Discuss the meaning of scale validation as a \"continuous process.\" Briefly des

Question

Discuss the meaning of scale validation as a "continuous process." Briefly describe and compare internal and external validity. In your answer, be sure to include for each type: (a) a definition, (b) the critical issue it addresses, and (c) whether it is higher or lower in experimental versus no experimental (field) research List the treats to internal validity. Give a brief explanation of each of the treats Compare random selection and random assignment. In your response, be sure to define each term and an explanation for how they differ Explain the role of inferential statistics in research design and hypothesis testing. Give a definition of statistical Type I and Type II errors, and statistical power In your opinion, should all research be held strictly to the criterion of generalizability? Explain why or why not

Explanation / Answer

4)

A streamlined process that involves ongoing production of end products or services. More informally, this process keeps going on 24-hours a day and 7 days a week. For example, stock exchange markets continuously monitor stock performances and report them to the investor on a regular basis.

7)

Random selection requires the use of some form of random sampling (such as stratified random sampling, in which the population is sorted into groups from which sample members are chosen randomly). Random sampling is a probability sampling method, meaning that it relies on the laws of probability to select a sample that can be used to make inference to the population; this is the basis of statistical tests of significance.

Random assignment takes place following the selection of participants for the study. In a true experiment, all study participants are randomly assigned either to receive the treatment or to act as a control in the study (meaning they do not receive the treatment). Although random assignment is a simple procedure (it can be accomplished by the flip of a coin), it can be challenging to implement outside of controlled laboratory conditions.

8)

Type I error

When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is , which is the level of significance you set for your hypothesis test. An of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for . However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.

Type II error

When the null hypothesis is false and you fail to reject it, you make a type II error. The probability of making a type II error is , which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists.

The power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. Statistical power is inversely related to beta or the probability of making a Type II error. In short, power = 1 – .

In plain English, statistical power is the likelihood that a study will detect an effect when there is an effect there to be detected. If statistical power is high, the probability of making a Type II error, or concluding there is no effect when, in fact, there is one, goes down.

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