Copyright  2003 by Dr. Gallimore, Wright State University Department of Biomedical, Industrial Engineering & Human Factors Engineering Human Factors Research Methodologies

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Overview Descriptive Studies –Characterize population according to attributes Experimental Research –Test effect of variables on performance Evaluation Research –Test system or produce

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Research Settings Laboratory Field Simulation Work Sampling

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Selecting Variables Independent Variables –Treatment conditions that are manipulated –Task related –Environmental –Subject related Most studies include only a few IVs

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Selecting Variables Dependent Variables –Systems descriptive criteria equipment reliability, cost of operation –Task performance criteria Quantity of output, quality of output, performance time, errors –Human criteria Frequency measures, intensity measures, latency measures, duration measures, physiological indices, subjective responses

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Choosing Subjects Representative Random Sample size depends upon –Degree of accuracy required –Amount of variance in population –Statistic being used

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Collecting Data Descriptive Studies –Mostly survey and interviews, questionnaires –Important to avoid bias Experimental Research –Most controlled situation for data collection –Must be careful to select relevant experimental design and sampling technique. Evaluation Research –Researcher observation most often suffice as form of collection –Must work with system designers and builders to collect data

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Statistics in a Nutshell Interested in Entire Population Large Population = Expensive Surveys Sample a Small Group –Infer Population Characteristics –from Sample Characteristics

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Examples Exit polls on election day Marketing surveys for new products Medical trials for new vaccines Neilson ratings of television shows Air measurements for pollutants

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Definitions Population --- Universe (Entire Group) Parameter Numeric Characteristic of a Population Average, Variance, Standard Deviation, etc Sample --- Subgroup of the Population Statistic Numeric Characteristic of a Sample Average, Variance, Standard Deviation, etc

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Sampling Sampling is faster, cheaper, easier.

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Sampling Sampling is faster, cheaper, easier. Hope, that the sample is representative of the population; and therefore, the sample statistic is an accurate estimate of the population parameter.

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Sampling Sampling is faster, cheaper, easier. Hope, that the sample is representative of the population; and therefore, the sample statistic is an accurate estimate of the population parameter. If the sample is not representative of the population, then all bets are off !

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Statistics Descriptive Statistics –Collecting and Analyzing Data –“Tells a story about the numbers” Inferential Statistics –Drawing conclusions about a population

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Descriptive Statistics Measures of Central tendency –Mean (Arithmetic Average) –Median (Middle Value) Measures of Variation –Variance –Standard Deviation

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Measures of Central Tendency Mean –Arithmetic Average –Balance Point –Basis for other statistics –Influenced by extremes Median –Middle Value –Different Balance Point –Not influenced by extremes

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Important Statistics Mean –Tells where the data are centered Standard Deviation –Tells how far the data are spread out

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Analyzing Data Descriptive –Central tendency: mode, medium, mean –Dispersion: range, interquartile range, (25 th -75 th percentile), standard deviation –Effiecient, unbiased Correlational Inferential (ANOVA, MANOVA, regression) –Results only as good as the quality of selected variables, sampling of subjects, and experimental design. –Statistical significance doesn’t mean results are “meaningful”

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Analyzing Data Inferential (Cont) –Pitfalls in statistical analysis Type I error: rejecting a hypothesis when it is true Type II error: accepting a hypothesis when it is false

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Requirements for Research Criteria Practical Requirements –Objective, quantitative, unobtrusive, easy to collect, minimal cost (money and effort) Reliablity –Consistency across time and samples Validity (of dependent variables) –Face validity –Content validity (domain sampling) –Construct validity (basic behavior) Freedom from contamination Sensitivity

Copyright  2001 by Dr. Gallimore, Wright State University Department of Biomedical, Human Factors, & Industrial Engineering Measure of Human Reliability Probability of success due to error-free performance Data bases Technique for human error rate prediction Stochastic simulation models Criticisms –All errors result in failures? –Sources of reliability data too subjective –Reliability data lack breadth of coverage –Now have very complex systems