Formal User Studies

Marti Hearst (UCB SIMS)SIMS 213, UI Design &

DevelopmentApril 13, 1999

Outline Experiment Design

– Factoring Variables– Interactions

Special considerations when involving human participants

Example: Marking Menus– Motivation– Hypotheses– Design– Analysis

Formal Usability Studies

Situations in which these are useful– to determine time requirements for task

completion– to compare two designs on measurable

aspects» time required» number of errors» effectiveness for achieving very specific tasks

Require Experiment Design

Experiment Design Experiment design involves

determining how many experiments to run and which attributes to vary in each experiment

Goal: isolate which aspects of the interface really make a difference

Experiment Design Decide on

– Response variables» the outcome of the experiment»usually the system performance»aka dependent variable(s)

– Factors (aka attributes)» aka independent variables

– Levels (aka values for attributes)– Replication

»how often to repeat each combination of choices

Experiment Design Studying a system; ignoring users Say we want to determine how to

configure the hardware for a personal workstation Hardware choices

»which CPU (three types)»how much memory (four amounts)»how many disk drives (from 1 to 3)

– Workload characteristics»administration, management, scientific

Experiment Design We want to isolate the effect of each

component for the given workload type. How do we do this?

– WL1 CPU1 Mem1 Disk1– WL1 CPU1 Mem1 Disk2– WL1 CPU1 Mem1 Disk3– WL1 CPU1 Mem2 Disk1– WL1 CPU1 Mem2 Disk2– …

There are (3 CPUs)*(4 memory sizes)*(3 disk sizes)*(3 workload types) = 108 combinations!

Experiment Design

One strategy to reduce the number of comparisons needed:– pick just one attribute– vary it– hold the rest constant

Problems:– inefficient– might miss effects of interactions

Interactions among Attributes

A1 A2B1 3 5B2 6 8

A1 A2B1 3 5B2 6 9

A1

B1B1

A2

A1

B2

A2

B2

Non-interacting Interacting

A2A2 A1A1

B1 B2B1 B2

Experiment Design Another strategy: figure out which

attributes are important first Do this by just comparing a few

major attributes at a time – if an attribute has a strong effect,

include it in future studies– otherwise assume it is safe to drop it

This strategy also allows you to find interactions between attributes

Experiment Design

Common practice: Fractional Factorial Design– Just compare important subsets– Use experiment design to partially

vary the combinations of attributes Blocking

– Group factors or levels together– Use a Latin Square design to arrange

the blocks

Adapted from slide by James Landay

Between Groups vs. Within Groups

Do participants see only one design or both? Between groups

– two groups of test users– each group uses only 1 of the systems

Within groups experiment– one group of test users

» each person uses both systems» can’t use the same tasks (learning)

– Why is this a consideration?– People often learn during the experiment.

Special Considerations for Formal Studies with Human

Participants Studies involving human participants

vs. measuring automated systems– people get tired– people get bored– people (may) get upset by some tasks– learning effects

»people will learn how to do the tasks (or the answers to questions) if repeated

»people will (usually) learn how to use the system over time

More Special Considerations

High variability among people– especially when involved in

reading/comprehension tasks– especially when following hyperlinks!

(can go all over the place)

Experiment Design Example: Marking Menus

Based onKurtenbach, Sellen, and Buxton, Some Articulartory and Cognitive Aspects of

“Marking Menus”, Graphics Interface ‘94, http://reality.sgi.com/gordo_tor/papers

Experiment Design Example: Marking Menus

Pie marking menus can reveal – the available options – the relationship between mark and command

1. User presses down with stylus 2. Menu appears 3. User marks the choice, an ink trail follows

Why Marking Menus?

Supporting markings with pie menus should help transition between novice and expert

Useful for keyboardless devices Useful for large screens Pie menus have been shown to be

faster than linear menus in certain situations

What do we want to know? Are marking menus better than pie

menus?– Do users have to see the menu?– Does leaving an “ink trail” make a difference?– Do people improve on these new menus as

they practice? Related questions:

– What, if any, are the effects of different input devices?

– What, if any, are the effects of different size menus?

Experiment Factors Isolate the following factors (independent

variables):

– Menu condition»exposed, hidden, hidden w/marks (E,H,M)

– Input device»mouse, stylus, track ball (M,S,T)

– Number of items in menu »4,5,7,8,11,12 (note: both odd and even)

Response variables (dependent variables):

– Response Time – Number of Errors

Experiment Hypotheses Note these are stated in terms of the

factors (independent variables)– Exposed menus will yield faster response times

and lower error rates, but not when menu size is small

– Response variables will monotonically increase with menu size for exposed menus

– Response time will be sensitive to number of menu choices for hidden menus (familiar ones will be easier, e.g., 8 and 12)

– Stylus better than Mouse better than Track ball

Experiment Hypotheses

– Device performance independent of menu type

– Performance on hidden menus (both marking and hidden) will improve steadily across trials. Performance on exposed menus will remain constant.

