What is Experimental Validity and Reliability 9 Internal and 8 External Killers: Hidden Pitfalls That Can Ruin Your Research. Inconsistent or poorly defined measurement instruments can produce different results even when measuring the same thing, reducing reliability.
The Hidden Pitfalls That Can Ruin Your Research A Complete Guide to Experimental Validity and Reliability 9 Internal and 8 External Killers
Hidden pitfalls that can undermine research include reliability risks, such as random error, inconsistent measurement instruments, and inadequate testing conditions, as well as validity risks, such as history, maturity, selection bias, measurement error, researcher bias, and demand characteristics. Addressing these problems through careful experimental design and control of confounding variables is critical to the accuracy, consistency, and relevance of research findings.
Why Your Research Results Might Be Wrong
As we have seen, the fundamental purpose of experimental design is to impose control over conditions that would otherwise cloud the true effects of the independent variables upon the dependent variables.
Clouding conditions that threaten to jeopardize the validity of experiments have been identified by Campbell and Stanley (1963), Bracht and Glass (1968) and Lewis-Beck (1993), conditions incidentally that are of greater consequence to the validity of quasi-experiments (more typical in educational research) than to true experiments in which random assignment to treatments occurs and where both treatment and measurement can be more adequately controlled by the researcher.
Understanding the Two Pillars of Valid Research
The following summaries adapted from Campbell and Stanley, Bracht and Glass, and Lewis-Beck distinguishes between ‘internal validity’ and ‘external validity’.
What is Internal Validity?
Internal validity is concerned with the question, do the experimental treatments, in fact, make a difference in the specific experiments under scrutiny?
What is External Validity?
External validity, on the other hand, asks the question, given these demonstrable effects, to what populations or settings can they be generalized?
The 9 Silent Killers of Internal Validity
1. History Effects: When Outside Events Hijack Your Results
History Frequently in educational research, events other than the experimental treatments occur during the time between pretest and post-test observations. Such events produce effects that can mistakenly be attributed to differences in treatment.
2. Maturation: The Natural Changes That Confound Your Data
Maturation Between any two observations subjects change in a variety of ways. Such changes can produce differences that are in dependent of the experimental treatments. The problem of maturation is more acute in protracted educational studies than in brief laboratory experiments.
3. Statistical Regression: The Mean’s Magnetic Pull
Statistical regression Like maturation effects, regression effects increase systematically with the time interval between pretests and posttests. Statistical regression occurs in educational (and other) research due to the unreliability of measuring instruments and to extraneous factors unique to each experimental group.
Regression means simply that subjects scoring highest on a pretest are likely to score relatively lower on a post-test; conversely, those scoring lowest on a pretest are likely to score relatively higher on a post-test. In short, in pretest-post-test situations, there is regression to the mean. Regression effects can lead the educational researcher mistakenly to attribute post-test gains and losses to low scoring and high scoring respectively.
4. Testing Effects: When the Pretest Becomes the Problem
Testing Pretests at the beginning of experiments can produce effects other than those due to the experimental treatments. Such effects can include sensitizing subjects to the true purposes of the experiment and practice effects which produce higher scores on post test measures.
5. Instrumentation Issues: When Your Tools Turn Against You
Instrumentation Unreliable tests or instruments can introduce serious errors into experiments. With human observers or judges or changes in instrumentation and calibration, error can result from changes in their skills and levels of concentration over the course of the experiment.
6. Selection Bias: The Wrong Participants Problem
Selection Bias may be introduced as a result of differences in the selection of subjects for the comparison groups or when intact classes are employed as experimental or control groups. Selection bias, moreover, may inter act with other factors (history, maturation, etc.) to cloud even further the effects of the comparative treatments.
7. Experimental Mortality: The Dropout Dilemma
Experimental mortality the loss of subjects through dropout often occurs in long-running experiments and may result in confounding the effects of the experimental variables, for whereas initially the groups may have been randomly selected, the residue that stays the course is likely to be different from the unbiased sample that began it.
8. Instrument Reactivity: When Measurement Changes Behavior
Instrument reactivity The effects that the instruments of the study exert on the people in the study (see also Vulliamy, Lewin and Stephens, 1990).
9. Selection-Maturation Interaction: The Confusing Combo
Selection-maturation interaction where there is confusion between the research design effects and the variables’ effects.
The 8 External Validity Threats That Limit Your Research Impact
Threats to external validity are likely to limit the degree to which generalizations can be made from the particular experimental conditions to other populations or settings. Below, we summarize a number of factors (adapted from Campbell and Stanley, 1963; Bracht and Glass, 1968; Hammersley and Atkinson, 1983; Vulliamy, 1990; Lewis-Beck, 1993) that jeopardize external validity.
1. Vague Variables: The Replication Roadblock
Failure to describe independent variables explicitly unless independent variables are adequately described by the researcher, future replications of the experimental conditions are virtually impossible.
2. Sample Mismatch: When Your Participants Don’t Represent Reality
Lack of representativeness of available and target populations Whilst those participating in the experiment may be representative of an available population, they may not be representative of the population to which the experimenter seeks to generalize her findings, i.e. poor sampling and/or randomization.
3. The Hawthorne Effect: When Being Watched Changes Everything
Hawthorne effect Medical research has long recognized the psychological effects that arise out of mere participation in drug experiments, and placebos and double-blind designs are commonly employed to counteract the biasing effects of participation. Similarly, so-called Hawthorne effects threaten to contaminate experimental treatments in educational research when subjects realize their role as guinea pigs.
4. Measurement Misalignment: When Lab Results Don’t Match Real Life
Inadequate operationalizing of dependent variables Dependent variables that the experimenter operationalizes must have validity in the non-experimental setting to which she wishes to generalize her findings. A paper and pencil questionnaire on career choice, for example, may have little validity in respect of the actual employment decisions made by undergraduates on leaving university.
5. Pretest Sensitivity: When Initial Testing Skews Results
Sensitization/reactivity to experimental conditions as with threats to internal validity, pretests may cause changes in the subjects’ sensitivity to the experimental variables and thus cloud the true effects of the experimental treatment.
6. Interaction Chaos: When Multiple Factors Collide
Interaction effects of extraneous factors and experimental treatments all of the above threats to external validity represent interactions of various clouding factors with treatments. As well as these, interaction effects may also arise as a result of any or all of those factors identified under the section on ‘Threats to internal validity’.
7. Unreliable Tools: When Your Instruments Let You Down
Invalidity or unreliability of instruments the use of instruments which yield data in which confidence cannot be placed (see below on tests).
8. Ecological Validity: The Context Transfer Challenge
Ecological validity, and its partner, the extent to which behavior observed in one context can be generalized to another
Hammersley and Atkinson (1983:10) comment on the serious problems that surround attempts to relate inferences from responses gained under experimental conditions, or from interviews, to everyday life.
The Internal-External Validity Relationship: A Critical Balance
By way of summary, we have seen that an experiment can be said to be internally valid to the extent that within its own confines, its results are credible (Pilliner, 1973); but for those results to be useful, they must be generalizable beyond the confines of the particular experiment; in a word, they must be externally valid also.
Pilliner points to a lopsided relationship between internal and external validity. Without internal validity an experiment cannot possibly be externally valid. But the converse does not necessarily follow; an internally valid experiment may or may not have external validity.
Thus, the most carefully designed experiment involving a sample of Welsh-speaking children is not necessarily generalizable to a target population which includes non-Welsh-speaking subjects.
The Path Forward: Maximizing Both Types of Validity
It follows, then, that the way to good experimentation in schools, or indeed any other organizational setting, lies in maximizing both internal and external validity.
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