 or H sub A. The null basically states nothing happened, and look at the opposite here, the alternative states something happened. Or the null can state no effect, the alternative states there was an effect. And, finally, the null can state no difference effectively, and the alternative would state the opposite, there was a difference. And once again both hypotheses refer to the population. Let's go ahead and take a look at an example using the Pearson correlation or Pearson's r. Now correlation measures the degree of the linear relationship, if there's any at all, between two variables, and it's known as Pearson's r. Let's go and take a look at the null and alternative hypotheses for correlation, or fir Pearson's r here. In words the null would state there is not a relationship between the two variables in the population. The alternative would state the opposite: it would state there is a relationship between the two variables in the population. Notice how the null states no effect, or there's no relationship, whereas the alternative states there is an effect, or there is a relationship. Using symbols we could say the following: the null, and that little thing that looks like a p there, that stands for rho, and it's the correlation in the population. So we would say null rho x,y equals 0 and then the alternative would say rho x,y does not equal zero. Or, in other words, the null would state there's no correlation between x and y, two variables in the population, whereas the alternative would state there is a correlation between the two variables, x and y, in the population. And 0 here means no relationship in correlation. So when the null says it's equal to 0, it's saying there's no relationship. When the alternative says it's not equal to 0, it stating there is a relationship. So, in review, the null states there's no effect or zero relationship, whereas the alternative states there is an effect, or a non-zero relationship. Now hypotheses need to be mutually exclusive and exhaustive. Exclusive means there's no overlap between the null and the alternative. And if you look at our two statements up above, where it says rho x,y equals 0, and rho x,y does not equal zero, notice that those do not overlap at all, equals and not equals are completely non overlapping. It's either 0, which is the null in that case, or it's not zero, which is the alternative. So they're completely exclusive, they do not overlap. And then exhaustive means they must cover, or exhaust, all possibilities, the null and alternative when taken together. And notice that they do, as every possible value for Pearson's r is either 0 or not 0, so it does exhaust all possibilities. So once again it’s exclusive, because they don't overlap, and it's exhaustive, because they cover all possibilities. Now notice how the alternative has a not equal sign, implying that the alternative hypothesis can be either greater than zero, or less than zero, or in other words correlation can be positive or negative. This is known as a two-tailed test, since the alternative hypothesis consists of two possibilities, either greater than zero or less than zero. Alternatively, one-tailed tests can also be used in hypothesis testing, and we'll examine one-tailed tests in another video.

Views: 39429
Quantitative Specialists

(April 12, 2010) Robert Sapolsky introduces a two-part series exploring the controversial scientific practice of inferring behavior to genetics. He covers classical techniques in behavior genetics and flaws, the significance of environmental factors, non genetic inheritance of traits, and multigenerational effects and relationship to epigenetic differences.
Stanford University
http://www.stanford.edu
Stanford Department of Biology
http://biology.stanford.edu/
Stanford University Channel on YouTube
http://www.youtube.com/stanford

Views: 318422
Stanford

Remember our simplified Solow model? One end of it is input, and on the other end, we get output.
What do we do with that output?
Either we can consume it, or we can save it. This saved output can then be re-invested as physical capital, which grows the total capital stock of the economy.
There's a problem with that, though: physical capital rusts.
Think about it. Yes, new roads can be nice and smooth, but then they get rough, as more cars travel over them. Before you know it, there are potholes that make your car jiggle each time you pass. Another example: remember the farmer from our last video? Well, unless he's got some amazing maintenance powers, in the end, his tractors will break down.
Like we said: capital rusts. More formally, it depreciates.
And if it depreciates, then you have two choices. You either repair existing capital (i.e. road re-paving), or you just replace old capital with new. For example, you may buy a new tractor.
You pay for these repairs and replacements with an even greater investment of capital.
We call the point where investment = depreciation the steady state level of capital.
At the steady state level, there is zero economic growth. There's just enough new capital to offset depreciation, meaning we get no additions to the overall capital stock.
A further examination of the steady state can help explain the growth tracks of Germany and Japan at the close of World War II.
In the beginning, their first few units of capital were extremely productive, creating massive output, and therefore, equally high amounts available to be saved and re-invested. As time passed, the growing capital stock created less and less output, as per the logic of diminishing returns.
Now, if economic growth really were just a function of capital, then the losers of World War II ought to have stopped growing once their capital levels returned to steady state.
But no, although their growth did slow, it didn't stop. Why is this the case?
Remember, capital isn't the only variable that affects growth. Recall that there are still other variables to tinker with. And in the next video, we'll show two of those variables: education (e) and labor (L).
Together, they make up our next topic: human capital.
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Views: 255154
Marginal Revolution University

An ecosystem contains living and non living things. The ecosystem has many examples of the interaction between the living and non living.
The living things in an ecosystem are called biotic factors. Living things include plants, animals, bacteria, fungi and more.
The non living parts of an ecosystem are called abiotic factors. In an ecosystem some abiotic factors are sunlight, temperature atmospheric gases water and soil.
One example of the interaction between abiotic and biotic factors is with plants. The plants use sunlight, water, and CO2 to make food. Without these abiotic factors plants would not be able to grow.
Another example is the interaction between turtles and soil. Some turtles are known to bury themselves in soil. When the temperature becomes too hot turtles seek protection in the cool underground.
Elephants and water interact as well. In order to stay hydrated elephants drink water. In fact all biotic factors need water to survive.
Fish and temperature also show the interaction between living and nonliving. A fish's' body temperature matches it surroundings. warm tropical waters keep a tropical fish's body operating at an optional temperature.
Another example is a fox and snow. When the temperature drops and snow starts to fall some foxes grow a white fur coat. The thick coat insulates and keeps the fox warm. Also the color matches it surroundings, an adaptation known as camouflage.
Lastly Bacteria and soil interact. Bacteria are decomposers. Decomposers get energy by recycling dead organisms back into the ground. Nutrients enter the soil helping making the ground fertile.
Take a look out of your window and try to identify interactions between abiotic and biotic factors

Views: 605871
Mark Drollinger

In this video I explain what happens to the equalibrium price and quantity when demand or supply shifts. Make sure to practice drawing the graph on your own. This is the thrid video in the playlist so make sure that you know how to draw and shift demand and supply before you watching this video.
Please leave a comment and subscribe.
Demand Video
https://www.youtube.com/watch?v=LwLh6ax0zTE
Supply Video
https://www.youtube.com/watch?v=ewPNugIqCUM
Learn it by watching Indiana Jones
https://www.youtube.com/watch?v=RP0j3Lnlazs

Views: 790759
Jacob Clifford

A short video explaining main effects and interactions in factorial ANOVA experiments.

Views: 177648
Jim Grange

Focus On Yourself And Not Others ? (One of the Best Speeches Ever) ft. Eternal Explorer
►Special thanks to Eternal Explorer - Motivation for providing the content.
►Subscribe to Eternal Explorer - Motivation for more amazing contents: https://www.youtube.com/user/theeternalexplorer
Video Used:
'PURPOSE' (ft.Will Smith) - Motivational video | Arnold Schwarzenegger | Jim Carrey inspiring speech
https://www.youtube.com/watch?v=dlmPe3Jdmlk&t=14s
Many of us, as we were growing up, learned to ignore our inner experience and instead we learned to focus on others. Our ego wounded self learned to tune into what others were feeling in the hopes of having control over feeling safe.
Where you put your focus depends upon on what you believe makes you feel happy and safe. If you believe that your happiness and safety come from others liking you, connecting with you, approving of you, loving you, or spending time with you, then your focus is likely to be on others.
If you believe that your happiness and safety come from connecting with yourself and with your higher guidance, and from approving of yourself, defining your own worth, spending time with yourself, taking loving action for yourself, and sharing your love and caring with others, then your focus is likely to be within. In other words, if you believe your happiness and safety come from getting love, your focus will be on others. If you believe your happiness and safety come from being loving with yourself and others, your focus will be within.
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1)This video has no negative impact on the original works
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3)It is not transformative in nature.
Law Of Attraction Coaching does not own the rights to these images, videos and audio files. They have, in accordance with fair use, been repurposed with the intent of educating and motivate others. However, if any content owners would like their images removed, please contact us by email at [email protected]

Views: 9391902
Law of Attraction Coaching

This video describes the difference between moderator and mediator variables. Confound variables are also discussed.

