A conceptual model differs from an exploratory model because it is used in research work to summarize achievable strategies or else to organize a chosen method to a plan or idea. In contrast, an exploratory model differs from a conceptual model because it is used to describe as well as explain a scenario or the manner in which things operate. Conceptual models are more of theories that try to link to all parts of a research study such as a problem statement, objectives, methodologies and data collection and management (Kane and Radosevich, 30). In contrast, exploratory models do not explain and describe the realities of an event.
However, the descriptions must correspond reasonably to an adequate segment of all the information, interpretation and hypothetical conditions known about the event. Conceptual models can work as maps that provide logic to experimental investigations. In comparison, exploratory models present new knowledge that may contradict that which is already known (Kane and Radosevich, 29).
How to Avoid Selection Biases in Research?
In epidemiological research, one way to minimize selection bias is through the selection of the research participants. During the selection process, bias can be avoided by clearly identifying the study population and choosing the appropriate comparison groups. For instance, in a cohort type of study, the unexposed along with the exposed must be the same, except for the exposure. In retrospective cohort studies, biases can be avoided by selecting the comparison groups without knowledge of the sickness status. In case control studies, the controls should reveal the exposure in the populace that produced the cases. On the other hand, the controls must be chosen separately of the exposure condition. Randomization minimizes bias in interventional epidemiological studies (Kane and Radosevich, 31).
Internal and External Validity of a Study
Internal Validity
Internal validity in research focuses on the “true” origin of the results that an investigator observes in his or her study. A strong internal validity implies that the study not only has consistent measures of dependent as well as independent variables but also a strong validation that causally connects the study’s independent variables to conditional variables (Kane and Radosevich, 25). Internal validity allows an investigator to exclude superfluous variables, or substitute, frequently unforeseen, and causes for the study’s conditional variables (Kane and Radosevich, 30).
External Validity
External validity focuses on the generalizability of a study to the public and other similar situations. For researchers to have convincing external validity in their research studies, they require an odds sample of participants or subjects drawn using randomization technique from a defined study population such as all registered nurses in John Hopkins Hospital. When a study has a strong external validity, its outcomes can be generalized to other populations as well as conditions with certainty. Public outlook assessments usually place great emphasis on describing the populace of interest in addition to drawing high-quality study samples from that populace (Kane and Radosevich, 28).
Potential Biases in Research
Potentials biases refer to all types of bias that are likely to occur in a research study (Kane and Radosevich, 32). They include biases like sampling bias, selection bias, observation bias, measurement bias and exposure bias, instrument bias, recall bias, timing bias and withdraw bias among other. The presence of potential biases in research does not mean that the studies are invalid, but it means their accuracy is questionable.
Works Cited
Kane, Robert L. and David M. Radosevich. Conducting health outcomes research. Sudbury, MA: Jones and Bartlett Publishers, 2011. Print.