1.4 Experimental Design and Ethics
3 min read•june 25, 2024
is crucial for establishing cause-and-effect relationships in research. It involves manipulating independent variables, measuring dependent variables, and using to create comparable groups. These elements help isolate the effects of and minimize confounding factors.
Ethical considerations are paramount in experimental design. techniques, such as and studies, reduce and enhance result reliability. , , and minimizing harm are also essential ethical principles that protect participants and maintain research integrity.
Experimental Design
Components of randomized experiments
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- Randomized experiments establish cause-and-effect relationships between variables by manipulating the () and measuring changes in the ()
- is controlled by the researcher hypothesized to cause changes in the (drug dosage)
- Dependent variable changes in response to the independent variable and is measured to determine the effect (blood pressure)
- Treatments are different levels or conditions of the independent variable administered to experimental units (high dose, low dose, )
- Experimental units are subjects or objects to which treatments are applied can be individuals, groups, or objects (patients, petri dishes)
- of experimental units to treatment groups helps ensure similarity in all aspects except for the treatment received (coin flip, random number generator)
- of the experiment with multiple trials or groups increases the reliability of results
Random assignment and control groups
- Random assignment distributes potential evenly across treatment groups reducing the likelihood that differences in the dependent variable are due to factors other than the independent variable (age, gender)
- Control groups do not receive the treatment or receive a standard treatment serving as a baseline for comparison with treatment groups (sugar pill)
- Control groups help isolate the effect of the independent variable by controlling for other factors (natural recovery, regression to the mean)
- Random assignment and control groups minimize the impact of confounding variables, increase , and allow for stronger causal inferences about the relationship between variables (smoking causes lung cancer)
Statistical Considerations in Experimental Design
- affects the precision and generalizability of results, with larger samples typically providing more reliable estimates
- is the ability of a study to detect a true effect, influenced by sample size, effect size, and significance level
- refers to the extent to which a study measures what it intends to measure and produces accurate, generalizable results
- Research protocols outline the specific procedures, methods, and analyses to be used in an experiment, ensuring consistency and reproducibility
Ethics in Experimental Design
Blinding in experimental design
- Blinding conceals information about treatment assignment from participants, researchers, or both to minimize bias and the influence of expectations on results
- Single-blind: Participants are unaware of their (patient does not know if they received the drug or )
- Double-blind: Both participants and researchers directly involved in the experiment are unaware of treatment assignments (neither patient nor doctor knows who received the drug or placebo)
- Blinding helps minimize the where participants experience a perceived improvement due to their belief in the treatment, even if the treatment is inactive (sugar pill reduces pain)
- Blinding prevents participants' expectations from influencing their responses or behavior ensuring that observed effects are due to the treatment itself rather than psychological factors ()
- Blinding enhances the reliability and validity of experimental results by reducing bias (, )
- Other ethical considerations in experimental design include:
- Informed consent: Participants should be fully informed about the experiment and voluntarily agree to participate (risks, benefits, purpose)
- Confidentiality: Participants' personal information and data should be kept confidential and secure (anonymized data, encrypted files)
- Minimizing harm: Experiments should be designed to minimize potential risks or harm to participants (safety monitoring, emergency protocols)
Key Terms to Review (59)
ANOVA: ANOVA, or Analysis of Variance, is a statistical method used to compare the means of two or more groups or conditions to determine if there are any significant differences between them. It is a powerful tool for experimental design and hypothesis testing.
Belmont Report: The Belmont Report is a foundational document that outlines the ethical principles and guidelines for the protection of human subjects in research. It was developed in response to unethical research practices that came to light in the past, and serves as a cornerstone for the ethical conduct of research involving human participants.
Bias: Bias refers to the systematic tendency to deviate from an accurate or impartial assessment or judgment, often due to personal, emotional, or situational factors. It can lead to distortions or errors in experimental design, data collection, and interpretation, with significant implications for the validity and reliability of research findings.
