Study Design in Animal Models of Stroke
Yanlin Wang-Fischer in Manual of Stroke Models in Rats, 2008
Experimental design is the process of organizing the experiment properly to ensure that the right type of data is available to answer the questions of interest as clearly and efficiently as possible. The specific questions that the experiment is intended to answer must be clearly identified before carrying out the experiment. We should also attempt to identify known or expected sources of variability in the experimental units since one of the main aims of an experiment is to reduce the effect of these sources of variability on the answers to questions of interest. That is, we design the experiment to improve the precision of our answers. In some ways, the design of a study is more important than the analysis. A poorly analyzed study can be reanalyzed, but a badly designed study can never be retrieved. Consideration of design is also important because the design of a study will govern how the data are to be analyzed. Experimental design involves randomization of animals or samples, replication, and control of bias by blinded design.
Quantitative approaches in sport-related concussion research
Gordon A. Bloom, Jeffrey G. Caron in Psychological Aspects of Sport-Related Concussions, 2019
Experiments are the only reliable method available for determining cause-effect relationships between two or more variables (Reis & Judd, 2000). An experimental design involves measuring a variable of interest (dependent variable), some sort of manipulation to a different variable (independent variable), and then examining changes in the dependent variable following the manipulation of the independent variable, with the objective of determining cause and effect. Although experimental designs require multiple assessment points (like cohort designs), what makes them different is the presence of a manipulation. A manipulation can include a specific intervention like a training program or treatment technique but can also include the onset of a new condition, like a concussion. The key requirements for a true experimental research design, which are researchers’ only method for inferring causal relationships, are the presence of a control group (i.e., a group of individuals who participate in the study but do not receive the manipulation) and randomization (i.e., where every participant has an equal opportunity to be in the experimental or control group).
Quantitative Methods for Analyzing Experimental Studies in Patient Ergonomics Research
Richard J. Holden, Rupa S. Valdez in The Patient Factor, 2021
The experimental design, structure, and measurement type predominantly determine the statistical techniques that can be used to analyze the data collected from patient ergonomics studies. A sound experimental design is required to enable patient ergonomics researchers to gather interpretable comparisons of the effects of manipulated variables. At the very least, a good experimental design consists of identified independent variables and their respective states that will be manipulated or held constant, associated dependent variables that measure the outcomes of the experiment, characteristics and the number of participants to be used, and a scheme for the replication of unique states of the manipulated variables. There are two common methods of collecting data from experimental studies: the between-subjects, or independent, design and the within-subject, or repeated measures, design. The former involves manipulation of the independent variable using different groups of participants, and the latter involves manipulation of the independent variable with the same group. Our case study is an example of a within-subject experimental design. The independent variable in our study is the type of FHx data collection interface. It is tested at two levels—conventional and conversational.
Characterizing disease-associated changes in post-translational modifications by mass spectrometry
Published in Expert Review of Proteomics, 2018
Camilla Thygesen, Inga Boll, Bente Finsen, Maciej Modzel, Martin R. Larsen
PTMs generate the great diversity and complexity of proteoforms necessary for organisms to function, but they can also initiate cell processes leading to diseases. The study of PTMs still presents great technical challenges and LC-MS/MS has become a major player in solving these. To study PTMs by LC-MS/MS efficiently, it requires highly complex workflows, with a high possibility of introducing technical bias [123]. A recent study tried to assess this issue by analyzing the same samples in different laboratories employing identical workflows and instruments. What they found was that results were lacking in repeatability as well as reproducibility [124]. Thus the experimental design and sample handling is crucial for obtaining reliable results. One way of decreasing technical variability in LC-MS/MS is through multiplexing of samples by chemical or metabolic labeling. This enables researchers to combine samples at an early time point, thereby reducing sample-to-sample and run-to-run variations as well as instrument time. Especially chemical labels for relative quantitation such as iTRAQ and TMTTM are well suited, having also clinical potential in comparison to metabolic labeling. The drawback of quantitative workflows using chemical labels is the limitation on the number of available labels and lower identification rates [125].
Fabrication, assessment, and optimization of alendronate sodium nanoemulsion-based injectable in-situ gel formulation for management of osteoporosis
Published in Drug Delivery, 2023
Wesam H. Abdulaal, Khaled M. Hosny, Nabil A. Alhakamy, Rana B. Bakhaidar, Yasir Almuhanna, Fahad Y. Sabei, Mohammed Alissa, Mohammed Majrashi, Jawaher Abdullah Alamoudi, Mohannad S. Hazzazi, Ayman Jafer, Rasha A. Khallaf
The main objectives of a statistical experimental design are to define interactions between variables, obtain the greatest amount of information from the fewest trials possible, and identify the causes of experimental errors (Alkhalidi et al., 2018). Such designs also require accurate planning and adherence to statistical formulas, which is a benefit. The accuracy of the study aims and investigations that must be carried out to achieve those goals must be pursued rigorously by investigators. The optimal composition of a formulation and the methods for developing it on a wide scale can also be predicted by the design (Hosny et al., 2020). In a way, experimental designs are economical because they frequently present the best option for the targeted formulation (Salem et al., 2020). The chief objective of this research was to fabricate an innovative injectable oily in-situ formulation loaded with ALS to provide a prolonged-release intramuscular depot of the drug, which would be given four times a year. Such a paradigm is expected to raise ALS bioavailability and enhance patient adherence.
Procedural Integrity Reporting in the Journal of Organizational Behavior Management (2000–2020)
Published in Journal of Organizational Behavior Management, 2022
Daniel Cymbal, David A. Wilder, Nelmar Cruz, Grant Ingraham, Mary Llinas, Ronald Clark, Marissa Kamlowsky
To meet the inclusion criteria, studies were required have been experimental or quasi-experimental in nature. We defined experimental studies as those that included one or more participants with active manipulation of an independent variable. Quasi-experimental studies were those that included some form of experimental design (i.e., systematically manipulated an independent variable) but violated some tenant of execution (e.g., no random assignment for group designs; lack of demonstrated experimental control in single subject design). Thus, these requirements excluded certain forms of empirical evaluation (e.g., correlational designs; meta-analyses) as well as discussion/conceptual articles, comments, book reviews, and Editor’s notes. Based on previous reviews of procedural integrity (e.g., Falakfarsa et al., 2021), we counted studies with multiple experiments (i.e., separate Method, Results, and/or Discussion sections) as separate, individual studies.
Related Knowledge Centers
- Naturalistic Observation
- Power of A Test
- Sensitivity & Specificity
- Statistical Inference
- Experiment
- Quasi-Experiment
- Dependent & Independent Variables
- Controlling For A Variable
- Validity
- Reliability
- Power of A Test
- Sensitivity & Specificity