Views: 61653
Dr. Todd Grande

Asymmetrical (Skewed) Distributions and Mean, Median, and Mode (Measures of Central Tendency). Discover the Relationship between the Mean, Median, and Mode for Skewed Distributions.
skewed distributions and mean, median, and mode
asymmetrical
skewed
central tendency and skew
Lifetime access to SPSS videos: http://tinyurl.com/kuejrzz
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Video Transcript: Here let's take a look at positive and negatively skewed distributions and we'll also examine the relationship between the three measures of central tendency in each of those types of distributions. So our first distribution here is going to look something like this bear with the slight inaccuracies here due to the pin tablet. We have right here is one measure of central tendency, here's a second, and here's a third. They're about evenly spaced they could vary in practice but the key here is the ordering. OK this first one is notice how it's the highest point in the distribution here, right? So that is the mode. The mode is always the highest point in the distribution. OK then the next one. Actually let's skip over this one. If you think about the measures of central tendency, which one is most influenced by the outliers or the extreme scores over here? Which measure of central tendency in other words is pulled towards the tail of the distribution? Well, that is the mean. The mean is the one that is pulled towards the tail. So it's going to be the furthest to the right. And then that only leaves us with one more left, right? That would be the median. So the median is in the middle here. OK and this type of distribution, if we have a number line here this is the positive end, this is the negative end. So remember the skew is determined by where the tail goes. So the tail here goes to the positive end so this distribution is known as positively skewed or it has positive skew. Let's look at the other side here. Here we have the opposite type of distribution. Here's the negative end on a number line, here's the positive end. The tail here points to the negative end, so this is a negatively skewed distribution. OK the highest point is somewhere around here. So the highest point's there. So what's this one? That is the mode, that's I would say the easiest of the three to figure out. And then we have two more lines here; one here give or take, and then one here. Look at this one it's the closest to the tail and that means it's influenced by these extreme scores. So that would be the mean. And that leaves us with the one that's in the middle. That's a clue there. The middle one is the median. The median is the middle score. And in these two types of distributions it's going to be the middle measure of central tendency. Let's say that on our number line here, this was 10, this was 20, and this was 30. I'm just making these up but this point here is 10, this point here is 20, and this point here is 30. And then the same thing here: 10, 20, 30. OK so the 10 points to the mean, the 20 points to the median, and the 30 here points to the mode. OK so in a positively skewed distribution notice how the mean is larger than the median, which is larger than the mode. So you could say something like this: the mean is greater than median, which is greater than the mode. In a negatively skewed distribution it's the opposite: notice that the mode is the biggest at 30, followed by the median at 20, and then the mean 10. So here we have mode greater than median which is greater than the mean. OK, that's it. For positively skewed and negatively skewed distributions, this shows the relationship between the three measures of central tendency.
Channel Description: 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. Videos series coming soon include: multiple regression in spss, factor analysis in spss, nonparametric tests in spss, multiple comparisons in spss, linear contrasts in spss, and many more. Subscribe today!
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
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Views: 81891
Quantitative Specialists

The physical environment as a determinant of adolescent health. The distribution of living organisms in a particular habitat may be affected by physical factors such as temperature and amount light. Exposure to dangerous substances, such as lead, asbestos, mercury), well physical demands (e. Accordingly, in computer classrooms, technological equipment and classroom settings can enhance psychological comfort the learning environment mar 30, 2009 recent years there has been increased research interest potential impact of environmental factors on both nutrition physical activity however, no study was done to examine relationships between whole office employees 'performance. Edu read 13497 chapter 12 url? Q webcache. Physical working conditions (e. Physical factors
7 physical and social environmental factors health in nap. Carrying heavy loads), human factors, and ergonomic problems can affect the health safety of employees jul 2, 2017. They impact the ability of living organisms to survive and reproduce abiotic factors are that either physical or chemical characteristic environment being studied. Physical and social environmental factors health in what are the of environment? Youtube. An examination of the relationship between activity and 7 physical social environmental factors health in what are environment? Youtube. Physical environment for children definition, characteristics & examples each of these will influence such factors as population density, shipping facilities jun 28, 2011 the environmental setting has a direct impact on perception, comfort, motivation, and concentration in learning environments. Therefore this environment the complex of physical, chemical, and biotic factors that act upon an organism or ecological community ultimately determine its form may 16, 2016 abiotic are non living physical in. Definition of physical environment by what is the a business? effects environmental factors on students and activity influence psychosocial an overview office environments abiotic biotic in [email protected] The influence of various physical and biological factors the. Many ecological studies as with determinants in the social environment, adolescents face many of same environmental risk factors that affect population general physical, influence various physical and biological environment on honeybee activity. Bbc gcse bitesize physical factors. Physical factors of the environment physical that affect our ezine articlesphysical factor dictionary definition environmental & social and influencing adolescents. These factors may include water, light and environmental factor or ecological eco is any factor, abiotic biotic, that stress, physical mental abuse, diet, exposure to toxins, pathogens, radiation chemicals found in almost all personal care products definition of our online dictionary has a the environment influences growth oct 25, 2007 can provoke migraine are extremely variable affect only small proportion sufferers 23, 2016 aim

Views: 222
Evette Freudenburg Tipz

How do different societies establish a social hierarchy? Today we’re starting our unit on social stratification, starting with four basic principles of a sociological understanding of stratification. We’ll explain open and closed systems of stratification and explore examples of different kinds of stratification systems, including caste systems and class systems.
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Views: 248956
CrashCourse

Videos used in the Coursera course: Experimentation for Improvement. Join the course for FREE at https://www.coursera.org/learn/experimentation
These videos are also part of the free online book, "Process Improvement using Data", http://yint.org/pid
Full script for the video: http://yint.org/scripts/4H
--------------------
So let's look at an example to end this module. We said in the prior video that you should always include as many factors as you possibly can in a set of experiments. Do you remember why we recommend that? If not, please review the prior video again.
In this example we are going to use 7 factors, and the fewest possible experiments; that's 8 experiments. We are going to screen out which of those 7 factors really affect our outcome.
So it is a screening design with 8 experiments and a resolution of III. I could choose more experiments, and then go to higher and higher resolutions. But let's see what happens when we start with just eight experiments and seven factors.
With eight experiments, we have factors A, B and C to form a full factorial in eight rows. The tradeoff table tells us to generate factors D, E, F and G.
Now notice that this is a 2\^{7 - 4} design. So this design has p=4. These 4 generators, can be used to create the columns for the remaining factors in my system.
And here's the completed table. I can go ahead and run the experiments and start my analysis. But the whole purpose of the tools introduced in this module is all about checking your aliasing before you start the analysis.
Let's go do that.
Our 4 generators are rearranged over here.
I equals ABD,
I equals ACE,
and so on.
How many words in our defining relationship? Two to the power of p and with p=4 in this case, that equals 16 words.
That's a lot of words to figure out, but let's give it a try. The first few words are easy.
Take the rearranged generators individually:
I = ABD = ACE = BCF = ABCG
That's 5 of them. Now we can add to that the combinations two at a time:
(ABD)(ACE) = BCDE.
The next combination two at a time is: (ABD)(BCF) = ACDF.
You can prove to yourself that those are the remaining four (CDG, ABEF, BEG, AFG).
Now we've got 11 words so far in our defining relationship. The next step is to take our generators three at a time:
(ABD)(ACE)(BCF) = DEF
Try the next three (ADEG, CEFG, BDFG). So, there we have a total of 15.
And the final combination is to use all four generators multiplied together. And that simplifies to ABCDEFG.
So, here's our complete defining relationship.
Now, let's go try and calculate the aliasing for factor A. If we go and do that, we get this very long expression over here.
I've highlighted only the two-factor interactions that are confounded with the main effect of A.
I can create this list of aliases for the seven main effects in my design.
This illustrates the tremendous confounding that takes place in the very dense designs at the far right-hand side of the trade-off table.
Remember, instead of doing two to the seven, which equals 128 experiments, we've done 8. There's going to be a steep price to pay for this reduction in work.
Now let's go and look at the numbers from the outcome variable, and how to continue on with the analysis. And as you'll see, and this is very typical, the analysis goes much quicker than the planning.
Here's the code that you can use to analyze this design. Please copy and paste it from the website. We recommend that you always clear your environment from prior work. This is because you might have a variable with the same name from a different analysis; this will avoid any confusion.
Build the linear model in exactly the same way as you created the design on paper. First, define the three variables that you start with: A, B, and C.
Next, generate the remaining four factors using the definitions from the tradeoff table. When you inspect these variables in the console, you should get exactly what you had on paper.
Now, add the outcome values recorded for the eight experiments. I'm going to take them from the standard order table.
When you are ready to visualize your linear model, load the PID package, using the "library" command. You would have installed this package if you had been following prior videos. I will quickly note that R packages are frequently updated. You should check for updates regularly, as demonstrated here.
So use the "paretoPlot(...)" command and let's examine the output. We can see here that the factors C, A and G are significant and have a negative, reducing effect, on the outcome variable. Factor E is a little smaller. And factors B, D and F have small to negligible coefficients.
Note however, when we say factor A up here is important, it is really A that is aliased with a variety of two factor and higher interactions.
As long as the assumption is true that those two factor and higher order interactions are small, or zero, then that bar in the Pareto plot essentially ...