Blinding: Blinding is a technique used in experimental design and research studies to minimize bias and ensure the validity of results. It involves concealing the identity or treatment assignment of participants from one or more parties involved in the study, such as the participants themselves, the researchers, or the data analysts.
Chi-Square: Chi-square (χ²) is a statistical test used to determine the likelihood that the difference between observed and expected frequencies in a dataset is due to chance. It is a fundamental tool in experimental design and ethics, as it helps researchers evaluate the significance of their findings and ensure the validity of their studies.
Cluster Sampling: Cluster sampling is a probability sampling technique where the entire population is divided into groups or clusters, and a random sample of these clusters is selected to represent the whole population. This method is often used when the population is geographically dispersed or when a complete list of all individual members is not available.
Confidence interval: A confidence interval is a range of values, derived from sample data, that is likely to contain the value of an unknown population parameter. It provides an estimated range that is believed to contain the parameter with a certain level of confidence.
Confidence Interval: A confidence interval is a range of values that is likely to contain an unknown population parameter, such as a mean or proportion, with a specified level of confidence. It provides a measure of the precision of an estimate and allows researchers to make inferences about the population based on a sample.
Confidentiality: Confidentiality is the ethical principle of ensuring that sensitive information is only accessible to authorized individuals or entities. It is a critical aspect of various fields, including experimental design and research ethics, where it helps protect the privacy and rights of research participants.
Confirmation Bias: Confirmation bias is the tendency for people to seek out, interpret, and prioritize information that confirms their existing beliefs or hypotheses, while ignoring or dismissing information that contradicts them. This cognitive bias can significantly impact decision-making, experimental design, and ethical considerations in research and analysis.
Confounding Variables: Confounding variables are factors in a study or experiment that are not the primary focus of the research, but can influence the relationship between the independent and dependent variables. These variables can lead to incorrect conclusions if not properly identified and controlled for.
Control group: A control group is a group in an experiment that does not receive the treatment or intervention being tested. It serves as a baseline to compare the effects of the treatment on other groups.
Control Group: A control group is a crucial component in experimental design, serving as a benchmark to compare the effects of an intervention or treatment. It is a group of participants that does not receive the experimental treatment, allowing researchers to isolate the impact of the independent variable being studied.
Convenience Sampling: Convenience sampling is a non-probability sampling technique where the sample is selected based on its accessibility and proximity to the researcher. This method is often used in exploratory research or when resources are limited, as it provides a quick and easy way to collect data from a readily available population.
Crossover Design: A crossover design is an experimental design in which participants receive multiple treatments or interventions, with each participant acting as their own control. This design allows for the comparison of different treatments within the same individual, reducing the impact of individual differences and potentially increasing the statistical power of the study.
Dependent variable: A dependent variable is the outcome or response that researchers measure in an experiment to see if it changes due to manipulation of the independent variable. It is dependent on the conditions set by the experimenter.
Dependent Variable: The dependent variable is the variable that is being measured or observed in a study. It is the outcome or response that is influenced or determined by the independent variable(s). The dependent variable is the variable of primary interest in a research study.
Double-Blind: Double-blind is a research methodology where neither the participants nor the researchers know which treatment or intervention each participant is receiving. This approach is used to minimize bias and ensure the validity of the study's findings in the context of experimental design and ethics.
Double-blind experiment: A double-blind experiment is a study where neither the participants nor the experimenters know who is receiving a particular treatment. This method helps to eliminate bias and ensure more reliable results.
Ethics: Ethics refers to the moral principles that guide an individual's or organization's behavior and decision-making. It involves the study of what is right and wrong, and the application of those principles in various contexts.
In the context of experimental design and research, ethics is a critical consideration that ensures the protection of human participants and the integrity of the research process.
Experimental Design: Experimental design refers to the systematic process of planning and structuring experiments to ensure valid and reliable results. It involves determining the appropriate methods, variables, and procedures to effectively investigate a research question or hypothesis.
