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How to Design and Evaluate Research in Education

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CHAPTER 13 Experimental <strong>Research</strong> 2792. Gender. Step 1: Posttreatment critical ability maybe related <strong>to</strong> gender. Step 2: If groups differ significantly<strong>in</strong> proportions of each gender, threat exists.Although possible, this is unlikely. Step 3: Likelihoodof hav<strong>in</strong>g an effect unless controlled: low.Mortality. Step 1: Mortality is likely <strong>to</strong> affect posttreatmentscores on any measure of critical th<strong>in</strong>k<strong>in</strong>gs<strong>in</strong>ce those subjects who drop out or are otherwise lostwould likely have lower scores. Step 2: Groups probablywould not differ <strong>in</strong> numbers lost, but this should beverified. Step 3: Likelihood of hav<strong>in</strong>g an effect unlesscontrolled: moderate.Location. Step 1: If location of implementation oftreatment <strong>and</strong>/or of data collection differs for the twogroups, this could affect posttreatment scores on the criticalth<strong>in</strong>k<strong>in</strong>g test. Posttreatment scores would be expected<strong>to</strong> be affected by such resources as class size,availability of read<strong>in</strong>g materials, films, <strong>and</strong> so forth.Step 2: This threat may differ for groups unless controlledfor by st<strong>and</strong>ardiz<strong>in</strong>g locations for implementation <strong>and</strong>data collection. The classrooms us<strong>in</strong>g each method maydiffer systematically unless steps are taken <strong>to</strong> ensure thatresources are comparable. Step 3: Likelihood of hav<strong>in</strong>gan effect unless controlled: moderate <strong>to</strong> high.Instrumentation.1. Instrument decay. Step 1: Instrument decay may affectany outcome. Step 2: Instrument decay could differfor groups. This should not be a major problem,provided all <strong>in</strong>struments used are carefully exam<strong>in</strong>ed<strong>and</strong> any alterations found are corrected. Step 3: Likelihoodof hav<strong>in</strong>g an effect unless controlled: low.2. Data collec<strong>to</strong>r characteristics. Step 1: Data collec<strong>to</strong>rcharacteristics might affect scores on criticalth<strong>in</strong>k<strong>in</strong>g test. Step 2: This threat might differ forgroups unless controlled by us<strong>in</strong>g the same data collec<strong>to</strong>r(s)for all groups. Step 3: Likelihood of hav<strong>in</strong>gan effect unless controlled: moderate.3. Data collec<strong>to</strong>r bias. Step 1: Bias could certa<strong>in</strong>ly affectscores on critical th<strong>in</strong>k<strong>in</strong>g test. Step 2: Thisthreat might differ for groups unless controlled bytra<strong>in</strong><strong>in</strong>g implementers <strong>in</strong> adm<strong>in</strong>istration of the <strong>in</strong>strument<strong>and</strong>/or keep<strong>in</strong>g them ignorant as <strong>to</strong> whichtreatment group is be<strong>in</strong>g tested. Step 3: Likelihoodof hav<strong>in</strong>g an effect unless controlled: high.Test<strong>in</strong>g. Step 1: Pretest<strong>in</strong>g, if used, might well affectposttest scores on critical th<strong>in</strong>k<strong>in</strong>g test. Step 2: Presumablythe pretest would affect both groups equally, however,<strong>and</strong> would not be likely <strong>to</strong> <strong>in</strong>teract with method,s<strong>in</strong>ce <strong>in</strong>struc<strong>to</strong>rs us<strong>in</strong>g each method are teach<strong>in</strong>g criticalth<strong>in</strong>k<strong>in</strong>g skills. Step 3: Likelihood of hav<strong>in</strong>g an effectunless controlled: low.His<strong>to</strong>ry. Step 1: Extraneous events that might affectcritical th<strong>in</strong>k<strong>in</strong>g skills are difficult <strong>to</strong> conjecture, butthey might <strong>in</strong>clude such th<strong>in</strong>gs as a special TV series onth<strong>in</strong>k<strong>in</strong>g, attendance at a district workshop on criticalth<strong>in</strong>k<strong>in</strong>g by some students, or participation <strong>in</strong> certa<strong>in</strong>extracurricular activities (e.g., debates) that occur dur<strong>in</strong>gthe course of the study. Step 2: In most cases, theseevents would likely affect both groups equally <strong>and</strong>hence are not likely <strong>to</strong> constitute a threat. Such eventsshould be noted <strong>and</strong> their impact on each group assessed<strong>to</strong> the degree possible. Step 3: Likelihood of hav<strong>in</strong>g aneffect unless controlled: low.Maturation. Step 1: Maturation could affect outcomescores s<strong>in</strong>ce critical th<strong>in</strong>k<strong>in</strong>g is presumably related<strong>to</strong> <strong>in</strong>dividual growth. Step 2: Presum<strong>in</strong>g that the<strong>in</strong>struc<strong>to</strong>rs teach each method over the same time period,maturation should not be a threat. Step 3: Likelihoodof hav<strong>in</strong>g an effect unless controlled: low.Attitude of Subjects. Step 1: Subjects’ attitudescould affect posttest scores. Step 2: If the members ofeither group perceive that they are receiv<strong>in</strong>g any sort of“special attention,” this could be a threat. The extent <strong>to</strong>which either treatment is “novel” should be evaluated.Step 3: Likelihood of hav<strong>in</strong>g an effect unless controlled:low <strong>to</strong> moderate.Regression. Step 1: Regression is unlikely <strong>to</strong> affectposttest scores unless subjects are selected on the basisof extreme scores. Step 2: This threat is unlikely <strong>to</strong>affect groups differently, although it could do so. Step 3:Likelihood of hav<strong>in</strong>g an effect unless controlled: low.Implementation. Step 1: Instruc<strong>to</strong>r characteristicsare likely <strong>to</strong> affect posttreatment scores. Step 2: Becausedifferent <strong>in</strong>struc<strong>to</strong>rs teach the methods, they may welldiffer. This could be controlled by hav<strong>in</strong>g several <strong>in</strong>struc<strong>to</strong>rsfor each method, by hav<strong>in</strong>g each <strong>in</strong>struc<strong>to</strong>r teachboth methods, or by moni<strong>to</strong>r<strong>in</strong>g <strong>in</strong>struction. Step 3: Likelihoodof hav<strong>in</strong>g an effect unless controlled: high.The trick, then, <strong>to</strong> identify<strong>in</strong>g threats <strong>to</strong> <strong>in</strong>ternal validityis, first, <strong>to</strong> th<strong>in</strong>k of different variables (conditions,

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