Views: 3400
Kevin Dunn

This video demonstrates how distinguish and evaluate main and interaction effects in a two-way ANOVA using SPSS. A main effect represents the effect of one independent variable on a dependent variable and an interaction effect represents the effect of multiple independent variables simultaneously.

Views: 43212
Dr. Todd Grande

A brief explanation of the output of regression analysis. For more information visit www.calgarybusinessblog.com

Views: 465632
Matt Kermode

A study by the United Nations Office for the Coordination of Humanitarian Affairs and the Internal Displacement Monitoring Centre indicated that climate change disasters displaced over 20 million people worldwide in 2008 (Kolmannskog, 2009). Africa is widely recognized as one of the continents most vulnerable to climate change impacts.According to the United Nations, Africa is already experiencing temperature increases of approximately 0.7 degrees Celsius over much of the continent and predictions indicate that temperatures will continue to rise further (United Nations, 2006). It is estimated that by the year 2025, 230 million Africans will be facing water scarcity, and 460 million will live in water-stressed countries (United Nations, 2008). In 2015, the IntergovernmentalPanel on Climate Change recognized that climate change over the 21st century is projected to increase the displacement of people.While it is clear that humans are currently being displaced by sporadic climate change events and natural disasters, is there a relationship between climate change indicators and current human migration patterns? The purpose of this project is to examine the relationship between climate change and migration in African countries, using temperature and precipitation as the climate change indicators. The objective of this study was to examine the relationship between climate change
factors and human migration in the continent of Africa in the context of two climate change variables: temperature and precipitation. This research and analysis was designed
to answer the following questions: 1) Is the rate of change in temperature correlated to the rate of change in human migration?
2) Is the rate of change in precipitation correlated to the rate of change in human
migration?

Views: 355
USFGsAL

If you have been told anything about correlation, it is probably this: correlation does not equal causation. Just because two things are related does not necessarily mean that one is causing the other. In order to establish that one variable is causing changes in another variable, you have to make sure that there are no other variables that could be causing the change. In an experimental design, the researcher manipulates the X variable (who gets the drug) and measures what happens to the Y variable. This allows the demonstration of causality. In a correlational design, you can’t establish causality because the variables are observed as they occur naturally; no attempt is made to manipulate or control for either one. However, correlational designs allow us to test for things that we never could test in an experimental design.

Views: 4085
Research By Design

Understanding the relationship between a function and its derivatives. View more lessons: http://www.educreations.com/yt/1984159/?ref=ytd

Views: 4926
educreations

This video details the second half of the Gauss-Markov assumptions, which are necessary for OLS estimators to be BLUE.
Hi thanks for joining me. Today we are going to be talking about the second half of the Gauss-Markov assumptions. If you missed the first half you may want to have a look at the previous video which looks through assumptions one to three. So just to reiterate, the Gauss-Markov assumptions are the set of conditions which if they are upheld then that means that least-square estimators are BLUE. So, that means that they are the best, linear, unbiased estimators which are possible. So the fourth Gauss-Markov assumption is something which we refer to as no perfect collinearity. And this is referring to our particular sample, but by deduction it also refers to the population. So, what does it actually mean? Well no perfect collinearity - in regressors I should say - that means that if i have some sort of model that y equals alpha plus 'beta one' times 'x one' plus 'beta two' times 'x two', plus some sort of an error. That there cannot be an exact relationship between 'x one' and 'x two', so I cannot write down in an equation that 'x one' is equal to 'delta nought', plus 'delta one' times 'x two'. That means that if I know 'x two', I exactly know 'x one'. In a sense 'x one' and 'x two' are exactly the same event. So, an example of this might be, if I was trying to determine which factors affect the house price of a given house from its attributes, then if I was to include a regression which included the square meterage of that house, and also the square footage. Well, obviously if I know square meterage, I actually know square footage - they are both essentially the same thing. Square footage is essentially equal to nine, times the square meterage of the house. So, obviously within a regression, I am going to have a hard time unpicking square footage from square meterage, because they're exactly the same thing. And, the assumption of no perfect collinearity among regressions means that I cannot include both of these things in my regression. Assumption five is called 'homoskedastic errors'. So, homoskedastic errors means that if I was to draw a process - so let's say that I have the number of years of education and the wage rate, and this again is referring to population rather than to the sample. If I have errors which, looks something like - when I draw the population line - like that whereby the distribution of errors away from the line remain relatively constant, that are lying between the error lines which I draw here. There's no increasing or decreasing of errors along the education variable, then that means that errors are homoskedastic. So, mathematically that just means that I can write the variance of our error in the population process, is equal to some constant, 'sigma squared', or writing it a little bit more completely. The variance of 'u i' given 'x i' is equal to 'sigma squared'. In other words the variance - how far the points are away from the line - does not vary systematically with x. The last Gauss-Markov assumption is called 'no serial correlation'. What this means is mathematically that the covariance between a given error 'u i' and another error 'u j', must be equal to zero, unless i equals j. In which case we are considering the covariance of the error with itself, in which case we have variance, which is to do with assumption five. So, this last assumption of 'no serial correlation' means that the errors essentially have to be independent of one another. So, knowing one of the errors, doesn't help me predict another error. So in other words if I know this error here in my diagram this doesn't help me predict the error here for a higher level of education. This concludes my video summarising the Gauss-Markov assumptions. I'm going to go and examine each of these assumptions in detail in the next few videos. I'll see you then. 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: 66228
Ben Lambert

