which of the following represents a common threat to statistical conclusion validity?>course hero

What is a common threat to statistical conclusion validity?

Threats lead you to make incorrect conclusions about relationships. They include: Fishing (mining the data and repeating tests to find something… anything! significant…): can result in incorrectly concluding there is a relationship when in fact there is not.

What is a common threat to statistical conclusion validity quizlet?

Extraneous Variance in the Experimental Setting.

What are threats to conclusion validity?

A threat to conclusion validity is a factor that can lead you to reach an incorrect conclusion about a relationship in your observations. You can essentially make two kinds of errors about relationships: Conclude that there is no relationship when in fact there is (you missed the relationship or didn't see it)

What threatens statistical validity?

Any effect that can impact the internal validity of a research study may bias the results and impact the validity of statistical conclusions reached. These threats to internal validity include unreliability of treatment implementation (lack of standardization) or failing to control for extraneous variables.

What is statistical conclusion validity in research?

Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statistical methods are used whose small-sample behavior is accurate, besides being logically capable of providing an answer to the research question.

What are threats to validity quizlet?

Threat to construct validity. Experimenter Expectancies (Construct Validity) The extent to which it is possible that the researcher's beliefs, ideas, hopes, opinions, and hypotheses inadvertently affected participants responses.

What factors influence statistical conclusion validity quizlet?

Terms in this set (9) Low Power. Variability in Experimental Procedures. Heterogeneity in the Participants. Unreliable Measures. Artifacts. Multiple Comparisons.

What is threat to validity in research?

Threats to internal validity of your study design might mean that factors outside of the program or treatment could account for the results obtained from the evaluation.

What is statistical validity in research?

Statistical validity can be defined as the extent to which drawn conclusions of a research study can be considered accurate and reliable from a statistical test. To achieve statistical validity, it is essential for researchers to have sufficient data and also choose the right statistical approach to analyze that data.

What are threats to validity in quantitative research?

There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction and attrition.

What does threats to validity mean?

From a research design standpoint, the simplest way to understand threats to validity is that a hypothesis might be tested in a manner other than what the researcher had intended—a situation not to be confused with the researcher's failure to obtain the result he or she had expected.

Which of the following is a threat to external validity?

There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment and situation effect.

What makes a conclusion valid?

Valid: an argument is valid if and only if it is necessary that if all of the premises are true, then the conclusion is true; if all the premises are true, then the conclusion must be true; it is impossible that all the premises are true and the conclusion is false.

What are the 12 threats to internal validity?

These threats to internal validity include: ambiguous temporal precedence, selection, history, maturation, regression, attrition, testing, instrumentation, and additive and interactive threats to internal validity.

What are the different dangers to be avoided in drawing up conclusion?

Six Things to AVOID in Your Conclusion1: AVOID summarizing. ... 2: AVOID repeating your thesis or intro material verbatim. ... 3: AVOID bringing up minor points. ... 4: AVOID introducing new information. ... 5: AVOID selling yourself short. ... 6: AVOID the phrases “in summary” and “in conclusion.”

What are threats to external validity?

What are threats to external validity? There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment and situation effect.

What is statistical conclusion validity?

Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statistical methods are used whose small-sample behavior is accurate , besides being logically capable of providing an answer to the research question.

What is the ultimate goal of research?

The ultimate goal of research is to produce dependable knowledge or to provide the evidence that may guide practical decisions. Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statis ….

What are the problems with the needle in the haystack?

