IS 4800 Empirical Research Methods for Information Science

IS 4800 Empirical Research Methods for Information Science Class Notes Feb. 24, 2012 Instructor: Prof. Carole Hafner, 446 WVH hafner@ccs. neu. edu Tel: 617 -373 -5116 Course Web site: www. ccs. neu. edu/course/is 4800 sp 12/

Types of Quantitative Studies We’ve Discussed • Observational • Survey • Experimental – One-factor, two-level, between-subjects – One-factor, two-level, within-subjects • aka “repeated measures” or “crossover” – Matched pairs 2

Types of Experimental Designs • Between-Subjects Design – Different groups of subjects are randomly assigned to the levels of your independent variable – Data are averaged for analysis – Use t-test for independent means – Example: “single factor, two-level, between subjects” design • Level A=Word vs. Level B=Wizziword 3

Types of Experimental Designs • Within-Subjects Design – A single group of subjects is exposed to all levels of the independent variable – Data are averaged for analysis – aka “repeated measures design”, “crossover design” – Use t-test for dependent means aka “paired samples t-test” – We will discuss “single factor, two-level, within subjects” designs. 4

Between-Subjects Design • Each group is a sample from a population • Big question: are the populations the same (null hypothesis) or are they significantly different? 5

Sidebar: Randomization • Crucial: method must not be applied subjectively • Point in time at which randomization occurs is important recruiting randomization experiment final measures 6

Sidebar: Randomization • Simple randomization – Flip a coin – Random number generator – Table of random numbers – Partition numeric range into number of conditions • Problems? 7

Sidebar: Randomization • Blocked randomization – Avoids serious imbalances in assignments of subjects to conditions – Guarantees that imbalance will never be larger than a specified amount – Example: want to ensure that every 4 subjects we have an equal number assigned to each of 2 conditions => “block size of 4” – Method: write all permutations of N conditions taken B at a time (for B = block size) • Example: AABB, ABAB, BABA, BBAA, ABBA – At the start of each block, select one of the orderings at random – Should use block sizes > 2 8

Sidebar: Randomization • Stratified randomization – First stratify Ss based on measured factors (prior to randomization) (e. g. , gender) – Within each strata, randomize • Either simple or blocked Strata 1 2 Sex M F Condition assignment ABBA BABA… BABA BBAA…

Within-Subjects Designs Benefits • More Power! Why? – Controls for all inter-subject variability – Randomized between-subjects design just balances the effects between groups – (Matched-pair controls for identified and matched extraneous variables) 10

The Problem of Error Variance • Error variance is the variability among scores not caused by the independent variable – Error variance is common to all experimental designs – Error variance is handled differently in each design • Sources of error variance (“extraneous variables”) – Individual differences among subjects – Environmental conditions not constant across levels of the independent variable – Fluctuations in the physical/mental state of an individual subject 11

Error Variance Independent Variable Individual Differences Environmental Conditions + Measured Outcomes

Handling Error Variance • Taking steps to reduce error variance – Hold extraneous variables constant by treating subjects as similarly as possible – Match subjects on crucial characteristics • Increasing the effectiveness of the independent variable – Strong manipulations yield less error variance than weak manipulations 13

Matched Group Design Treatment 1 Match Pairs Randomize • Use when you know some third variable has significant correlation with outcome • A between-subjects design • Use paired-samples t-test! Treatment 2 14

Handling Error Variance • Randomizing error variance across groups – Distribute error variance equivalently across levels of the independent variable – Accomplished with random assignment of subjects to levels of the independent variable • Statistical analysis – Random assignment tends to equalize error variance across groups, but not guarantee that it will – You can estimate the probability that observed differences are due to error variance by using inferential statistics 15

Within-Subjects Designs • Subjects are not randomly assigned to treatment conditions – The same subjects are used in all conditions – Closely related to the matched-groups design • Advantages – Reduces error variance due to individual differences among subjects across treatment groups – Reduced error variance results in a more powerful design • Effects of independent variable are more likely to be detected 16

Within-Subjects Designs Disadvantages • More demanding on subjects, especially in complex designs • Subject attrition is a problem • Carryover effects: Exposure to a previous treatment affects performance in a subsequent treatment 17

Carryover Example • Embodied Conversational Agents to Promote Health Literacy for Older Adults Brochure Computer T 0 T 1 T 2 Diabetes Knowledge Assessment

Sources of Carryover • Learning – Learning a task in the first treatment may affect performance in the second • Fatigue – Fatigue from earlier treatments may affect performance in later treatments • Habituation – Repeated exposure to a stimulus may lead to unresponsiveness to that stimulus • Sensitization – Exposure to a stimulus may make a subject respond more strongly to another • Contrast – Subjects may compare treatments, which may affect behavior • Adaptation – If a subject undergoes adaptation (e. g. , dark adaptation), then earlier results may differ from later ones 19

