Validity and Reliability Chapters 8 Validity and Reliability

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Validity and Reliability Chapters 8

Validity and Reliability Chapters 8

Validity and Reliability • Validity is an important consideration in the choice of an

Validity and Reliability • Validity is an important consideration in the choice of an instrument to be used in a research investigation – It should measure what it is supposed to measure – Researchers want instruments that will allow them to make warranted conclusions about the characteristics of the subjects they study • Reliability is another important consideration, since researchers want consistent results from instrumentation – Consistency gives researchers confidence that the results actually represent the achievement of the individuals involved

Reliability • • Test-retest reliability Inter-rater reliability Parallel forms reliability Internal consistency (a. K.

Reliability • • Test-retest reliability Inter-rater reliability Parallel forms reliability Internal consistency (a. K. A. Cronbach’s alpha)

Validity • Face – Does it appear to measure what it purports to measure?

Validity • Face – Does it appear to measure what it purports to measure? • Content – Do the items cover the domain? • Construct – Does it measure the unobservable attribute that it purports to measure?

Validity • Criterion – Predictive – Concurrent • Consequential

Validity • Criterion – Predictive – Concurrent • Consequential

Types of validity (cont. ) The instrument The construct Here the instrument samples some

Types of validity (cont. ) The instrument The construct Here the instrument samples some and only of the construct

Types of validity The construct The instrument Here the instrument samples all and more

Types of validity The construct The instrument Here the instrument samples all and more of the construct

The construct Here the instrument fails to sample ANY of the construct The instrument

The construct Here the instrument fails to sample ANY of the construct The instrument

The construct The instrument Here the instrument samples some but not all of the

The construct The instrument Here the instrument samples some but not all of the construct

Perfection! The construct and the instrument!

Perfection! The construct and the instrument!

Reliability and Validity

Reliability and Validity

Experimental Research Designs

Experimental Research Designs

The (Never-Ending) Search for Causation • Establishing causation among variables : » Produces increased

The (Never-Ending) Search for Causation • Establishing causation among variables : » Produces increased understanding of those variables » Results in the ability to manipulate conditions in order to produce desired changes

Experimental Research • Can demonstrate cause-and-effect very convincingly • Very stringent research design requirements

Experimental Research • Can demonstrate cause-and-effect very convincingly • Very stringent research design requirements • Experimental design requires: » Random assignment to groups (experimental and control) » Independent treatment variable that can be applied to the experimental group » Dependent variable that can be measured in all groups

Quasi-Experimental Research • Used in place of experimental research when random assignment to groups

Quasi-Experimental Research • Used in place of experimental research when random assignment to groups is not feasible • Otherwise, very similar to true experimental research

Causal-Comparative Research • Explores the possibility of cause-and-effect relationships when experimental and quasi-experimental approaches

Causal-Comparative Research • Explores the possibility of cause-and-effect relationships when experimental and quasi-experimental approaches are not feasible • Used when manipulation of the independent variable is not ethical or is not possible

Fundamentals of Experimental and Quasi. Experimental Research • Cause and effect: » Incorporates a

Fundamentals of Experimental and Quasi. Experimental Research • Cause and effect: » Incorporates a temporal element—the cause is a condition that exists prior to the effect; effect is a condition that occurs after the cause » There exists a “logical connection”—cause-and-effect is demonstrated when manipulation of the independent variable results in differences in the dependent variable (as evidenced by comparing the experimental group to the control group)

What Aids Our Causal Arguments? • Theory – "causes certainly are connected to effects;

What Aids Our Causal Arguments? • Theory – "causes certainly are connected to effects; but this is because our theories connect them, not because the world is held together by cosmic glue. The world may be glued together by imponderables, but that is irrelevant for understanding causal explanation. " Hanson, 1958. • Temporal Elements • Design – "No causation without manipulation" Rubin & Holland

Inferring Causality Sir Bradford Hill • • Strength of association Consistency Specificity Temporal order

Inferring Causality Sir Bradford Hill • • Strength of association Consistency Specificity Temporal order Dose-Response (biological gradient) Plausibility Experimental evidence Analogy

Fundamentals of Experimental and Quasi. Experimental Research • Random selection and random assignment :

Fundamentals of Experimental and Quasi. Experimental Research • Random selection and random assignment : » Distinguish between “selection” and “assignment” » Random selection helps to assure population validity » If you incorporate random assignment Experimental research » If you do not use random assignment Quasi-experimental research

Fundamentals of Experimental and Quasi. Experimental Research (cont’d. ) • When to use experimental

Fundamentals of Experimental and Quasi. Experimental Research (cont’d. ) • When to use experimental research design : » If you strongly suspect a cause-and-effect relationship exists between two conditions, and » The independent variable can be introduced to participants and can be manipulated, and » The resulting dependent variable can be measured for all participants

Internal and External Validity • “Validity of research” refers to the degree to which

Internal and External Validity • “Validity of research” refers to the degree to which the conclusions are accurate and generalizable • Both experimental and quasi-experimental research are subject to threats to validity • If threats are not controlled for, they may introduce error into the study, which will lead to misleading conclusions