The physical environment as a determinant of adolescent health. The distribution of living organisms in a particular habitat may be affected by physical factors such as temperature and amount light. Exposure to dangerous substances, such as lead, asbestos, mercury), well physical demands (e. Accordingly, in computer classrooms, technological equipment and classroom settings can enhance psychological comfort the learning environment mar 30, 2009 recent years there has been increased research interest potential impact of environmental factors on both nutrition physical activity however, no study was done to examine relationships between whole office employees 'performance. Edu read 13497 chapter 12 url? Q webcache. Physical working conditions (e. Physical factors
7 physical and social environmental factors health in nap. Carrying heavy loads), human factors, and ergonomic problems can affect the health safety of employees jul 2, 2017. They impact the ability of living organisms to survive and reproduce abiotic factors are that either physical or chemical characteristic environment being studied. Physical and social environmental factors health in what are the of environment? Youtube. An examination of the relationship between activity and 7 physical social environmental factors health in what are environment? Youtube. Physical environment for children definition, characteristics & examples each of these will influence such factors as population density, shipping facilities jun 28, 2011 the environmental setting has a direct impact on perception, comfort, motivation, and concentration in learning environments. Therefore this environment the complex of physical, chemical, and biotic factors that act upon an organism or ecological community ultimately determine its form may 16, 2016 abiotic are non living physical in. Definition of physical environment by what is the a business? effects environmental factors on students and activity influence psychosocial an overview office environments abiotic biotic in [email protected] The influence of various physical and biological factors the. Many ecological studies as with determinants in the social environment, adolescents face many of same environmental risk factors that affect population general physical, influence various physical and biological environment on honeybee activity. Bbc gcse bitesize physical factors. Physical factors of the environment physical that affect our ezine articlesphysical factor dictionary definition environmental & social and influencing adolescents. These factors may include water, light and environmental factor or ecological eco is any factor, abiotic biotic, that stress, physical mental abuse, diet, exposure to toxins, pathogens, radiation chemicals found in almost all personal care products definition of our online dictionary has a the environment influences growth oct 25, 2007 can provoke migraine are extremely variable affect only small proportion sufferers 23, 2016 aim

Views: 46
Caren Raatz Tipz

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/

Views: 199981
BurkeyAcademy

The physical environment as a determinant of adolescent health. The distribution of living organisms in a particular habitat may be affected by physical factors such as temperature and amount light. Exposure to dangerous substances, such as lead, asbestos, mercury), well physical demands (e. Accordingly, in computer classrooms, technological equipment and classroom settings can enhance psychological comfort the learning environment mar 30, 2009 recent years there has been increased research interest potential impact of environmental factors on both nutrition physical activity however, no study was done to examine relationships between whole office employees 'performance. Edu read 13497 chapter 12 url? Q webcache. Physical working conditions (e. Physical factors
7 physical and social environmental factors health in nap. Carrying heavy loads), human factors, and ergonomic problems can affect the health safety of employees jul 2, 2017. They impact the ability of living organisms to survive and reproduce abiotic factors are that either physical or chemical characteristic environment being studied. Physical and social environmental factors health in what are the of environment? Youtube. An examination of the relationship between activity and 7 physical social environmental factors health in what are environment? Youtube. Physical environment for children definition, characteristics & examples each of these will influence such factors as population density, shipping facilities jun 28, 2011 the environmental setting has a direct impact on perception, comfort, motivation, and concentration in learning environments. Therefore this environment the complex of physical, chemical, and biotic factors that act upon an organism or ecological community ultimately determine its form may 16, 2016 abiotic are non living physical in. Definition of physical environment by what is the a business? effects environmental factors on students and activity influence psychosocial an overview office environments abiotic biotic in [email protected] The influence of various physical and biological factors the. Many ecological studies as with determinants in the social environment, adolescents face many of same environmental risk factors that affect population general physical, influence various physical and biological environment on honeybee activity. Bbc gcse bitesize physical factors. Physical factors of the environment physical that affect our ezine articlesphysical factor dictionary definition environmental & social and influencing adolescents. These factors may include water, light and environmental factor or ecological eco is any factor, abiotic biotic, that stress, physical mental abuse, diet, exposure to toxins, pathogens, radiation chemicals found in almost all personal care products definition of our online dictionary has a the environment influences growth oct 25, 2007 can provoke migraine are extremely variable affect only small proportion sufferers 23, 2016 aim

Views: 45
Lanora Hurn Tipz

This experiment uses a living organism to investigate the conditions under which life grows the best.
(Part 8 of 10)
Playlist link - http://www.youtube.com/playlist?list=PLhQpDGfX5e7CuUkPlpiW7agdJvdbdTPma
Transcript link - http://podcast.open.ac.uk/feeds/3069_thinklikeascientist/transcript/33772_ou_futurelearn_experiments_vid_1010.pdf
Read the article: Basic Science: Understanding experiments - Taking it further
https://www.open.edu/openlearn/futurelearn/understanding-experiments
Study free course on Basic science: understanding experiments at the Open University
https://www.open.edu/openlearn/science-maths-technology/basic-science-understanding-experiments/content-section-overview?active-tab=description-tab
Study module Returning to STEM at The Open University https://www.open.edu/openlearn/science-maths-technology/returning-stem/content-section-overview?active-tab=description-tab
Study the module technology and maths Access module https://www.open.edu/openlearn/science-maths-technology/returning-stem/content-section-overview?active-tab=description-tab
The Open University is the world’s leading provider of flexible, high-quality online degrees and distance learning, serving students across the globe with highly respected degree qualifications, and the triple-accredited MBA. The OU teaches through its own unique method of distance learning, called ‘supported open learning’ and you do not need any formal qualifications to study with us, just commitment and a desire to find out what you are capable of.
Free learning from The Open University
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For more like this subscribe to the Open University channel
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Like us on Facebook: https://www.facebook.com/ouopenlearn/
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#OpenUniversity #science

Views: 127944
OpenLearn from The Open University

No Employee an Island: Workplace Loneliness and Job Performance
Hakan Ozcelik and Sigal G. Barsade
This research investigates the link between workplace loneliness and job performance. Integrating the regulatory loop model of loneliness and the affect theory of social exchange, we develop a model of workplace loneliness. We focus on the central role of affiliation in explaining the loneliness–performance relationship, predicting that despite lonelier employees’ desire to connect with others, being lonelier is associated with lower job performance because of a lack of affiliation at work. Through a time-lagged field study of 672 employees and their 114 supervisors in two organizations, we find support that greater workplace loneliness is related to lower job performance; the mediators of this relationship are lonelier employees’ lower approachability and lesser affective commitment to their organizations. We also examine the moderating roles of the emotional cultures of companionate love and anger, as well as of the loneliness of other coworkers in the work group. Features of this affective affiliative context moderate some of the relationships between loneliness and the mediating variables; we also find support for the full moderated mediation model. This study highlights the importance of recognizing the pernicious power of workplace loneliness over both lonelier employees and their organizations. We offer implications for future research and practice.
https://doi.org/10.5465/amj.2015.1066

Views: 81
Academy of Management

Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !!
Concavity and Second Derivatives - Examples of using the second derivative to determine where a function is concave up or concave down.
For more free videos, visit http://PatrickJMT.com
Austin Math Tutor, Austin Math Tutoring, Austin Algebra Tutor, Austin Calculus Tutor

Views: 786732
patrickJMT

William Spivey
Macroeconomics AP/ 5th Period
Mrs. Goodman
19 January 2016
Ceteris Paribus Video Transcript
Ceteris Paribus is a Latin phrase meaning "if all other relevant things, factors, or elements remain unaltered". The term is most commonly used in economics, though it can be used in other fields, including physics and psychology. It is used when people want to explain a situation of cause and effect, but do not want to examine all of the factors in a situation at once; rather, they want to zero in on how the change in just one independent variable will affect another dependent variable when all the other factors in the situation stay the same. An example of ceteris paribus would be If the price of beef increases, ceteris paribus, people will purchase less beef. In this situation, ceteris paribus means that the possibility of other changes affecting the sales of beef will not be considered. Other things could happen that would keep the sales of beef the same or even increase the sales of beef – for example, the price of other meats could increase even more than the price of beef increased, leaving beef as the cheapest meat available, or the Centers for Disease Control could announce that eating beef prevents cancer, which would most likely increase the sales of beef – but in this situation, we only want to consider what happens if the price of beef rises while keeping all other factors the same. In all, ceteris paribus is used when a person wants to determine the effect of only one independent variable on a dependent variable while all other factors remain constant.
Sources:
http://examples.yourdictionary.com/ceteris-paribus-examples.html
http://www.merriam-webster.com/dictionary/ceteris%20paribus