Dealing With Carryover Effects • Counterbalancing – The various treatments are presented in a different order for different subjects – May be complete or partial – Balances the effects of carryover on each treatment – Assumes carryover effect is independent of the order 20

Dealing With Carryover Effects • Taking Steps to Minimize Carryover – Techniques such as pre-training, practice sessions, or rest periods between treatments can reduce some forms of carryover • Make Treatment Order an Independent Variable – Allows you to measure the size of carryover effects, which can be taken into account in future experiments 21

Dealing With Carryover Effects • The Latin Square Design – Sample partial counterbalancing approach – Used when you make the number of treatment orders equal to the number of treatments (each treatment occurs once in every row and column) – Example: want to evaluate 4 different word processors, using 4 admins in 4 departments. A completely counterbalanced design would require 4 x 4 x 4=64 trials. – Latin square attempts to eliminate systematic bias in assignment of treatment to departments & subjects. Subj Department 1 2 3 4 1 C B A D Treatments A-D 2 B A D C 3 D C B A 4 A D C B 22

Example of a Counterbalanced Single-Factor Design With Two Treatments Order 1 2 Subject 1 2 … Treatment Sequence AB BA Order 2 1 … Treatment A 23. 5 14. 6 … Treatment B 14. 2 11. 5 … How do you test for “order effects”?

Types of Studies We’ve Discussed • Review pro’s and con’s of between subjects and within subjects. What is matched pairs?

Example – Best Design? • You’ve developed a new web-based help system for your email client. You want to compare your system to the old printed manual. 25

Example – Best Design? • You’ve just developed the “Matchmaker” – a handheld device that beeps when you are in the vicinity of a compatible person who is also carrying a Matchmaker. • You evaluate the number of users who are married after six months of use compared to a non-intervention control group. 26

Example – Best Design? • You’ve just developed “Reado Speedo” that reads print books using OCR and speaks them to you at twice your normal reading rate. You want to evaluate your product against the old fashioned way on reading rate, comprehension and satisfaction. 27

Introduction to Usability Testing I. Summative evaluation: Measure/compare user performance and satisfaction • Quantitative measures • Statistical methods II. Formative Evaluation: Identify Usability Problems • Quantitative and Qualitative measures • Ethnographic methods such as interviews, focus groups

Usability Goals (Nielsen) 1. 2. 3. 4. 5. Learnability Efficiency Memorability Error avoidance/recovery User satisfaction Operationalize these goals to evaluate usability

What is a Usability Experiment? Usability testing in a controlled environment • There is a test set of users • They perform pre-specified tasks • Data is collected (quantitative and qualitative) • Take mean and/or median value of measured attributes • Compare to goal or another system Contrasted with “expert review” and “field study” evaluation methodologies The growth of usability groups and usability laboratories

Experimental factors Subjects representative sufficient sample Variables independent variable (IV) characteristic changed to produce different conditions. e. g. interface style, number of menu items. dependent variable (DV) characteristics measured in the experiment e. g. time taken, number of errors.

Experimental factors (cont. ) Hypothesis prediction of outcome framed in terms of IV and DV null hypothesis: states no difference between conditions aim is to disprove this. Experimental design within groups design each subject performs experiment under each condition. transfer of learning possible less costly and less likely to suffer from user variation. between groups design each subject performs under only one condition no transfer of learning more users required variation can bias results.

Summative Analysis What to measure? (and it’s relationship to a usability goal) Total task time User “think time” (dead time? ? ) Time spent not moving toward goal Ratio of successful actions/errors Commands used/not used frequency of user expression of: confusion, frustration, satisfaction frequency of reference to manuals/help system percent of time such reference provided the needed answer

Measuring User Performance Measuring learnability Time to complete a set of tasks Learnability/efficiency trade-off Measuring efficiency Time to complete a set of tasks How to define and locate “experienced” users Measuring memorability The most difficult, since “casual” users are hard to find for experiments Memory quizzes may be misleading

Measuring User Performance (cont. ) Measuring user satisfaction Likert scale (agree or disagree) Semantic differential scale Physiological measure of stress Measuring errors Classification of minor v. serious

Reliability and Validity Reliability means repeatability. Statistical significance is a measure of reliability Validity means will the results transfer into a real-life situation. It depends on matching the users, task, environment Reliability - difficult to achieve because of high variability in individual user performance

Formative Evaluation What is a Usability Problem? ? Unclear - the planned method for using the system is not readily understood or remembered (info. design level) Error-prone - the design leads users to stray from the correct operation of the system (any design level) Mechanism overhead - the mechanism design creates awkward work flow patterns that slow down or distract users. Environment clash - the design of the system does not fit well with the users’ overall work processes. (any design level) Ex: incomplete transaction cannot be saved

