1 lecture 19 chapter 9 evaluation techniques (part 2)

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Lecture 19

chapter 9evaluation techniques

(part 2)

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Evaluating Implementations

Requires an artefact:a simulation, a prototype, or afull implementation

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Experimental evaluation

• controlled evaluation of specific aspects of interactive behaviour

• evaluator chooses hypothesis to be tested

• a number of experimental conditions are considered which differ only in the value of some controlled variable.

• changes in behavioural measure are attributed to different conditions

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Experimental factors

• Subjects (i.e., the users, aka ‘participants’)– who – representative, sufficient sample

• not the programmer’s friend, boss, etc.• huge variability in performance of individuals

• Variables– things to modify and measure

• Hypothesis– what you’d like to show

• Experimental design– how you are going to show it– Includes ‘Protocol’ – what the subjects do

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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.

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Hypothesis

• prediction of outcome– framed in terms of IV and DV

e.g. “error rate will increase as font size decreases”

• null hypothesis:– states no difference between conditions– aim is to disprove this

e.g. null hyp. = “no change with font size”

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Experimental design

• “within groups” design (also called “repeated measures”)– each subject performs experiment under each condition– transfer of learning possible (practice makes

performance better; or alternatively fatigue or boredom makes it worse)

– less costly and less likely to suffer from user variation (each user is compared to themselves)

• between groups design– each subject performs under only one condition– no transfer of learning – more users required

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Within v. Between

• Consider a test on the difference of beer v. vodka martinis on reaction time– Null hypothesis – no difference in increase in reaction

time between the two beverages

• Design 1:– 30 people try beer; 30 other people try vodka – D.V.

is change in reaction time pre- v. post drinking• Not bad – be sure to randomize who goes into beer

group v. vodka group• But ‘power’ of the experiment will be reduced due to

the great variability of individuals in reaction to alcohol

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Within v. Between (contd.)

• Design 2:– All 60 people first try beer, then immediately try

vodka• Problem of carryover effect

• Better Design:– All 60 try beer, then a week later try vodka

• Now each individual is compared with themselves• Still possible problem of ordering effect (e.g., they

might get a little better at the reaction time test)

• Best Design:– 30 try beer, then a week later vodka; 30 try vodka

and then a week later beer

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Analysis of data

• Before you start to do any statistics:– look at data (e.g. average=5.25 – but 4.9 without outlier)– save original data

• Choice of statistical technique depends on– type of data– information required

• Type of data– discrete

• finite number of values• may be ordered, or

unordered (e.g., colors)– continuous

• any value0

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Analysis - types of test

• parametric– assume normal distribution– robust– powerful

• non-parametric– do not assume normal distribution– less powerful– more reliable

• contingency table– classify data by discrete attributes – count number of data items in each group

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Analysis of data (cont.)

• What information is required?– is there a difference?– how big is the difference?– how accurate is the estimate?

• Parametric and non-parametric tests mainly address first of these

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ANOVA – analysis of variance

• Quite easy to test whether there’s a significant difference between groups in Excel– Need to invoke

Tools/Add-ins/Analysis Toolpack to enable– Then just apply Tools/Data Analysis/ANOVA:

Single Factor to the data

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ANOVA from Excel

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Group 1 5 82 16.4 9.3Group 2 5 67 13.4 10.3Group 3 7 79 11.28571 3.904762

ANOVASource of Variation SS df MS F P-value F critBetween Groups 76.28908 2 38.14454 5.244339 0.019959 3.738892Within Groups 101.8286 14 7.273469

Total 178.1176 16

If P-value < 0.05 then we usually say the result is ‘significant’ (result is more than expected chance variation)

Say we have three columns of numbers representing the time to complete a task for 5, 5 and 7 users using three variations of an interface

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When is a difference a difference?

• In the world of parametric stats, we look for a statistic to be large enough to be ‘significant’– On the Gaussian (‘normal’) curve a |Z|=1.96

leaves 95% of the area of thecurve behind so is acommon ‘criticalvalue’ forclaimingsignificance

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Parametric assumptions• Parametric statistics assume that

some mathematically elegant assumptions hold true for the data– E.g., ANOVA (and standard

‘regression’) assume, among other things, normally distributed random error

– Trivia: The mathematical form of the probability density function for the normal distribution is remarkably formidable

• Centres on mean, , and is flattened by standard deviation,

Galton machine simulates normal distribution (aka ‘bell curve’)

Exponential distribution models time between events happening with a constant average rate

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So, what to measure?