Internal and External Validity • “Validity of research” refers to the degree to which

Internal and External Validity • “Validity of research” refers to the degree to which the conclusions are accurate and generalizable • Both experimental and quasi-experimental research are subject to threats to validity • If threats are not controlled for, they may introduce error into the study, which will lead to misleading conclusions

Threats to External Validity • External validity—extent to which the results can be generalized

Threats to External Validity • External validity—extent to which the results can be generalized to other groups or settings » Population validity—degree of similarity among sample used, population from which it came, and target population » Ecological validity—physical or emotional situation or setting that may have been unique to the experiment » If the treatment effects can be obtained only under a limited set of conditions or only by the original researcher the findings have low ecological validity.

Threats to Internal Validity • Internal validity—extent to which differences on the dependent variable

Threats to Internal Validity • Internal validity—extent to which differences on the dependent variable are a direct result of the manipulation of the independent variable » History—when factors other than treatment can exert influence over the results; problematic over time » Maturation—when changes occur in dependent variable that may be due to natural developmental changes; problematic over time » Testing—also known as “pretest sensitization”; pretest may give clues to treatment or posttest and may result in improved posttest scores » Instrumentation – Nature of outcome measure has changed.

Threats to Internal Validity (cont’d. ) » Regression – Tendency of extreme scores to

Threats to Internal Validity (cont’d. ) » Regression – Tendency of extreme scores to be nearer to the mean at retest » Implementation-A group treated in an unintentional differential manner. » Attitude-Hawthorne effect, compensatory rivalry. » Differential selection of participants—participants are not selected/assigned randomly » Attrition (mortality)—loss of participants » Experimental treatment diffusion – Control conditions receive experimental treatment.

Experimental and Quasi-Experimental Research Designs • Commonly used experimental design notation : » X

Experimental and Quasi-Experimental Research Designs • Commonly used experimental design notation : » X 1 = treatment group » X 2 = control/comparison group » O = observation (pretest, posttest, etc. ) » R = random assignment

Common Experimental Designs • Single-group pretest-treatment-posttest design: O X O » Technically, a pre-experimental

Common Experimental Designs • Single-group pretest-treatment-posttest design: O X O » Technically, a pre-experimental design (only one group; therefore, no random assignment exists) » Overall, a weak design » Why?

Common Experimental Designs (cont’d. ) • Two-group treatment-posttest-only design: R R X 1 X

Common Experimental Designs (cont’d. ) • Two-group treatment-posttest-only design: R R X 1 X 2 O O » Here, we have random assignment to experimental, control groups » A better design, but still weak—cannot be sure that groups were equivalent to begin with

Common Experimental Designs (cont’d. ) • Two-group pretest-treatment-posttest design: R O X 1 O

Common Experimental Designs (cont’d. ) • Two-group pretest-treatment-posttest design: R O X 1 O R O X 2 O » A substantially improved design—previously identified errors have been reduced

Common Experimental Designs (cont’d. ) • Solomon four-group design: R O X 1 O

Common Experimental Designs (cont’d. ) • Solomon four-group design: R O X 1 O R O X 2 O R X 1 O R X 2 O » A much improved design—how? ? » One serious drawback—requires twice as many participants

Common Experimental Designs (cont’d. ) • Factorial designs: R O X 1 g 1

Common Experimental Designs (cont’d. ) • Factorial designs: R O X 1 g 1 O R O X 2 g 1 O R O X 1 g 2 O R O X 2 g 2 O » Incorporates two or more factors » Enables researcher to detect differential differences (effects apparent only on certain combinations of levels of independent variables)

Common Experimental Designs (cont’d. ) • Single-participant measurement-treatment-measurement designs: O O O | X

Common Experimental Designs (cont’d. ) • Single-participant measurement-treatment-measurement designs: O O O | X O | O O O » Purpose is to monitor effects on one subject » Results can be generalized only with great caution

Common Quasi-Experimental Designs • Posttest-only design with nonequivalent groups: X 1 O X 2

Common Quasi-Experimental Designs • Posttest-only design with nonequivalent groups: X 1 O X 2 O » Uses two groups from same population » Questions must be addressed regarding equivalency of groups prior to introduction of treatment

Common Quasi-Experimental Designs (cont’d. ) • Pretest-posttest design with nonequivalent groups: O X 1

Common Quasi-Experimental Designs (cont’d. ) • Pretest-posttest design with nonequivalent groups: O X 1 O O X 2 O » A stronger design—pretest may be used to establish group equivalency

Similarities Between Experimental and Quasi-Experimental Research • Cause-and-effect relationship is hypothesized • Participants are

Similarities Between Experimental and Quasi-Experimental Research • Cause-and-effect relationship is hypothesized • Participants are randomly assigned (experimental) or nonrandomly assigned (quasi-experimental) • Application of an experimental treatment by researcher • Following the treatment, all participants are measured on the dependent variable • Data are usually quantitative and analyzed by looking for significant differences on the dependent variable