Explanatory variable: An explanatory variable is an independent variable in an experiment or study that is manipulated to observe its effect on a dependent variable. It helps in identifying causal relationships by influencing changes in the response variable.
Hawthorne Effect: The Hawthorne effect is a phenomenon where individuals modify their behavior or performance in response to being observed or part of an experiment. It suggests that people may change their actions simply because they know they are being studied, rather than due to the specific conditions of the study.
Helsinki Declaration: The Helsinki Declaration is a set of ethical principles for medical research involving human subjects, developed by the World Medical Association. It serves as a guide for physicians and other participants in medical research to ensure the rights, safety, and well-being of research participants.
Independent variable: An independent variable is a factor that is manipulated or controlled in an experiment to observe its effect on the dependent variable. It is the presumed cause in a cause-and-effect relationship.
Independent Variable: The independent variable is the variable that is manipulated or changed in an experiment or study to observe its effect on the dependent variable. It is the factor that the researcher has control over and hypothesizes will influence the outcome of the study.
Informed Consent: Informed consent is the process of obtaining voluntary agreement from an individual to participate in a research study or medical procedure, after the individual has been provided with all the necessary information to make an informed decision.
Internal validity: Internal validity refers to the extent to which a study accurately establishes a causal relationship between variables, ensuring that the observed effects are due to the manipulation of the independent variable and not other confounding factors. High internal validity means that the study's design effectively isolates the treatment effect, allowing researchers to draw credible conclusions about cause and effect. This is crucial in experimental design, where researchers must control for external variables to support ethical research practices.
Lurking variables: Lurking variables are variables that are not included as explanatory or response variables in a study but can influence the interpretation of relationships between these variables. They can create false associations or mask true relationships.
Null hypothesis: The null hypothesis is a statement that there is no effect or no difference, and it serves as the default or starting assumption in hypothesis testing. It is denoted as $H_0$ and is tested against the alternative hypothesis.
Null Hypothesis: The null hypothesis is a statistical hypothesis that proposes that there is no significant difference or relationship between two or more variables in a given population. It serves as the starting point for hypothesis testing and is the hypothesis that the researcher aims to either support or reject based on the observed data.
Observational Study: An observational study is a type of research design where the investigator observes and records the behavior or characteristics of subjects without intervening or manipulating the variables. This approach is commonly used to study naturally occurring phenomena and relationships between variables in real-world settings.
Observer Bias: Observer bias is a type of systematic error that occurs when the observations or measurements made by a researcher are influenced by their own preconceptions, beliefs, or expectations. This can lead to distorted or inaccurate data, which can compromise the validity and reliability of a study's findings.
P-value: The p-value is a statistical measure that indicates the probability of obtaining a test statistic at least as extreme as the one observed, given that the null hypothesis is true. It is a critical component in hypothesis testing, as it helps determine the statistical significance of the findings and guide decision-making.
Placebo: A placebo is an inert substance or treatment given to a control group in an experiment. It is used to eliminate the effects of participants' expectations on the results.
Placebo: A placebo is an inert substance or treatment that has no direct pharmacological or therapeutic effect, but is used in clinical trials to compare the effects of a real medical treatment. It is often used as a control in experiments to isolate the impact of the actual intervention being tested.
Placebo Effect: The placebo effect refers to the phenomenon where an individual experiences an improvement in their condition or symptoms simply from the expectation of receiving a beneficial treatment, even when the treatment itself has no active therapeutic components. This effect is a key consideration in experimental design and medical ethics.
R: R is a statistical programming language and software environment used for data analysis, visualization, and statistical computing. It is widely used in various fields, including business, academia, and research, due to its powerful capabilities and versatility.
Random assignment: Random assignment is the process of assigning participants to different groups in an experiment using randomization techniques. This ensures that each participant has an equal chance of being placed in any group, eliminating selection bias.
Random Assignment: Random assignment is a fundamental principle in experimental design where participants are randomly allocated to different treatment or control groups. This ensures that any observed differences between the groups can be attributed to the intervention being tested rather than other confounding factors.