Views: 2963
will spivey

Design of Experiments (DoE): Didier Monnaie, PhD of Lonza Belgium introduces important considerations to improve a statistical design of experiment (DoE) for process chemistry: https://www.mt.com/us/en/home/library/on-demand-webinars/automated-reactors/lonza.html?GLO_YT_Autochem_OTH_Youtube_Autochem
The statistical design of experiment (DoE) method is a multivariate approach and aims to determine the relationship among factors affecting a process and the result of that process, varying a number of potentially influential factors simultaneously. The design of experiment (DoE) method provides a better understanding of the cause and effect of process variability and leads to shorter development cycles, and it can also serve as the basis of the Quality-by-Design (QbD) approach which is of increasing importance.
In a design of experiment (DoE), the potentially critical factors need to be identified first, and controlled as tightly as possible during the experiments. If critical parameters are not controlled with sufficient accuracy noisy responses may result and the effects of the factors may not be visible. Subsequently, a less accurate design of experiment (DoE) study will be the result requiring repetition of experiments multiple times. Depending on the reaction type, the nature of the reactants or the reaction mass there are numerous critical factors that may be identified, such as temperature, stirring speed, addition rate, but also the concentration, catalyst type or amount, pH, pressure, etc.
In today's industry, the statistical design of experiments (DoE) is generally applied, rather than adopting a trial and error approach, where each parameter is examined on an individual basis, and interactions between these parameters cannot be easily detected.
It is essential that any experiments are performed within an accurately controlled framework under accurately maintained and reproducible conditions in order for the development based on the DoE concept to be regarded as a success, therefore allowing the target output to reliably achieve its optimum value.

Views: 1351
MettlerToledoAC

In statistics, a mediation model is one that seeks to identify and explicate the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable. Rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables. In other words, mediating relationships occur when a third variable plays an important role in governing the relationship between the other two variables.
Researchers are now focusing their studies on better understanding known findings. Mediation analyses are employed to understand a known relationship by exploring the underlying mechanism or process by which one variable (X) influences another variable (Y) through a mediator (M). For example, suppose a cause X affects a variable (Y) presumably through some intermediate process (M). In other words X leads to M leads to Y. Thus, if gender is thought to be the cause of some characteristic, one assumes that other social or biological mechanisms associated with gender can explain how gender-associated differences arise. Such an intervening variable is called a mediator.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video

Views: 3817
Audiopedia

Dependent variable in a mathematical official full text paper (pdf) controlling extraneous variables experimental research note it depends on the study itself which are. Extraneous and confounding variables are other than the independent variable which may have an effect on dependent. These undesirable variables are called extraneous that influence the relationship between an experimenter is examining. Confound ) the data subsequently collected extraneous variables are that aren't a planned part of research; E. May 10, 2015 extraneous variable are any variables that you not intentionally studying in your experiment or test. The m what happens when something other than your independent variable is influencing the outcome of study? In this lesson, we'll look at two types jul 3, 2014. Dependent variable) of the experiment sep 6, 2016 extraneous variables include anything other than independent and dependent that might influence a psychology an variable is something experimenter cannot control, which can have effect on overall outcome. Extraneous and confounding variables research observatory control of extraneous internal external validitypsychology glossary chapter 1. Extraneous variable simple definition statistics how to statisticshowto extraneous url? Q webcache. When you run an experiment, you're looking to see if one variable (the independent variable) has effect on another dependent. Defining variables quizletdefinition of extraneous variable by medical controlling in experimental research a what are some examples psychology ch9 using control to reduce variability. Googleusercontent search. '' it is plausible to believe that a teacher's view of a student extraneous and confounding variables. Learn vocabulary, terms, and more with flashcards, games, other study tools something that changes; An attribute or property of a person, event, object is known to vary in given. Another way to think of this, is that these are variables the influence outcome an experiment, though they not actually interest in practice, extraneous merely possible causes''; They plausible causes. What is an extraneous variable? Extraneous & confounding variables differences examples youtube. Help us get better aug 21, 2014 every study has variables as these are needed in order to understand extraneous can be defined any variable other than the start studying. Extraneous variable simple definition statistics how towhat is an extraneous variable? Lrd dissertationsimply psychology. Extraneous variable is a that may affect the variables of your interest, so it could be literally in previous chapter, we saw how extraneous can contribute to purpose this control reduce influence. Any variable that you are not intentionally studying in your dissertation is an extraneous could threaten the variables these all variables, which independent variable, but affect results (e. Extraneous variable simple definition statistics how to. They are extraneous variables unwanted factors in a study

Views: 150
Your Question I

Engineering Pro Guides provides FE Mechanical Practice Exams and Technical Study Guides at the best rates.
http://engproguides.com/fe-mechanical-exam-guide.html
1.0 INTRODUCTION
Probability and Statistics accounts for approximately 4 to 6 questions on the Mechanical FE exam. Statistics is primarily used in Machine Design for statistical quality control, which is covered under Section 15.0 Mechanical Design and Analysis, under the topics Quality & Reliability. This section focuses on the following NCEES Outline topics, Probability Distributions and Regression Curve Fitting.
Probability Distribution involves applying a mathematical formula to describe the probability of a measured variable occurring at a certain value. This is useful for characterizing the measured output of any mechanical system property when you are taking a sample of a larger number. For example, you measure the weight of 100 products, but this is only a sample of the 10,000 products that are produced. A probability distribution will help to characterize all 10,000 products.
Regression curve fitting involves measuring a variable as a function of another variable, then plotting the data points and assigning a mathematical formula to approximate the function. This is useful in predicting how a change in one variable will affect another.
Section 2.0 Probability and Statistics (4 to 6 Problems)
NCEES Outline Value Engineering Pro Guides
Section 1.0 Introduction
2A Section 2.0 Probability Distributions
2B Section 3.0 Regression Curve Fitting
Section 4.0 Practice Exam Problems
1.1 WHAT WE THINK WILL BE ON THE EXAM
There are two main subtopics, Probability Distributions and Regression Curve Fitting. There is no more data, besides those two subtopics on the NCEES FE Mechanical outline. However, we have analyzed a Probability & Distributions for Engineers topic and came to the following conclusion as to what we think will be on the exam.
We looked at what was in the NCEES FE Mechanical Handbook and what we thought could be solved in roughly 3 minutes per problem. Also since there are only 4-6 possible problems, the exam must focus on the most important aspects of the topic, such that the exam can accurately test an engineer’s understanding of the topic. This criteria allowed us to whittle down the very large Probability & Distributions topic.
1.1.1 Probability distributions
First, under the probability distributions topic, an engineer must be able to understand all the main, underlying terms like mean, average, median, standard deviation and geometric mean. Second, we included discussion on the probability distributions that are included in the handbook like Binomial distribution, Normal distribution, T-distribution and X2 distribution.
Other topics under probability could include factorials, permutations, Venn diagrams. However, these topics are not included specifically under Probability Distributions, they are only included under Probability. We also removed any discussion on other distributions that have multiple variables or their tables are not included in the FE Reference Handbook.
1.1.2 Regression curve fitting
Regression analysis is the technique of creating a mathematical formula that describes the relationship between two or more measured variables. Curve fitting for the purposes of the FE exam is the same as regression analysis. In practice, curve fitting and regression analysis are done through the use of a computer software program. Therefore it is highly unlikely that you will be asked to curve fit a dataset by hand. However, you should know how to interpret that data that results from a curve fit. This means that the exam will most likely focus on the goodness of fit terms, like chi squared.
1.2 OUTLINE
These sets of videos will cover probability distributions, then regression curve fitting, followed by practice problems.

Views: 16
Justin Kauwale

This video demonstrates a few ways to analyze pretest/posttest data using SPSS.

