This is a five-section course as part of a two-course sequence in Research Methods in Psychology. This course deals with experimental methods whereas the other course dealt with descriptive methods.

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From the course by Georgia Institute of Technology

Experimental Research Methods in Psychology

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Georgia Institute of Technology

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This is a five-section course as part of a two-course sequence in Research Methods in Psychology. This course deals with experimental methods whereas the other course dealt with descriptive methods.

From the lesson

Evaluating Causal Claims

- Dr. Anderson D. SmithRegentsâ€™ Professor Emeritus

School of Psychology

Anderson Smith and we're talking about validity.

Â Let's spend a little more time talking about internal validity and

Â using statistics to tell us what is valid, and what isn't valid.

Â So internal validity means that we, and we've talked about that a lot in this

Â course means that we are careful in making casual references in designing whatever it

Â was on and making sure that we don't have confounded variables.

Â That is experimental manipulations are controlled.

Â We have to make sure that no confounding variables.

Â And if there are mediating variables, we have to be aware of what they are.

Â We have to understand those occur when we are designing an experiment.

Â We have to also as we talked about,

Â we have to make sure our measurements are reliable, well-defined and precise.

Â They're reliable and they're valid.

Â So internal validity and we discuss about how to control for confounding variables,

Â and we can do that by matching that as we know what the confounding variable is.

Â So, we'll simply match at subjects in the groups on that variable.

Â So now, they can't confound what it is that we're manipulating.

Â We can actually include the variable in the multi-variable experiment.

Â That is if we have a confounding variable,

Â we simply make that another factor in an experiment.

Â So, we can determine what the relative effects are of that confounding variable

Â and the variable that we're interested in.

Â That's probably one of the best ways to control an experiment.

Â And if they're different measures, one confounding that might confound,

Â well, then we can actually come of with statistical controls called

Â a covariance design that partials that out.

Â So analysis of covariance which its called,

Â actually looks to see whether that confounding variable.

Â We use that as a covariate, so we have to measure it.

Â And then we actually assess that statistically and remove it, so

Â we can look at is the answer relationship between independent variable and

Â dependent variable after controlling for the variance or

Â remove it from the analysis and

Â that's the way of looking at the effects of this confounding variable.

Â Now in doing that, in using it analysis of covariance, we're really talking

Â about this important step after manipulating the independent variable and

Â collecting our measures to the dependent variable.

Â In the experimental design, we know we first have a rationale for

Â making a hypothesis.

Â We have a hypothesis.

Â We do the empirical study where we manipulate the independent variable and

Â measure the dependent variable, and then we do the data analysis.

Â In only after we do the data analysis, the statistical analysis.

Â We know whether or not the difference that we looking at is reliable and valid.

Â It's a good one and

Â that lead us back a conclusions back to the research literature itself.

Â So, let's talk a little bit more about statistical analysis.

Â Now when we analyze an experiment to look at a causal effect,

Â we're used to what it called Inferential Statistics.

Â Is there a difference in a dependent variable that is caused by

Â the manipulation in an independent variable?

Â Or as we say, is it difference to the chance?

Â So, is that difference we're looking at a reliable and valid difference?

Â Is it a good difference that it actually is statistically significant or

Â is it a difference that is due to chance?

Â Is the dependent variable a function of the independent variable?

Â That's the goal of the experiment.

Â Is there a causal effect between manipulating the independent variable and

Â measuring the dependent variable?

Â So, different kinds of statistics.

Â There are descriptive statistics that are used in descriptive studies and

Â then there are inferential statistics that are used in experimental studies where

Â we're looking for causal effects.

Â So in descriptive studies, we collect data.

Â We describe the data in a meaningful way.

Â We organize the data.

Â We summarize the data.

Â We correlate the data.

Â We're simply describing what's there.

Â With inferential statistics, however, we're doing hypothesis testing.

Â We're actually making a hypothesis and then testing it.

Â We're making inferences about whether or

Â not the effect we see is a meaningful effect, or not.

Â We're determining whether there's a relationship between the independent

Â variable and the dependent variable.

Â Very different kinds of outcomes from just describing the data,

Â which we'd use in descriptive studies.

Â So inferential statistics or hypothesis testing, we try to draw

Â conclusions about the population based on the sample we used in an experiment.

Â The conclusion now is not guaranteed to be correct.

Â What the statistics will tell us is to what extent is it a correct conclusion?

Â Is it something which is a difference that we observe?

Â Something which we will assume is a good,

Â different set shows the effect and the conclusion therefore is a good one.

Â But remember, it's not guarantee we might be making an error.

Â There's something called the null hypothesis and

Â the null hypothesis will state there is no difference in the manipulation.

Â Now testing the null hypothesis is very difficult,

Â because sometimes it's difficult as I'll show

Â you to disprove or prove no difference.

Â So, inferential statistics allow us to at least test it and

Â come up with a probability that we are correct or incorrect.

Â That's what they do.

Â So with hypothesis testing, we have two kinds of errors.

Â If the true state of world might be that the hypothesis is false or

Â that the hypothesis is true, then null hypothesis.

Â So that means if the null hypothesis is false, there is a real difference.

Â And if the null hypothesis is true, there is no difference.

Â And so if we reject the null hypothesis when, in fact,

Â the null hypothesis is false, that's a correct decision.

Â A type II error would be that the null hypothesis is false and

Â we say, the null hypothesis is true.

Â We say, there's no difference when there's really a difference.

Â That's kind of rare.

Â More likely, however is the null hypothesis is true and

Â we say that the null hypothesis is false.

Â That is a type I error.

Â We reject the null hypothesis.

Â We say that there is a difference when, in fact, there's no difference and

Â that's a type I and that's really what we want to avoid.

Â We want to find the difference and show whether or

Â not we have a difference or not.

Â And so we want to be able to not have a type I error which says that

Â there's no difference and we say, there's a difference.

Â And so in type I error which is basically saying

Â that the sampling error can produce extreme results and

Â type II error says, research lacks in statistical power a low sample size.

Â So, we use inferential statistics to inform us about

Â the probability of making a type I error.

Â This one we want to really avoid.

Â We do not want to say, hypothesis is true when it is in fact,

Â not true and our difference is really chance, type I error.

Â So we have to have some idea of what is a probability that our difference is due to

Â chance and we can do that through inferential statistics,

Â because what they tell us is the probability of making that error.

Â So what we do is we have some, it's truly arbitrary.

Â But it's collectively agreed upon chance level and we'll adopt that.

Â Typically, what you used is a chance of 5%.

Â So we want to show an experiment that the probability of making a type I error

Â that is of saying that we have a difference when, in fact, it's only chance

Â is less than 5% and that's the standard which is used in inferential statistics.

Â We might adopt a more stringent criterion like 1% or

Â 0.01% or 0.001%, but

Â most researchers would agree that the 5% is sort of at

Â least the maximum when distributed used and this is how we do it.

Â In hypothesis testing,

Â we have this probability that a null hypothesis is true and we want to say,

Â only look at the difference that meets this P value of less than 5%.

Â If the probability that Ho is true is less than 5%, then we'll say, okay,

Â we've got a difference.

Â It's not a null hypothesis.

Â It's a difference based on a hypothesis of a difference.

Â Thank you.

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