Explain The Sources Of Error In Measurement
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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 3 sources of error in measurement it possible that some errors are systematic, that they hold across most
Types Of Errors In Measurement
or all of the members of a group? One way to deal with this notion is to revise the different types of errors in measurement 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
Possible Sources Of Error In Measurement
try to diagnose their effects on our research. What is Random Error? 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 sources of error in measurement in research 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 consistent
in measurement to have a general knowledge of likely error sources, so that: errors can be controlled where possible or the effects of the error can be considered. With this understanding, a uniform standard of precision can
Sources Of Error In Measurement Lab
be applied in all of the steps involved in arriving at an estimate. Such a
Sources Of Error In Measurement In Research Methodology
standard reduces the chance of wasting resources by measuring some things with little error, and others with great error when the final result factors contributing to measurement error uses both measurements. Errors arise from many sources. It pays the natural resource manager or scientist to determine as early as possible what are likely to be the dominant sources of error in the measurement task and to http://www.socialresearchmethods.net/kb/measerr.php devote sufficient time to devising ways of reducing these errors. This is best accomplished by a preliminary trial - in short, a rehearsal. As well as providing a provisional estimate of the size of the various errors, the rehearsal enables one to check that the procedures are appropriate and sound. There are four kinds of error: mistake accidental error bias sampling error Mistake Mistakes are caused by human carelessness, casualness or fallibility, e.g. incorrect use http://fennerschool-associated.anu.edu.au/mensuration/BrackandWood1998/ERROR.HTM or reading of an instrument, error in recording, arithmetic error in calculations. There is no excuse for mistakes, but we all make them! In general, never be satisfied with a single reading no matter what you are measuring. Repeat the measurement. This shows up careless mistakes and improves the precision of the final result. Accidental error Accidental errors are unavoidable. They arise due to inconstant environmental conditions, limitations or deficiencies of instruments, assumptions and methods. Accidental error is usually not important as the error tends to be compensating. Accidental error can be reduced by using more accurate and precise equipment but this can be expensive. A competent scientist is expected to be able to assess in advance how good an instrument needs to be in order to give results of an accuracy sufficient for the task in hand. In other words, he / she is expected to make an appropriate choice from the equipment available (or to design a more appropriate instrument). Bias Bias is a systematic distortion in a measurement, i.e. it is a non-compensating error. Common sources of bias are: flaw in measurement instrument or tool, e.g. survey tape 50 cm short; flaw in the method of selecting a sample, e.g. stocking counts - some observers always count the boundary tree, others always exclude it; flaw in the technique of estimating a p
is often not met with in entirety. As such, the researcher must be aware about the sources of error in measurement. Following http://blog.reseapro.com/2013/01/sources-of-error-in-measurement/ are listed the possible sources of error in measurement. a) Respondent: At times the respondent may be reluctant to express strong negative feelings or it is just possible that he may have very little knowledge, but may not admit his ignorance. All this reluctance is likely to result in an interview of ‘guesses.' Transient factors like fatigue, boredom, anxiety, of error etc. may limit the ability of the respondent to respond accurately and fully. b) Situation: Situational factors may also come in the way of correct measurement. Any condition which places a strain on interview can have serious effects on the interviewer-respondent rapport. E.g., if someone else is present, he can distort responses by joining in or merely by sources of error being present. If the respondent feels that anonymity is not assured, he may be reluctant to express certain feelings. c) Measurer: The interviewer can distort responses by rewording or reordering questions. His behavior, style and looks may encourage or discourage certain replies from respondents. Careless mechanical processing may distort the findings. Errors may also creep in because of incorrect coding, faulty tabulation and/or statistical calculations, particularly in the data-analysis stage. d) Instrument: Error may arise because of the defective measuring instrument. The use of complex words, beyond the comprehension of the respondent, ambiguous meanings, poor printing, inadequate space for replies, response choice omissions, etc. are a few things that make the measuring instrument defective and may result in measurement errors. Hence, researcher must know that correct measurement depends on successfully meeting all of the issues mentioned above. He must, as far as possible, try to eliminate, neutralize or otherwise deal with all the possible sources of error so that the final results may not be contaminated. Tweet This entry was posted in E
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