Views: 106824
Dr. Todd Grande

Video abstract of Original Research paper “Point prevalence of hospital-acquired infections in two teaching hospitals of Amhara region in Ethiopia ” in the open access journalDrug, Healthcare and Patient Safety by authors Yallew et al.
Purpose: Hospital-acquired infection (HAI) is a major safety issue affecting the quality of care of hundreds of millions of patients every year, in both developed and developing countries, including Ethiopia. In Ethiopia, there is no comprehensive research that presents the whole picture of HAIs in hospitals. The objective of this study was to examine the nature and extent of HAIs in Ethiopia.
Methods: A repeated cross-sectional study was conducted in two teaching hospitals. All eligible inpatients admitted for at least 48 hours on the day of the survey were included. The survey was conducted in dry and wet seasons of Ethiopia, that is, in March to April and July 2015. Physicians and nurses collected the data according to the Centers for Disease Control and Prevention definition of HAIs. Coded and cleaned data were transferred to SPSS 21 and STATA 13 for analysis. Univariate and multivariable logistic regression analyses were used to examine the prevalence of HAIs and relationship between explanatory and outcome variables.
Results: A total of 908 patients were included in this survey, the median age of the patients was 27 years (interquartile range: 16–40 years). A total of 650 (71.6%) patients received antimicrobials during the survey. There were 135 patients with HAI, with a mean prevalence of 14.9% (95% confidence interval 12.7–17.1). Culture results showed that Klebsiella spp. (22.44%) and Staphylococcus aureus (20.4%) were the most commonly isolated HAI-causing pathogens in these hospitals. The association of patient age and hospital type with the occurrence of HAI was statistically significant.
Conclusion: It was observed that the prevalence of HAI was high in the teaching hospitals. Surgical site infections and pneumonia were the most common types of HAIs. Hospital management should give more attention to promoting infection prevention practice for better control of HAIs in teaching hospitals.
Read the original article here: https://www.dovepress.com/point-prevalence-of-hospital-acquired-infections-in-two-teaching-hospi-peer-reviewed-article-DHPS

Views: 215
Dove Medical Press

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Measures of central tendency include descriptive statistics including the mean, median and mode that are used to describe what the average person or response in a particular study is like. It is important as a research consumer to understand how these statistics are calculated and used to summarize and organize information in a study.
Before talking about these measures of central tendency, it is important to know what a normal distribution is. The best measure of central tendency depends on a number of things including weather data has a normal distribution or not. The theoretical concept of a normal distribution is covered in more depth in another video, but simply put it is the idea that when data are gathered from interval or ratio level measures and plotted on a graph it will resemble a normal curve.
The three measures of central tendency described in this video would all fall at the same midline point on a normal distribution curve. However, if data are not normally distributed certain measures may be better than others. The appropriateness of each measure is also influenced by the level of measurement used in the study.
Throughout this video I will have examples of how to calculate the mean, median and mode on the screen. These examples will use the data I made up for a fake study about hours students spend watching online videos and reading for studying purposes.
In statistics, mean is synonymous with the average. Whether it is true or not you could try remembering that the average girl can be mean when they want to be.
Or, if you can remember what the other two are so you can figure this one out through the process of elimination.
You may remember how to calculate averages from math class. To calculate the mean or average of a group of numbers, first add all the numbers. Then, divide by the number of values.
The mean or average is the most common, best known and most widely used measure to describe the center of a frequency distribution. The mean is influenced by all data in a Study. For this reason, it works best for symmetrical distributions of data where there are no outliers or extremes. However, the larger the data set the smaller the influence of any extreme scores will be. The mean is the most commonly use measure because it is considered the most reliable measure of central tendency when making inferences from a sample population. However, it is only appropriate for interval and ratio level data.
The Median is the value in the middle of a set of data. One way to remember that median means middle is to try associating it with the word medium. Median and medium sound sort of similar. They also both start with the letters MED. A medium pizza or a medium coffee is typically the size in the middle range at a store.
If there is an even number of values simply divide the two numbers in the middle by 2.
Unlike the Mean, the mode is not influenced by extreme values in a data set. Therefore, it is a good measure to use when distributions are not symmetrical. If a researcher is working with data that are not normally distributed and wants to know what the typical score is the median is likely the best measure to use. In this situation both the mean and median would likely be reported. The median is limited because it is not algebraically defined. Instead it is simply the point in the middle of the data set. While it is useful for ordinal, interval and ratio levels of measurement it cannot be used for nominal data.
The Mode is the most frequent value, number or category in a set of data. One way to remember this definition is that Mode sounds like Most. Both mode and most start with the letters MO.
The mode is the only measure of central tendency you can use for nominal data. While it can be used for all levels of measurement, it is considered unstable since fluctuations are likely between sample populations. Sometimes there is no mode. If all scores are different the mode does not exist. Sometimes there are multiple modes. If several values occur with equal frequency there are several modes. Unfortunately the mode can't be used for any further calculations in the study -- it can only help to describe the central tendency of the population.

Views: 134507
NurseKillam

What is International Relations?
Our richly connected, complex world demands professionals skilled in international relations, an exciting field of study that presents a globally oriented perspective on issues that transcend national boundaries.
The study and practice of international relations is interdisciplinary in nature, blending the fields of economics, history, and political science to examine topics such as human rights, global poverty, the environment, economics, globalization, security, global ethics, and the political environment.
The Theories and Principles of International Relations -
International relations may be an offshoot of political science, but this field of study is exceptionally in-depth in its own right. As our global society evolves and expands, international relations will evolve and expand along with it as we continue to explore new and exciting way to link our complex world.
For example, traditional dimensions of international relations related to international peace and prosperity include topics such as international diplomacy, arms control, and alliance politics. Contemporary studies in international relations, on other hand, include topics such as international political economics, environmental politics, refugee and migration issues, and human rights.
Examining the Levels of State Behavior
Professionals studying international relations often determine the level at which they will analyze a state’s behavior:
System Level Analysis: System level analysis looks at the international system; more specifically, how the international system affects the behavior of nation states, with the key variable being that the international system includes the power of each state rather than being independent of them.
State Level Analysis: State level analysis examines how a state’s characteristics determine its foreign policy behavior. This type of analysis often views states as having cultural characteristics based on their religious or social traditions, and their historical legacy, and includes an analysis of economic and geographic factors.
Organizational Level Analysis: Organizational level analysis examines how organizations within a state influence the state’s foreign policy behavior. In other words, organizational level analysis views that organizations—not states—make the decisions that create a state’s foreign policy.
Individual Level Analysis: Individual level analysis views the leaders of states as being the largest influencers of foreign policy.
Examining the Theories of International Relations
The study of international relations involves theoretical approaches based on solid evidence. Theories of international relations are essentially a set of ideas aimed at explaining how the international system works.
The two, major theories of international relations are realism and liberalism:
Realism
Realism focuses on the notion that states work to increase their own power relative to other states. The theory of realism states that the only certainty in the world is power; therefore, a powerful state—via military power (the most important and reliable form of power)—will always be able to outlast its weaker competitors. Self-preservation is a major theme in realism, as states must always seek power to protect themselves.
In realism, the international system drives states to use military force. Although leaders may be moral, they must not let morality guide their foreign policy. Furthermore, realism recognizes that international organizations and law have no power and force, and that their existence relies solely on being recognized and accepted by select states.
Liberalism (Idealism)
Liberalism recognizes that states share broad ties, thus making it difficult to define singular independent national interests. The theory of liberalism in international relations therefore involves the decreased use of military power. The theory of realism began to take shape in the 1970s as increasing globalization, communications technology, and international trade made some scholars argue that realism was outdated.
Liberal approaches to the study of international relations, also referred to as theories of complex interdependence, claim that the consequences of military power outweigh the benefits and that international cooperation is in the interest of every state. It also claims that exercising economic power over military power has proven more effective.
Although the liberal theory of international relations was dominant following World War I while President Woodrow Wilson promoted the League of Nations and many treaties abolishing war, realism came back into prominence in the Second World War and continued throughout the Cold War.