Random sampling: Random sampling is a method of selecting a subset of individuals from a population in which each member has an equal chance of being chosen. This technique aims to produce representative and unbiased samples.
Random Sampling: Random sampling is a method of selecting a subset of individuals from a larger population, where each member of the population has an equal chance of being chosen. This technique is widely used in statistical analysis and experimental design to ensure unbiased and representative data collection.
Randomized Controlled Trial: A randomized controlled trial (RCT) is a type of experimental study design in which participants are randomly assigned to either a treatment group or a control group. This allows researchers to assess the effectiveness of an intervention by comparing the outcomes between the two groups, while controlling for potential confounding factors.
Replication: Replication is the process of repeating an experiment or study multiple times to ensure the reliability and validity of the results. It is a fundamental concept in experimental design and research ethics, as it allows researchers to confirm their findings and address potential sources of bias or error.
Research Protocol: A research protocol is a detailed plan that outlines the objectives, methodology, and ethical considerations of a research study. It serves as a roadmap for conducting the research in a systematic and rigorous manner, ensuring the study's validity, reliability, and adherence to ethical principles.
Response Bias: Response bias refers to the tendency of survey or study participants to respond in a way that does not accurately reflect their true thoughts, feelings, or behaviors. This can occur due to various psychological and social factors, leading to systematic errors in the data collected.
Response variable: A response variable is the outcome or dependent variable that researchers measure in an experiment. It is expected to change as a result of variations in the explanatory or independent variables.
Sample Size: Sample size refers to the number of observations or data points collected in a statistical study or experiment. It is a crucial factor that determines the reliability and precision of the conclusions drawn from the data.
SAS: SAS, or Statistical Analysis System, is a software suite developed by SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It is widely used in various fields, including experimental design and research ethics, to analyze data, generate reports, and support decision-making processes.
Selection Bias: Selection bias is a type of systematic error that occurs when the sample selected for a study or experiment is not representative of the population of interest. This can lead to inaccurate or biased results, as the data collected may not accurately reflect the true characteristics of the population.
Single-blind: Single-blind is an experimental design where the participants in a study are unaware of the treatment they are receiving, but the researchers conducting the study know which participants are receiving the different treatments. This type of design helps to minimize potential bias and improve the validity of the study's findings.
SPSS: SPSS (Statistical Package for the Social Sciences) is a powerful software application used for statistical analysis, data management, and data visualization. It is widely used in various fields, including business, social sciences, and research, to analyze and interpret data effectively.
Statistical Power: Statistical power refers to the likelihood that a statistical test will detect an effect or difference if it truly exists in the population. It is a crucial concept in experimental design and hypothesis testing, as it helps researchers determine the appropriate sample size and evaluate the strength of their findings.
Stratified Sampling: Stratified sampling is a probability sampling technique where the population is divided into distinct subgroups or strata, and samples are randomly selected from each stratum in proportion to the stratum's size. This method ensures that the sample is representative of the overall population, allowing for more precise estimates and inferences.
Survey: A survey is a method of collecting information from a sample of individuals to gain insights about a larger population. It involves gathering data through various techniques, such as questionnaires, interviews, or observations, to understand people's opinions, behaviors, or characteristics.
T-test: The t-test is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups or populations. It is commonly used in various contexts, including experimental design, hypothesis testing, and regression analysis.
Treatment Group: The treatment group is a fundamental concept in experimental design, referring to the group of participants or subjects that receive the intervention or treatment being studied. This term is particularly relevant in the context of evaluating the effectiveness of an intervention, as the treatment group is compared to a control group to determine the impact of the treatment.
Treatments: Treatments refer to the different conditions or interventions applied to subjects in an experiment. Each treatment represents a different level or type of factor being studied.
Validity: Validity refers to the extent to which a measurement or study accurately represents the concept it is intended to measure or investigate. It is a critical consideration in both experimental design and research ethics, ensuring that the conclusions drawn from a study are well-founded and meaningful.