In this discussion board provide 300 to 500 words on one of the topics below and

Question

In this discussion board provide 300 to 500 words on one of the topics below and take time to respond to classmates discussions. The discussion itself will be due Wednesday evening for our hybrid meeting, responses will be due by Friday. Also be certain to include references in APA or IEEE format (do not count as part of your word count).

Error Topics:

Measurement Errors

Systematic Errors

Random Errors

When discussing errors topic touch on the following:

- Identify methods for handling errors from your error topic

- Error Prevention

- An environment or scenario where this error will present it self or personal experience with handling this type of error

Explanation / Answer

Measurement Error

The true score theory is a good simple model for measurement, but it may not always be an accurate reflection of reality. In particular, it assumes that any observation is composed of the true value plus some random error value. But is that reasonable? What if all error is not random? Isn't it possible that some errors are systematic, that they hold across most or all of the members of a group? One way to deal with this notion is to revise the simple true score model by dividing the error component into two subcomponents, random error and systematic error. here, we'll look at the differences between these two types of errors and try to diagnose their effects on our research.

The measurement error is the result of the variation of a measurement of the true value. Usually, Measurement error consists of a random error and systematic error. The best example of the measurement error is, if electronic scales are loaded with 1kg standard weight and the reading is 10002grams, then

The measurement error is = (1002grams-1000grams) =2grams

Measurement Errors are classified into two types: systematic error and random errors

Systematic Errors

The Systematic errors that occur due to fault in the measuring device are known as systematic errors. Usually they are called as Zero Error – a positive or negative error. These errors can be detached by correcting the measurement device. These errors may be classified into different categories.

[Systematic Errors]

Systematic Errors

In order to understand the concept of systematic errors, let us classify the errors as:

Instrumental Errors
Environmental Errors
Observational Errors
Theoritical

Instrumental Errors

Instrumental errors occur due to wrong construction of the measuring instruments. These errors may occur due to hysteresis or friction. These types of errors include loading effect and misuse of the instruments. In order to reduce the gross errors in measurement, different correction factors must be applied and in the extreme condition instrument must be recalibrated carefully.

Random error is caused by any factors that randomly affect measurement of the variable across the sample. For instance, each person's mood can inflate or deflate their performance on any occasion. In a particular testing, some children may be feeling in a good mood and others may be depressed. If mood affects their performance on the measure, it may artificially inflate the observed scores for some children and artificially deflate them for others. The important thing about random error is that it does not have any consistent effects across the entire sample. Instead, it pushes observed scores up or down randomly. This means that if we could see all of the random errors in a distribution they would have to sum to 0 -- there would be as many negative errors as positive ones. The important property of random error is that it adds variability to the data but does not affect average performance for the group. Because of this, random error is sometimes considered noise.

What is Systematic Error?

Systematic error is caused by any factors that systematically affect measurement of the variable across the sample. For instance, if there is loud traffic going by just outside of a classroom where students are taking a test, this noise is liable to affect all of the children's scores -- in this case, systematically lowering them. Unlike random error, systematic errors tend to be consistently either positive or negative -- because of this, systematic error is sometimes considered to be bias in measurement.

Reducing Measurement Error

So, how can we reduce measurement errors, random or systematic? One thing you can do is to pilot test your instruments, getting feedback from your respondents regarding how easy or hard the measure was and information about how the testing environment affected their performance. Second, if you are gathering measures using people to collect the data (as interviewers or observers) you should make sure you train them thoroughly so that they aren't inadvertently introducing error. Third, when you collect the data for your study you should double-check the data thoroughly. All data entry for computer analysis should be "double-punched" and verified. This means that you enter the data twice, the second time having your data entry machine check that you are typing the exact same data you did the first time. Fourth, you can use statistical procedures to adjust for measurement error. These range from rather simple formulas you can apply directly to your data to very complex modeling procedures for modeling the error and its effects. Finally, one of the best things you can do to deal with measurement errors, especially systematic errors, is to use multiple measures of the same construct. Especially if the different measures don't share the same systematic errors, you will be able to triangulateacross the multiple measures and get a more accurate sense of what's going on.

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