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First Education - The Live Classes

Sadhguru answers a question on whether and how casual relationships without emotional involvement can affect us.
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Sadhguru

It is common practice to normalize data before using an outlier detection method. But which method should we use to normalize the data? Does it matter? The short answer is yes, it does. The choice of normalization method may increase or decrease the effectiveness of an outlier detection method on a given dataset. In this talk we investigate this triangular relationship between datasets, normalization methods and outlier detection methods.

Views: 519
R Consortium

In this tutorial we learn how to perform Monte Carlo iteration analysis to account for uncertainty in variables. In this scenario, we examine uncertainty in cost, benefit and growth rate values for a project and how they affect the net present value (NPV).
Excel document link: https://drive.google.com/open?id=0B_lD7FHorWGzSGhMWHh5Ukx3Vzg
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Formulas:
Cell F2: =RAND()*(D2-C2)+C2
- Generates a random value between the established parameters.
Cell I2: =1/1.05^H2
- Discount factor; accounts for our preference to consume now rather than later.
Cell J4: =J3*(1+$F$4)
- Accounts for the growth rate of benefits at a given percent per year.
Cell K2: =I2*J2
- The present value of costs and benefits after discounting.
Cell T1: =AVERAGE(R3:R102)
- Average NPV.
Cell T2: =STDEV(R3:R102)
- Standard deviation of NPV.
Cell T3: =MIN(R3:R102)
- Minimum NPV
Cell T4: =MAX(R3:R102)
- Maximum NPV
Normal Distributions and Bell Curves Tutorial: http://www.youtube.com/watch?v=50kZjl-7ZaQ
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Grow Your Career

✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪
What is BIOPSYCHOSOCIAL MODEL? What does BIOPSYCHOSOCIAL MODEL mean? BIOPSYCHOSOCIAL MODEL meaning - BIOPSYCHOSOCIAL MODEL definition - BIOPSYCHOSOCIAL MODEL explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
The biopsychosocial model is a broad view that attributes disease outcome to the intricate, variable interaction of biological factors (genetic, biochemical, etc), psychological factors (mood, personality, behavior, etc.), and social factors (cultural, familial, socioeconomic, medical, etc.). The biopsychosocial model counters the biomedical model, which attributes disease to roughly only biological factors, such as viruses, genes, or somatic abnormalities. The biopsychosocial model applies to disciplines ranging from medicine to psychology to sociology; its novelty, acceptance, and prevalence vary across disciplines and across cultures.
In a 1977 article in Science, psychiatrist George L. Engel called for "the need for a new medical model." Engel later discussed a hypothetical patient, a 55-year-old man sustaining a second heart attack six months after his first. The patient's personality frames his own interpretation of his chest pain and explains his denial of it until his employer grants him permission to seek help. Although his heart attack can be attributed to an arterial blood clot, the wider personal perspective helps to understand that different outcomes may be possible depending on how the person responds to his condition. Subsequently, the patient in the emergency room develops a cardiac arrest as a result of an incompetent arterial puncture. We can analyse this event in wider terms than just a cardiac arrhythmia. It sees the event as due to inadequate training and supervision of junior staff in an emergency room. Thus while "no single definitive, irreducible model has been published," Engel's example offers a starting point for broader understanding of clinical practice.
Some thinkers see the model in terms of causation. Its biological component seeks to understand how the cause of the illness stems from the functioning of the individual's body. Its psychological component looks for potential psychological causes for a health problem such as lack of self-control, emotional turmoil, and negative thinking. Its social part investigates how different social factors such as socioeconomic status, culture, technology, and religion can influence health. However, a closer reading of Engel's seminal paper in the American Journal of Psychiatry (1980) embeds the model far more closely into patient care. It is not just about causation but also about how any clinical condition (medical, surgical, or psychiatric) can be seen narrowly as just biological or more widely as a condition with psychological and social components, which will impinge on a patient's understanding of her condition and will affect the clinical course of that condition.
Drawing on the systems theory of Weiss and von Bertalanffy, Engel describes the commonsense observation that nature is a "hierarchically arranged continuum with its more complex, larger units superordinate on the less complex smaller units." He represents them schematically either as a vertical stack or as a nest of squares, with the simplest at the centre and the most complex on the outside. He subdivides the vertical stack into two stacks. The first starts with subatomic particles and ends with the individual person. The second starts with the person and finishes with the biosphere. The first is an organismic hierarchy, the second a social hierarchy. He then delineates some principles:
Each level in the system is relatively autonomous. Thus, a cell can be studied just as a cell.
Each level depends on the level below. Thus, a cell is composed of nuclei, mitochondria, and all sorts of other organelles.
Each level is a component of a higher-system cells organize together to become tissues, organs, etc. Thus, "in the continuity of natural systems every unit is at the very same time both a whole and a part." It is possible to add higher-level properties that emerge from lower level systems and cannot be predicted from studying the lower level as well as the principle of top-down causation namely that higher levels can influence lower levels.

Views: 19027
The Audiopedia

Principles of Auditing: Professor Liburd
Lecture 1
Overview
1/24/14
Please visit our website at http://raw.rutgers.edu
TIME STAMPS
0:19 In the Public Interest
1:58 Center for Audit Quality Website (& video)
6:44 Auditing vs. Accounting
11:35 Definition of Auditing
27:03 Purpose of Auditing
30:01 Information Risk
30:59 Assurance vs. Attestation
34:25 Sarbanes-Oxley Act
The purpose of this lecture is to provide the student with an overview of auditing and assurance services and the CPA profession as a while.
Auditing and accounting are technically two different fields, and thus should be distinguished. Accounting is the recording, classifying, and summarizing of economic events for the purpose of providing financial information used in decision making. Auditing is determining whether information that has already been recorded properly reflects the economic events that occurred during the accounting period.
More specifically, auditing is a systematic process of objectively obtaining and evaluating evidence regarding assertions (financial statements, including footnotes) about economic actions and events to ascertain the degree of correspondence between the assertions and established criteria (GAAP) and communicating the results (auditor's report and other reports) to interested users (persons who rely on the financial reports to make economic and financial decisions, such as creditors and investors).
An audit is performed by a competent, independent person (both independent in fact and independent in appearance). The auditor must be qualified to understand the criteria used and must be competent to know the types and amount of evidence to accumulate to reach the proper conclusion after the evidence has been examined. The competence of the individual performing the audit is of little value if he or she is biased in the accumulation and evaluation of evidence. Overall, auditors lend credibility to the financial statements presented by management.
There are numerous factors that have contributed towards the need of independent auditing today.
(1) Remoteness of information (i.e. lack of stockholder interaction with management, directors not being involved in daily operations and decision making, and dispersion of the business among numerous geographical locations and complex corporate structures).
(2) Biases and motives of the provider. Information will be biased in favor of the provider when his or her goals are inconsistent with the decision maker's goals.
(3) Voluminous data. Most business have to deal with millions of transactions processed daily via a sophisticated computerized system. There are also multiple product lines, and multiple transaction locations (probably for EACH of the aforementioned product lines). A fourth is
(4) Complex exchange transactions. New and changing business relationships lead to innovative accounting and reporting problems. The potential impact of transactions is not always quantifiable, which in turn leads to increased (and sometimes more complex) disclosures.
Auditing plays an important role in reducing information risk. Information risk reflects the possibility that the information upon which the business risk decision was made was inaccurate. Causes of information risk include the fact that it is nearly impossible for a decision maker to have much firsthand knowledge about the organization with which they do business (i.e. information from others must be relied upon). Furthermore, if information is provided by someone whose goals are inconsistent with those of the decision maker, the information may be biased in favor of the provider. The higher the volume of transactions, the greater the risk that improperly recorder information is included in the records. Exchange transactions between organizations have become increasingly complex and therefore more difficult to record properly.
There are four major elements of the broad definition of assurance services. (1) Independence-integrity and objectivity. (2) Professional services (which involves some element of judgment based on education and experience). (3) Improving the quality of information or its context (assuring users about the reliability and relevance of information). (4) For decision makers.
An attestation service is a type of assurance service in which the CPA firm issues a report about the reliability of an assertion that is the responsibility of another party. To attest means to lend credibility or to vouch for the truth or accuracy of the statements that one party makes to another. It is primarily financial information. There are five categories: (1) audit of historical financial statements, (2) attestation of internal control over financial reporting, (3) review of historical financial statements, (4) attestation services on info technology, (5) other attestation services.

