receive close scrutiny of the original study as well as the replicated study.
The term replication is closely associated with the terms representation, transparency, reliability, and validity.
With this definition of terms and acceptance that these terms apply across the many different areas associated with research in the biological sciences, consider a few of the most frequently used research designs for biostatistics.
Some research designs are simple and are for exploratory purposes only. Some research designs are organized to investigate research questions that are complex and address multifaceted issues.
1.9.1 Case Study and Clinical Trial
The Case Study and Clinical Trial is a research process that is often associated with early-on or preliminary investigations, often when the number of partici- pants (e.g., subjects, patients, plots, etc.) is limited in number and representa- tion. Then, based on results, the case study or clinical trial may be expanded in either scope and complexity, sample size, and greater representation of the sample to the population. It is common for a case study or clinical trial to fo- cus on investigations involving prototype devices, feed or drugs in the product development pipeline, or exploratory protocols.
1.9.2 Pretest–Posttest for One Group
The Pretest–Posttest for One Group is a fairly simple approach to the research process where one identified group of subjects receives a pretest for a specific area of interest, a treatment of some type is applied, and a posttest is used as an ending activity. It is assumed, to some degree of acceptance, that the change between pretest and posttest, if any, is attributed to the treatment.
This research process is often viewed as an early-on research endeavor that is used for exploratory purposes, to look for general trends, or to practice with different treatment and measurement protocols.
1.9.3 Pretest–Posttest for Control Group
The Pretest–Posttest for Control Group is a somewhat more ambitious ap- proach, where there are typically two groups, a control group and an experi- mental group. The experimental group receives a pretest, a treatment, and a posttest. The control group receives a pretest, no treatment, and a posttest.
Change between pretest and posttest can now be differentiated between, and among, those subjects who received the treatment and those who did not re- ceive the treatment, allowing for some degree of interpretation of impact due to the treatment.
1.9.4 Posttest Only for Control Group
The Posttest Only for Control Group is a research process where there are typically two groups, a control group and an experimental group. There is no administration of a pretest. The experimental group receives a treatment and a posttest. The control group receives a posttest, only. This approach is used to address the possibility that the pretest, as an experience, possibly impacts posttest results. Differences between the control group and the experimental group, if any, are attributed to the treatment.
1.9.5 Fixed Group Comparative Analysis of a Single Factor
The Fixed Group Comparative Analysis of a Single Factor is a research process where one factor (e.g., Breed: Holstein or Guernsey; Region: North, South, East, or West; Soil Type: Clay, Sand, or Silt) and differences between and among breakout groups is the focus of inquiry for a defined measurement (e.g., dairy cattle milk production per lactation, Labrador Retrievers and weight at weaning, bushel per acre yields of grain sorghum plots, etc.).
1.9.6 Factorial Data Organization of Multiple Independent Vari- ables
The Factorial Data Organization of Multiple Independent Variables is a research process where there are two or more factors (e.g., Gender and Race, Breed and Added Mineral Supplements, etc.) and one or more measurable variables as- sociated with the subjects (e.g., Systolic Blood Pressure, dressing percentage after slaughter, etc.). Factorial designs are used in an attempt to promote the efficient use of resources, in that multiple analyses are possible as opposed to singular-by-singular attempts at experimentation. A factorial approach to the research process allows not only for inquiries at the univariate level of compar- ison, but it is now possible to investigate the influence of multiple factors and possible interactions between and among the factors.
1.9.6.1 Goodness of Fit (e.g., Chi-Square)
With a Goodness of Fit or Chi-Square (e.g.,Χ2) approach to factorial inquiries, it is possible to determine if sample data are in parity with the expected distri- bution from a certain population. A Goodness of Fit research approach might include something as simple as investigations into Rhode Island Red poultry flocks of approximately equal size and the monthly production of eggs of each egg size category (e.g., Peewee, Small, Medium, Large, Extra Large, Jumbo) by confinement type (e.g., battery cage confinement, cage-free with access to fenced-in pasture, cage-free but contained inside a structure). Are the observed
counts (e.g., number of eggs of each egg size by confinement type) in parity with expected counts, with expectations often based on a theoretical framework or set of assumptions?
1.9.6.2 Comparison of Group Means (e.g., Analysis of Variance) With a Comparison of Group Means or Analysis of Variance (ANOVA), it is possible to compare group means using a mean comparison procedure. This concept is addressed in later lessons, but for now it is best to merely mention that Tukey’s HSD (Honestly Significant Difference) test and Scheffé’s post-hoc test are used to determine if differences in means between and among breakout groups are statistically significant at a declared level of significance (e.g., 0.05, 0.01, 0.001) or if the differences are due only to chance.
1.9.7 Correlation, Association, Regression, Likelihood, and Predic- tion
The Correlation, Association, Regression, Likelihood, and Prediction research process is based on the common phrase Past behavior is the best predictor of future behavior. In simple form, Variable X is compared to Variable Y to see if there is any degree of correlation (e.g., association) between the two. If an association is found, then ultimately constructs such as regression, likelihood, probability, etc., are used to build a prediction equation. By knowing a value for X, it is possible to predict with some degree of assurance the value for Y.
The important thing to recall with correlation designs is that there may be an association between Variable X and Variable Y, but that does not in any way suggest that Variable X caused Variable Y or that Variable Y caused Variable X. Correlation does not imply or suggest causation—only association.
Whether a selected design is simple or complex, give special attention to the notion that good research in the biological sciences promotes the discovery of outcomes that should ultimately improve the human condition. Following along with that thought, recall that there are many other resources that go into in- tricate depth on research design and the type of questions addressed by those designs. Also, consider how the efficient use of resources and unavoidable im- pediments often demand real-world research design variations that may impact outcomes, in contrast to theoretical research designs where the influence of ex- traneous phenomena are controlled. With this concern, always consider the need for transparency and how transparency promotes replication, and from replication—acceptance of results by the professional community.