Qualitative methods for collecting usability problems Thinking aloud studies Difficult to conduct Experimenter prompting, non-directive Alternatives: constructive interaction, coaching method, retrospective testing Output: notes on what users did and expressed: goals, confusions or misunderstandings, errors, reactions expressed Questionnaires Should be usability-tested beforehand Focus groups, interviews

Observational Methods - Think Aloud user observed performing task user asked to describe what he is doing and why, what he thinks is happening etc. Advantages simplicity - requires little expertise can provide useful insight can show system is actually use Disadvantages subjective selective act of describing may alter task performance

Observational Methods - Cooperative evaluation variation on think aloud user collaborates in evaluation both user and evaluator can ask each other questions throughout Additional advantages less constrained and easier to user is encouraged to criticize system clarification possible

Observational Methods - Protocol analysis paper and pencil cheap, limited to writing speed audio good for think aloud, diffcult to match with other protocols video accurate and realistic, needs special equipment, obtrusive computer logging automatic and unobtrusive, large amounts of data difficult to analyze user notebooks coarse and subjective, useful insights, good for longitudinal studies Mixed use in practice. Transcription of audio and video difficult and requires skill. Some automatic support tools available

Query Techniques - Interviews analyst questions user on one to one basis usually based on prepared questions informal, subjective and relatively cheap Advantages can be varied to suit context issues can be explored more fully can elicit user views and identify unanticipated problems Disadvantages very subjective time consuming

Query Techniques - Questionnaires Set of fixed questions given to users Advantages quick and reaches large user group can be analyzed more rigorously Disadvantages less flexible less probing

Laboratory studies: Pros and Cons Advantages: specialist equipment available uninterrupted environment Disadvantages: lack of context difficult to observe several users cooperating Appropriate if actual system location is dangerous or impractical for to allow controlled manipulation of use.

Steps in a usability experiment 1. The planning phase 1. The execution phase 1. Data collection techniques 1. Data analysis

The planning phase Who, what, where, when and how much? • Who are test users, and how will they be recruited? • Who are the experimenters? • When, where, and how long will the test take? • What equipment/software is needed? • How much will the experiment cost? Prepare detailed test protocol *What test tasks? (written task sheets) *What user aids? (written manual) *What data collected? (include questionnaire) How will results be analyzed/evaluated? Pilot test protocol with a few users

Detailed Test Protocol What tasks? Criteria for completion? User aids What will users be asked to do (thinking aloud studies)? Interaction with experimenter What data will be collected? All materials to be given to users as part of the test, including detailed description of the tasks.

Execution phase Prepare environment, materials, software Introduction should include: purpose (evaluating software) voluntary and confidential explain all procedures recording question-handling invite questions During experiment give user written task description(s), one at a time only one experimenter should talk De-briefing

Execution phase: ethics of human experimentation applied to usability testing Users feel exposed using unfamiliar tools and making erros Guidelines: • Re-assure that individual results not revealed • Re-assure that user can stop any time • Provide comfortable environment • Don’t laugh or refer to users as subjects or guinea pigs • Don’t volunteer help, but don’t allow user to struggle too long • In de-briefing • answer all questions • reveal any deception • thanks for helping

Execution Phase: Designing Test Tasks: Are representative Cover most important parts of UI Don’t take too long to complete Goal or result oriented (possibly with scenario) Not frivolous or humorous (unless part of product goal) First task should build confidence Last task should create a sense of accomplishment

Data collection - usability labs and equipment Pad and paper the only absolutely necessary data collection tool! Observation areas (for other experimenters, developers, customer reps, etc. ) - should be shown to users Videotape (may be overrated) - users must sign a release Video display capture Portable usability labs Usability kiosks

Analysis of data Before you start to do any statistics: look at data save original data Choice of statistical technique depends on type of data information required Type of data discrete - finite number of values continuous - any value

Testing usability in the field 1. Direct observation in actual use discover new uses take notes, don’t help, chat later 2. Logging actual use objective, not intrusive great for identifying errors which features are/are not used privacy concerns

Testing Usability in the Field (cont. ) 3. Questionnaires and interviews with real users ask users to recall critical incidents questionnaires must be short and easy to return 4. Focus groups 6 -9 users skilled moderator with pre-planned script computer conferencing? ? 5 On-line direct feedback mechanisms initiated by users may signal change in user needs trust but verify 6. Bulletin boards and user groups

Field Studies: Pros and Cons Advantages: natural environment context retained (though observation may alter it) longitudinal studies possible Disadvantages: distractions noise Appropriate for “beta testing” where context is crucial for longitudinal studies