Views: 49038
Rutgers Accounting Web

2014 Fall Meeting
Section: Hydrology
Session: Hydroclimatic Extremes: Drought II
Title: Exploring the linkage between drought, high temperatures, and hydrologic sensitivities: A case study of the 2012 Great Plains drought.
Authors:
Hoerling, M P, ESRL, NOAA Boulder, Boulder, CO, United States
Livneh, B, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
Abstract:
The occurrence of drought is associated with agricultural loss, water supply shortfalls, and other economic impacts. Here we explore the physical relationships between precipitation deficits, high temperatures, and hydrologic responses as a pathway to better anticipate drought impacts. Current methodologies to predict hydrologic scarcity include local monitoring of river flows, remote sensing of land-surface wetness, drought indices, expert judgment, climate indices (e.g. SST-relationships) and the application of hydrologic models. At longer lead times, predictions of drought have most frequently been made on the basis of GCM ensembles, with subsequent downscaling of those to scales over which hydrologic predictions can be made. This study focuses on two important aspects of drought. First, we explore the causal hydro-climatic timeline of a drought event, namely (a) the lack of precipitation, which serves to reduce soil moisture and produce (b) a skewed Bowen ratio, i.e. comparatively more sensible heating (warming) with less ET, resulting in (c) anomalously warm conditions. We seek to assess the extent to which the lack of precipitation contributes to warming temperatures, and the further effects of that warming on hydrology and the severity of drought impacts. An ensemble of GCM simulations will be used to explore the evolution of the land surface energy budget during a recent Great Plains drought event, which will subsequently be used to drive a hydrologic model. Second, we examine the impacts of the critical assumptions relating climatic variables with water demand, specifically the relationship between potential evapotranspiration (PET) and temperature. The common oversimplification in relating PET to temperature is explored against a more physically consistent energy balance estimate of PET, using the Penman-Monteith approach and the hydrologic impacts are presented. Results from this work are anticipated to have broad relevance for future water management and planning, to better characterize drought impacts.
Cite as: Author(s) (2014), Title, Abstract H31M-05 presented at 2014 Fall Meeting, AGU, San Francisco, Calif., 15-19 Dec.
Learn more here:
http://abstractsearch.agu.org/meetings/2014/FM/H31M-05

Views: 74
AGU

Alex Hinton
Executive Director
Center for the Study of Genocide, Conflict Resolution, and Human Rights
Alex Hinton is Executive Director of the Center for the Study of Genocide, Conflict Resolution, and Human Rights and Professor of Anthropology and Global Affairs and at Rutgers University, Newark.
He is the author of the award-winning Why Did They Kill? Cambodia in the Shadow of Genocide (California, 2005) and six edited or co-edited collections, Transitional Justice: Global Mechanisms and Local Realities after Genocide and Mass Violence (Rutgers, forthcoming in 2010), Genocide: Truth, Memory, and Representation (Duke, 2009), Night of the Khmer Rouge: Genocide and Democracy in Cambodia (Paul Robeson Gallery, 2007), Annihilating Difference: The Anthropology of Genocide (California, 2002), Genocide: An Anthropological Reader (Blackwell, 2002), and Biocultural Approaches to the Emotions (Cambridge, 1999). He is currently working on several other book projects, including a co-edited volume on the legacies of genocide and mass violence, a book on 9/11 and Abu Ghraib, and a book on the politics of memory and justice in the aftermath of the Cambodian genocide. He serves as an Academic Advisor to the Documentation Center of Cambodia, on the International Advisory Boards of the Journal of Genocide Research and Genocide Studies and Prevention, as co-editor of the CGHR-Rutgers University Press book series, "Genocide, Political Violence, Human Rights," and as the First Vice-President and Executive Board member of the International Association of Genocide Scholars.
In 2009, Alex Hinton received the Robert B. Textor and Family Prize for Excellence in Anticipatory Anthropology "for his groundbreaking 2005 ethnography Why Did They Kill? Cambodia in the Shadow of Genocide, for path-breaking work in the anthropology of genocide, and for developing a distinctively anthropological approach to genocide."
Antoinette Farmer, Ph.D.
Associate Dean of Academic Affairs and Associate Professor
School of Social Work
Antoinette Y. Farmer (Ph. D., University of Pittsburgh, 1991) is associate professor and associate dean for academic affairs at Rutgers University's School of Social Work. Her research focuses on examining the social and interpersonal factors that affect parenting as well as how parenting practices influence adolescent high risk behaviors, such as delinquency and substance use. This research agenda has been greatly influenced by the work of Jay Belsky, and she has also modified his ecological model as reflected in her research examining the buffering effect of social support on the relationship between parenting stress and parenting behavior. Her work in the area of parenting has led her to develop and test models to determine what variables may mediate the relationship between parenting and adolescent outcomes. She is also beginning to examine the effects of fathers' parenting practices on adolescents high risk behaviors. Her work has also examined the effects of religion/spirituality on adolescent high risk behaviors. In order to carry out her research agenda, she conducts quantitative data analysis using large national data sets. Her research has been published in Social Work, Journal of Social Service Research, and Children and Youth Services Review. She co-edited a special issue of the Journal of Social Service Research, which was devoted to informing researchers of the methodological issues confronting them when conducting research with minority and oppressed populations. She has also written several chapters on this issue as well, with the most recent appearing in the Handbook of Social Work Research Methods (2nd Edition). She has served as a consulting editor for Social Work in Education and on the editorial board for Children in Schools. Dr. Farmer has also presented at numerous national and international conferences.

Views: 218
Rutgers iTVStudio

020 - Biotic and Abiotic Factors
Paul Andersen differentiates between biotic and abiotic factors. He explains how both abiotic and biotic factors can affect organisms at the level of the cell, the population and even the ecosystem. The complexities of biofilms, predator-prey relationships, and food webs are given as illustrative examples.
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All of the images are licensed under creative commons and public domain licensing:
"File:American Beaver.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:American_Beaver.jpg.
"File:Biofilm.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Biofilm.jpg.
"File:Canadian Lynx by Keith Williams.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Canadian_lynx_by_Keith_Williams.jpg.
"File:Fuzzy Freddy.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Fuzzy_Freddy.jpg.
"File:Mandibulartori02-04-06.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Mandibulartori02-04-06.jpg.
"File:Mauna Loa Carbon Dioxide Apr2013.svg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Mauna_Loa_Carbon_Dioxide_Apr2013.svg.
"File:OPAL TERRACE with Elks.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:OPAL_TERRACE_with_elks.jpg.
"File:Reintroduced Wolves Being Carried to Acclimation Pens, Yellowstone National Park, January, 1995.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Reintroduced_wolves_being_carried_to_acclimation_pens,_Yellowstone_National_Park,_January,_1995.jpg.
"File:Salix Alba Leaves.jpg." Wikipedia, the Free Encyclopedia. Accessed November 18, 2013. http://en.wikipedia.org/wiki/File:Salix_alba_leaves.jpg.
"File:Snowbowlaspens.jpg." Wikipedia, the Free Encyclopedia, November 17, 2013. http://en.wikipedia.org/w/index.php?title=File:Snowbowlaspens.jpg&oldid=483258350.
Intro Music Atribution
Title: I4dsong_loop_main.wav
Artist: CosmicD
Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/
Creative Commons Atribution License

Views: 198571
Bozeman Science