Sampling and Experimental Control Goals of clinical research is to make generalizations beyond the individual studied to others with similar conditions.
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Sampling and Experimental Control Goals of clinical research is to make generalizations beyond the individual studied to others with similar conditions or characteristics Populations larger group to which research results are generalized
Sampling and Experimental Control Sample Subgroup of the population is the reference group for drawing conclusions about the population. You must define your population’s inclusion and exclusion criteria. You assume that the response of sample members will be representative of how the population members would respond in similar circumstances.
Sampling Bias When individual selected overrepresents or underrepresents the attributes that you are studying First Step in Planning a Study 1. Target Population - overall group of people to which the researcher intends to generalize findings 2. Research must devise a plan for subject selection
Target Population Accessible Population Subject Selection
Sampling Techniques Random selection - every unit in the population has an equal chance of being selected. Try to limit sampling error (or variation). Should be unbiased. Random sample - decreases sampling error
Sampling Techniques 1. Simple random sampling - using table of random numbers 2.Systematic sampling 3. Stratified random sampling 4. Disproportional sampling - male vs.. female. More males so females receive a proportionately larger mathematical representation in the analysis of scores than males. 5. Cluster sampling - involves successive random sampling of a series of units in the population
Nonpropability Samples - are created when samples are chosen on same basis other than random selection. 1. Convenience sampling 2. Quota sampling 3. Purposive sampling 4. Snowball sampling
Experimental Control Experiments are based on a logical structure of design. Purpose is to support a case and effect relationship between the IV (condition) and DV (observed) Essence of a experiment lies in the researcher’s ability to manipulate and control variables and measurement.
Experimental Control Extraneous Variables (Nuisance or intervening variables) External factors - emerge from the environment and the experimental situation. Intrinsic factors - represent personal characteristics of the subjects of the study When extraneous variables are not controlled, they exert a confounding influence on the independent variable.
Characteristics of Experiments 1. Manipulation of Variables - manipulates the level of the IV 2. Random Assignment - each subject has an equal chance of being assigned to any group. 3. Use of control groups (helps to rule out extraneous effects) Can’t always be done therefore you compare new treatment vs. old treatment.
Characteristics of Experiments 4. The researchers protocol - researcher determines all factors most likely to contaminate the IV and attempts to minimize their effects as much as possible. 5. Double Blinding - neither of the subjects nor investigator are aware of the identity of the treatment groups until after the data are collected single Blinding - only the investigator or measurement team is blinded. Blinding increases the validity of the conclusions
Design Strategies for Controlling Intersubject Differences 1. Selection of Homogeneous Subjects - chose subjects who have the same characteristics of the extraneous variable. 2. Blocking - build extraneous variable into the design by using them as a dependent variables creating block of subjects that are homogeneous for the different levels of the variable. 3. Matching - match subjects on specific characteristics across groups.
Design Strategies for Controlling Intersubject Differences 4. Using Subjects as Their Own Control - Expose subjects to all levels of the IV, creating a RM design 5. Analysis of Covariance - select an extraneous variable as a covariate adjusting scores statistically to control for difference of the extraneous variables
Threats to Validity 1. Is there a relationship between IV and the DV? 2. Given that a relationship does exist, is there evidence that one causes the other? 3. Given that a cause - effect relationship is probable to what theoretical constructs can the results be generalized? 4. Can the results be generalized to persons, settings, and time that are different from those employed in the experimental situation?
Threats to Design Validity 1. Statistical CONCLUSION VALIDTY - appropriate use of statistical procedures for analyzing data low statistical power violated assumptions of statistical test reliability variance
Threats to Design Validity 2. Internal Validity - potential for confounding factors to interfere with the relationship between the IV and DV variables history testing regression selection
Threats to Design Validity 3. Construct validity of cause and effect - theoretical conceptualization of IV and DV variables multiple treatment interactions experimental bias Hawthorne effect
Threats to Design Validity 4. External validity - the extent to which results of a study can be generalized outside the experiment situation interaction of treatment and selection interaction of treatment and setting interaction of treatment and history