• Usually one (or several) of these things:– Speed / efficiency

• How many (or whatever) per unit time can the user process with this interface?

– Accuracy (errors)– Learnability (time to acquire the ability to do

something in particular with the interface)• And retention (how well they can do it some particular

time later)

– Satisfaction• Subjective assessment – how does the user feel about

the interface

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Subjective assessment

• Very important– If the user seriously doesn’t like it, probably there’s

something really wrong with the design (just maybe they can’t articulate what)

• Quantifiable– Yes/No, or better, Likert scale (ratings) through

interview or questionnaire• Need to ask the right questions

– E.g., don’t have leading questions (BAD: “Is this the absolute worst system you have used ever?”)

• And need to ask the questions well (so user reliably expresses what they mean)– E.g., don’t trip them up with double negatives

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Likert Scale

• Can be from 4 to 7 “points”• Usually about agreement to a phrase

– E.g., “I found the search function easy to use”– Strongly Agree, Agree, Neutral (optional), Disagree,

Strongly Disagree• May also be about importance

– E.g., “A site search function is…”– “not very important” to “extremely important”

• Or a general assessment– E.g., “The performance of the search function was…”– Poor, Fair, Satisfactory, Good, Excellent

• Great to ask open-ended questions, too– E.g., “What was the best aspect of the search function?”– But it’s the Likert scale data that you can quantify

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Dimensions and validity

• When designing a questionnaire…– Have in mind a few underlying issues that you are

trying to assess– Ask a few different questions that are coordinated

around each issue– Ask different ways – vary whether positive or

negative favours the issue in question– Ideally, verify the questionnaire by having people

role-play particularly happy or angry, and middle-of-the-road users

• And see if they answer the questions the way you’d expect!

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Questionnaire administration

• Face-to-face or telephone interviews– Esp. efficient (and unbiased) to have third party give

the telephone survey

• Mail-out (including email) questionnaire• Web-based questionnaire (maybe email out

URL)• Even a questionnaire is an experiment on

humans from the point of view of research ethics– Easier to achieve an ethical questionnaire

administration if it’s truly anonymous

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Response rates and bias

• Low response rate is a problem– Below 50% response rate, one wonders whether

respondents were exceptional (happiest, angriest or a mix; but not the “normal” folks)

– Better if you have some authority to motivate a response

• But back to issues of ethics – e.g., not truly anonymous if you know who to nag about non-response; and the “pressure” may be unfair

• Similar bias problems when you use volunteers for any experiment– Are these volunteers representative of your “normal”

users?

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Experimental studies on groupsMore difficult than single-user experiments

Problems with:– subject groups– choice of task– data gathering– Analysis

• Unfortunately (in terms of experimental requirements) a lot of things that are interesting in the real world, involve computers mediating group behaviour

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Subject groups

larger number of subjects more expensive

longer time to `settle down’… even more variation!

difficult to timetable

so … often only three or four groups

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Groups (contd.): Data gathering

several video cameras+ direct logging of application

problems:– synchronisation– sheer volume!

one solution:– record from each perspective

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Groups (contd.): Analysis

N.B. vast variation between groups

solutions:– ‘within groups’ experiments (each group works under

various conditions)– micro-analysis (e.g., gaps in speech)– anecdotal and qualitative analysis

controlled experiments may `waste' resources!– Experiments dominated by dynamics of group formation– Field studies are apt to be more realistic

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About statistics

• It’s an amazingly complex field– A lot of hidden complexities in running experiments and

saying that the observed differences really make a difference

• ‘threats to validity’ – are those things that make it possible that your experimental conclusion is in error

– Threats to internal validity: like carryover effects, or lack of randomization

– Threats to external validity: like that your whole population of subjects were unusual in some way, or the task was not representative of real use of the tool

– When the outcomes are serious (e.g., medical trials) professional statisticians are always used in design of the experiment as well as analysis and reporting of the findings

– Plenty of texts and courses on stats available (the Wikipedia is pretty good on this topics, too – e.g., for ANOVA)