RESEARCH ERROR- SAMPLING ERROR

RESEARCH ERROR

There are two broad types of error pres­ent in all research:

 (1) SAMPLING ERROR, or error related to selecting a sample from a population; and

(2) NONSAMPLING ERROR, such as

Measurement er­rors,

Data analysis errors,.

Mea­surement error : Some of the most common measurement errors include:

·        A poorly designed measurement instrument

·        Asking respondents the wrong questions or asking questions incorrectly.

It  is further divided into two categories:

Random error: Random error relates to problems where mea­surements and analyses vary inconsistently from one study to another—

Systematic error: sys­tematic error consistently produces incor­rect (invalid) results in the same direction, or same context, and is, therefore, predictable.

The cause of systematic errors and eliminate their influence.

·       Faulty data collection equipment

·       Untrained data collection personnel

·       Using only one type of measurement instead of multiple measures

·       Data input errors

SAMPLING ERROR

Sampling error find out by  an estimate of the difference between observed and expected measurements and is the foundation of all research interpretation.

Sampling error provides an indication of how close the data from a sample are to the population mean. A low sampling error indi­cates that there is less variability or range in the sampling distribution.

A theoretical sampling distri­bution is the set of all possible samples of a given size. This distribution of values is described by a bell-shaped curve or normal curve (also known as a Gaussian distribu­tion, after Karl F. Gauss, a German math­ematician and astronomer who used the concept to analyze observational errors).

There are two important terms related to computing errors due to sampling:

standard error (designated as SE) and

sampling error, which is also referred to as margin of error or confidence inter­val (designated as se or m, or Cl).

(1) Stan­dard error relates to the population and how samples relate to that population. Standard error is closely related to sample size—as sample size increases, the standard error decreases.

Confidence Level and   Confidence Interval

Sampling error involves two concepts:

Con­fidence Level  

and

Confidence Interval.

The confidence level indicates a degree of certainty (as a per­centage) that that the results of a study fall within a given range of values. Typical con­fidence levels are 95% and 99%.

The confi­dence interval is a plus-or-minus percentage that is a range within the confidence level. For ex­ample, if a 5% confidence interval is used, and 50% of the sample gives a particular answer for a question, the actual result for that question falls between 45% and 55% (50 ± 5).

  

In every normal distribution, the standard deviation defines a standard unit of distance from the mean of the distribution to the outer limits of the distribution. These standard deviation interval units (z-values) are used in establishing the confidence in­terval that is accepted in a research project. In addition, the standard deviation units in­dicate the amount of standard error. For ex­ample, using a confidence level of + 1 or –1 standard deviation unit-1 standard error—says that the probability is that 68% of the samples selected from the population will produce estimates within that distance from the population value (1 standard deviation unit; see Figure 4.3).

Computing Sampling Error

There are several ways to compute sam­pling error, but no single method is appropri­ate for all sample types or all situations.

Sampling error is an important concept in all research areas because it provides an indication of the degree of accuracy of the research, provides some type of explanation about error.