Effective researchers choose techniques that best help them answer their substantive questions.
Threats to validity of Research Design Chong-ho Yu The books by Campbell and StanleyCook and Campbelland Shadish, Cook, and Campbell, are considered seminal works in the field of experimental design. The following write-up is based upon their books with insertion of my examples and updated information.
Problem and Background Experimental method and essay-writing Campbell and Stanley point out that adherence to experimentation dominated the field of education through the s Thorndike era but that this gave way to great pessimism and rejection by the late s.
However, it should be noted that a departure from experimentation to essay writing Thorndike to Gestalt Psychology occurred most often by people already adept at the experimental tradition. Therefore, we must be aware of the past so that we avoid total rejection of any method, and instead take a serious look at the effectiveness and applicability of current and past methods without making false assumptions.
Replication Lack of replicability is one of the major challenges in social science research. After replicating one hundred psychological studies, Open Science Collaboration OSC found that a large portion of the replicated results were not as Research design a content analysis as the original reports in terms of significance p values and magnitude effect sizes.
Nonetheless, the preceding problem is not surprising because usually the initial analysis tends to overfit the model to the data. Needless to say, a theory remains inconclusive when replicated results are unstable and inconsistent.
Multiple experimentation is more typical of science than a one-shot experiment! Experiments really need replication and cross-validation at various times and conditions before the theory can be confirmed with confidence. In the past the only option is to replicate the same experiments over and over.
Nevertheless, today the researcher is allowed to virtually repeat the study using one single sample by resampling. Specifically, many data mining software applications have the features of cross-validation and bootstrap forest. In cross-validation the data set is partitioned into many subsets and then multiple analyses are run.
In each run the model is refined by previous "training" and thus the end result is considered a product of replicated experiments. In a similar vein, bootstrap forest randomly selects observations from the data and replicate the analysis many times.
The conclusion is based on the convergence of these diverse results. Cumulative wisdom An interesting point made is that experiments which produce or support opposing theories against each other probably will not have clear cut outcomes. In fact, different researchers might observe something valid that represents a part of the truth.
Adopting experimentation in education should not imply advocating a position incompatible with traditional wisdom.
Rather, experimentation may be seen as a process of refining or enhancing this wisdom. Therefore, cumulative wisdom and scientific findings need not be opposing forces. Factors Jeopardizing Internal and External Validity Please note that validity discussed here is in the context of experimental design, not in the context of measurement.
Efficacy and effectiveness In medical studies, usually efficacy studies in experimental settings are conducted to address the issue of internal validity whereas effectiveness studies in naturalistic settings the "real" world are employed to examine the external validity of the claim.
Usually patients in experimentation are highly selected whereas patients in the real world are not. For example, subjects in clinical trials usually have just the illness under study.
Patients who have multiple health conditions are excluded from the study because those uncontrolled variables could muddle the research results.
However, in the real world it is not unusual that patients have multiple illnesses.
As a result, a drug that could work well in a lab setting may fail in the real world. Thus, medical researchers must take both internal validity and external validity into account while testing the goodness of a treatment. On one hand, efficacy studies aim to answer this question: Does the treatment work in a close experimental environment?
On the other hand, effectiveness studies attempt to address a different issue: Does the treatment work in the real-life situation? Interestingly enough, the US drug approval and monitoring processes seem to compartmentalize efficacy and effectiveness. Rigorous experiments and hard data are required to gain the FDA's approval.
But after the drugs are on the market, it takes other agencies to monitor the effectiveness of the drugs.This paper describes the research process – from planning to presentation, with the emphasis on credibility throughout the whole process – when the methodology of qualitative content analysis is chosen in a qualitative study.
Qualitative content analysis as described in published literature shows conflicting opinions and unsolved issues regarding meaning and use of concepts, procedures and interpretation.
Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Researchers quantify and analyze . A unique attribute of qualitative content analysis is the focus on a continual process of revising and developing meanings in the data based on new discoveries.
Unlike quantitative content analysts who set their coding scheme early in the research process — typically modifying it only slightly or not at all during data collection — qualitative researchers methodically and frequently.
Making interpretations from a content analysis are tricky things, esp., when conducting a “primary content analysis” when the content being analyzed is derived from non-research-related, pre-existing sources such as newspapers, blog posts, Hollywood films, YouTube videos, television broadcasts, and the like.
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