When you’re looking for the needle in the haystack you essentially have two basic problems: the tiny needle and too much hay. You can view this as a signal-to-noise ratio problem.The “signal” is the needle — the relationship you are trying to see. The “noise” consists of all of the factors that make it hard to see the relationship. There are several important sources of noise, each of which is a threat to conclusion validity. One important threat is low reliability of measures (see reliability ). This can be due to many factors including poor question wording, bad instrument design or layout, illegibility of field notes, and so on. In studies where you are evaluating a program you can introduce noise through poor reliability of treatment implementation. If the program doesn’t follow the prescribed procedures or is inconsistently carried out, it will be harder to see relationships between the program and other factors like the outcomes. Noise that is caused by random irrelevancies in the setting can also obscure your ability to see a relationship. In a classroom context, the traffic outside the room, disturbances in the hallway, and countless other irrelevant events can distract the researcher or the participants. The types of people you have in your study can also make it harder to see relationships. The threat here is due to random heterogeneity of respondents. If you have a very diverse group of respondents, they are likely to vary more widely on your measures or observations. Some of their variety may be related to the phenomenon you are looking at, but at least part of it is likely to just constitute individual differences that are irrelevant to the relationship being observed.

What is the significance of 0.05?

In the social sciences, researchers often use the rather arbitrary value known as the 0.05 level of significance to decide whether their result is credible or could be considered a “fluke.” Essentially, the value 0.05 means that the result you got could be expected to occur by chance at least 5 times out of every 100 times you run the statistical analysis. The probability assumption that underlies most statistical analyses assumes that each analysis is “independent” of the other. But that may not be true when you conduct multiple analyses of the same data. For instance, let’s say you conduct 20 statistical tests and for each one you use the 0.05 level criterion for deciding whether you are observing a relationship. For each test, the odds are 5 out of 100 that you will see a relationship even if there is not one there (that’s what it means to say that the result could be “due to chance”). Odds of 5 out of 100 are equal to the fraction 5/100 which is also equal to 1 out of 20. Now, in this example, you conduct 20 separate analyses. Let’s say that you find that of the twenty results, only one is statistically significant at the 0.05 level. Does that mean you have found a statistically significant relationship? If you had only done the one analysis, you might conclude that you’ve found a relationship in that result. But if you did 20 analyses, you would expect to find one of them significant by chance alone, even if there is no real relationship in the data. We call this threat to conclusion validity fishing and the error rate problem. The basic problem is that you were “fishing” by conducting multiple analyses and treating each one as though it was independent. Instead, when you conduct multiple analyses, you should adjust the error rate (i.e., significance level) to reflect the number of analyses you are doing. The bottom line here is that you are more likely to see a relationship when there isn’t one when you keep reanalyzing your data and don’t take that fishing into account when drawing your conclusions.

What happens if you don't know the assumptions behind a statistical analysis?

If you are not sensitive to the assumptions behind your analysis you are likely to draw erroneous conclusions about relationships. In quantitative research we refer to this threat as the violated assumptions of statistical tests. For instance, many statistical analyses assume that the data are distributed normally — that the population from which they are drawn would be distributed according to a “normal” or “bell-shaped” curve. If that assumption is not true for your data and you use that statistical test, you are likely to get an incorrect estimate of the true relationship. And, it’s not always possible to predict what type of error you might make — seeing a relationship that isn’t there or missing one that is.

What does 0.05 mean in statistics?

Essentially, the value 0.05 means that the result you got could be expected to occur by chance at least 5 times out of every 100 times you run the statistical analysis. The probability assumption that underlies most statistical analyses assumes that each analysis is “independent” of the other.

Why is it hard to see relationships between programs?

If the program doesn’t follow the prescribed procedures or is inconsistently carried out, it will be harder to see relationships between the program and other factors like the outcomes. Noise that is caused by random irrelevancies in the setting can also obscure your ability to see a relationship.

What happens if a statistical test is not true?

If that assumption is not true for your data and you use that statistical test, you are likely to get an incorrect estimate of the true relationship. And, it’s not always possible to predict what type of error you might make — seeing a relationship that isn’t there or missing one that is.

What is threat to conclusion validity?

A threat to conclusion validity is a factor that can lead you to reach an incorrect conclusion about a relationship in your observations. You can essentially make two kinds of errors about relationships: Conclude that there is no relationship when in fact there is (you missed the relationship or didn’t see it) ...