The idea behind reliability is that any significant results must be more than a one-off finding and be inherently repeatable. Other researchers must be able to perform exactly the same experiment , under the same conditions and generate the same results.
This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis. Without this replication of statistically significant results , the experiment and research have not fulfilled all of the requirements of testability. This prerequisite is essential to a hypothesis establishing itself as an accepted scientific truth.
For example, if you are performing a time critical experiment, you will be using some type of stopwatch. Generally, it is reasonable to assume that the instruments are reliable and will keep true and accurate time. However, diligent scientists take measurements many times, to minimize the chances of malfunction and maintain validity and reliability.
At the other extreme, any experiment that uses human judgment is always going to come under question. Human judgment can vary wildly between observers , and the same individual may rate things differently depending upon time of day and current mood. This means that such experiments are more difficult to repeat and are inherently less reliable.
Reliability is a necessary ingredient for determining the overall validity of a scientific experiment and enhancing the strength of the results. Debate between social and pure scientists, concerning reliability, is robust and ongoing. Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method.
For example, there must have been randomization of the sample groups and appropriate care and diligence shown in the allocation of controls. Internal validity dictates how an experimental design is structured and encompasses all of the steps of the scientific research method. Even if your results are great, sloppy and inconsistent design will compromise your integrity in the eyes of the scientific community. Internal validity and reliability are at the core of any experimental design.
External validity is the process of examining the results and questioning whether there are any other possible causal relationships. Control groups and randomization will lessen external validity problems but no method can be completely successful. This is why the statistical proofs of a hypothesis called significant , not absolute truth.
Any scientific research design only puts forward a possible cause for the studied effect. There is always the chance that another unknown factor contributed to the results and findings.
This extraneous causal relationship may become more apparent, as techniques are refined and honed. If you have constructed your experiment to contain validity and reliability then the scientific community is more likely to accept your findings.
Eliminating other potential causal relationships, by using controls and duplicate samples, is the best way to ensure that your results stand up to rigorous questioning. This measure is reliable, but no valid that is, it's consistent but wrong. The second, shows hits that are randomly spread across the target. You seldom hit the center of the target but, on average, you are getting the right answer for the group but not very well for individuals.
In this case, you get a valid group estimate, but you are inconsistent. Here, you can clearly see that reliability is directly related to the variability of your measure. The third scenario shows a case where your hits are spread across the target and you are consistently missing the center. Your measure in this case is neither reliable nor valid. Finally, we see the "Robin Hood" scenario -- you consistently hit the center of the target.
Your measure is both reliable and valid I bet you never thought of Robin Hood in those terms before. Another way we can think about the relationship between reliability and validity is shown in the figure below. Here, we set up a 2x2 table. The columns of the table indicate whether you are trying to measure the same or different concepts.
The rows show whether you are using the same or different methods of measurement. Imagine that we have two concepts we would like to measure, student verbal and math ability. Furthermore, imagine that we can measure each of these in two ways.
Second, we can ask the student's classroom teacher to give us a rating of the student's ability based on their own classroom observation. The first cell on the upper left shows the comparison of the verbal written test score with the verbal written test score.
But how can we compare the same measure with itself? We could do this by estimating the reliability of the written test through a test-retest correlation, parallel forms, or an internal consistency measure See Types of Reliability. What we are estimating in this cell is the reliability of the measure. The cell on the lower left shows a comparison of the verbal written measure with the verbal teacher observation rating.
Because we are trying to measure the same concept, we are looking at convergent validity See Measurement Validity Types.
Issues of research reliability and validity need to be addressed in methodology chapter in a concise manner. Reliability refers to the extent to which the same answers can be obtained using the same instruments more than one time. In simple terms, if your research is associated with high levels of.
Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure.
Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method. For example, there must have been randomization of the sample groups and appropriate care and diligence shown in . Different methods vary with regard to these two aspects of validity. Experiments, because they tend to be structured and controlled, are often high on internal validity. In contrast, observational research may have high external validity (generalizability) because it has taken place in the real world. Relationship between reliability.
Reliability is consistency across time (test-retest reliability), across items (internal consistency), and across researchers (interrater reliability). Validity is the extent to which the scores actually represent the variable they are intended to. Reliability and validity, jointly called the “psychometric properties” of measurement scales, are the yardsticks against which the adequacy and accuracy of our measurement procedures are evaluated in scientific research.