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
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 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:
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.