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. Check out our quiz-page with tests about:.
Martyn Shuttleworth Oct 20, Although bias in research can never be completely eliminated, it can be drastically reduced by carefully considering factors that have the potential to influence results during both the design and analysis phases of a study. The most common types of bias in research studies are selection biases, measurement biases and intervention biases.
Selection bias also may occur if a study compares a treatment and control group, but they are inherently different. If selection bias is present in a study, it is likely to influence the outcome and conclusions of the study. This can occur due to leading questions, which in some way unduly favor one response over another, or measurement bias may be due to social desirability and the fact that most people like to present themselves in a favorable light, and therefore, will not respond honestly.
When assessing behavioral changes, it is essential to have a baseline or control group for comparison. It is important to evaluate the impact of a program and determine whether the program actually had an impact, or if what happened would have occurred regardless of the implementation of the program.
Such an evaluation could yield useful information to program implementers. But it could not be considered a rigorous evaluation of the effects of the program if there are good reasons to believe that scores might have changed even without the program. For example, many programs and organizations have developed in recent years to make cell phones and mobile technology available to rural areas. A program evaluation could report that a program was able to increase cell phone ownership in a village over a 3-year period.
However, conclusive impact cannot be discerned if the statistics compare the village before and after the program intervention. A credible comparison group is important to determine or prove the full impact of a program or intervention. For example, one could locate other rural areas in the same country that had cell phone usage rates that were comparable to those of the intervention site prior to the start of the program.
After the same specified time period i. It is important to remember that just because a study is valid in one instance it does not mean that it is valid for measuring something else. It is important to ensure that validity and reliability do not get confused.
Reliability is the consistency of results when the experiment is replicated under the same conditions, which is very different to validity. These two evaluations of research studies are independent factors, therefore a study can be reliable without being valid, and vice versa, as demonstrated here this resource also provides more information on types of validity and threats.
However, a good study will be both reliable and valid. So to conclude, validity is very important in a research study to ensure that our results can be used effectively, and variables that may threaten validity should be controlled as much as possible. Validity is possibly the most important aspect of research and if anything is to be achieved it should be relibiltiy and validity or findings are in sense worthless.
This is when subject may choose not to remain in there group and as a result differences between a control group and a treatment group may be a result of who remained in each group opposed to the variables. Subject attrition shows that validity isnt just a concern when arranging a research design but as the experiment progress there are still threats to internal validity that can be overcome during data analysis.
Attrition is manly an issue in longitudinal research reference below in which validity is incredibly difficult to control. The overall message of this comment is that validity issues are an ongoing process with a single research process and can be effected at any point and needs many measures to control. I agree with the above comment! There are many threats to both reliability and validity.
If you add in more information on when each of these situations could be used it would bulk and add to the argument. There needs to be more examples to back up your points also and they seem very bare. As well, try to add a bit more information on reliability into the argument.
The information on the two will help make a very valid point through out your blog instead of just trying to justify validity. Very informative blog, however how can we prevent these threats to validity? A single blind being the participant being unaware of which condition or group they are in and a double blind being when neither participant or researcher being aware of this fact.
Both lessen the expectancy effects of the experimental setting or group. Even things as simple as experimenter bias can become major issues. Really detailed informative blog, well done! You could have maybe included how you would try and stop their being threats to the validity, for example a double experimenter and participant or single just participant blind experiment, where they do not know what condition they are in in the experiment.
Homework for my TA simon:
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.
Internal validity - the instruments or procedures used in the research measured what they were supposed to measure. Example: As part of a stress experiment, people are shown photos of war atrocities. Example: As part of a stress experiment, people are shown photos of war atrocities.
Validity of Research Though it is often assumed that a study’s results are valid or conclusive just because the study is scientific, unfortunately, this is not the case. Researchers who conduct scientific studies are often motivated by external factors, such as the desire to get published, advance their careers, receive funding, or seek certain results. In general, VALIDITY is an indication of how sound your research is. More specifically, validity applies to both the design and the methods of your research. Validity in data collection means that your findings truly represent the phenomenon you are claiming to measure.
Validity: the best available approximation to the truth of a given proposition, inference, or conclusion. The first thing we have to ask is: "validity of what?"When we think about validity in research, most of us think about research components. Don’t confuse this type of validity (often called test validity) with experimental validity, which is composed of internal and external validity. Internal validity indicates how much faith we can have in cause-and-effect statements that come out of our research.