Experiment Design Participants

– 36 right-handed people» usually gender distribution is stated

– considerable mouse experience– (almost) no trackball, stylus experience

Task– Select target “slices” from a series of different pie

menus as quickly and accurately as possible– Menus were simply numbered segments

» meaningful items would have longer learning times

– Participants saw running scores» lose points for wrong selection

Experiment Design

One between-subjects factor – Menu Type

»Three levels: E, H, or M

Two within-subjects factors– Device Type

»Three levels: M, T, or S

– Number of Menu Items»Six levels: 4, 5, 7, 8, 11, 12

How should we arrange these?

Experiment Design

E H M

12 12 12

Betweensubjectsdesign

How to arrange

thedevices?

Experiment Design

M

T

S

T

S

M

S

M

T

E H M

12 12 12

A LatinSquare

No row or

columnsharelabels

Experiment Design

M

T

S

T

S

M

S

M

T

E H M

How toarrange

themenu sizes?

Block by sizethen

randomize the

blocks.

Experiment Design

M

T

S

T

S

M

S

M

T

E H M

5 11

12 8

7 4

Block by sizethen

randomize the

blocks.

Experiment Design

M

T

S

T

S

M

S

M

T

E H M

5 11

12 8

7 4

7 8

12 5

4 11

40 trials per block

Experiment Overall Results

Group Mean RT(s.d)

Mean Errors(s.d.)

Mean %Errors

Exposed 0.98 (.23) 0.64 (1.0) 1.6%

Hidden 1.10 (.31) 3.27 (3.57) 8.2%

Marking 1.10 (.31) 3.76 (3.67) 9.4%

So exposing menus is faster … or is it?Let’s factor things out more.

A Learning EffectWhen we graph over the number of trials, we finda difference between exposed and hidden menus.This suggests that participants may eventually becomefaster using marking menus. (hypothesized)A later study verified this.

Factoring to Expose Interactions Increasing menu size increases selection time and

number of errors (hypothesized) No differences across menu groups in terms of

response time. That is, until we factor by menu size AND group

– Then we see that menu size has effects on hidden groups not seen on exposed group

– This was hypothesized (12 easier than 11)

Factoring to Expose Interactions Stylus and mouse outperformed trackball

(hypothesized) Stylus and mouse the same (not

hypothesized) Initially, effect of input device did not interact

with menu type– this is when comparing globally– BUT ...

More detailed analysis:– Compare both by menu type and device type– Stylus significantly faster with Marking group– Trackball significantly slower with Exposed group– Not hypothesized!

Average response time and errors as

a function of device, menu size,

and menu type

Potential explanations:Markings provide feedbackfor when stylus is pressedproperly.Ink trail is consistent withthe metaphor of using a pen.

Experiment Design

M

T

S

T

S

M

S

M

T

E H M

How can we tell if order in which the device appears has an effect on the final outcome?

Some evidence:There is no significant difference among devices in the Hidden group.Trackball was slowest and most error prone in all three cases.Still, there may be some hidden interactions, but unlikelyto be strong given the previous graph.

Statistical Tests

Need to test for statistical significance– This is a big area– Assuming a normal distribution:

»Students t-test to compare two variables»ANOVA to compare more than two

variables

Adapted from slide by James Landay

Analyzing the Numbers

Example: trying to get task time <=30 min. – test gives: 20, 15, 40, 90, 10, 5– mean (average) = 30– median (middle) = 17.5– looks good! – wrong answer, not certain of anything

Factors contributing to our uncertainty– small number of test users (n = 6)– results are very variable (standard deviation =

32)» std. dev. measures dispersal from the mean

Adapted from slide by James Landay

Analyzing the Numbers (cont.)

This is what statistics is for Crank through the procedures and you find

– 95% certain that typical value is between 5 & 55

Usability test data is quite variable– need lots to get good estimates of typical

values– 4 times as many tests will only narrow range by

2x

Followup Work

Hierarchical Markup Menu study

Followup Work Results of use of marking menus

over an extended period of time– two person extended study– participants became much faster

using gestures without viewing the menus

Followup Work Results of use of marking menus

over an extended period of time– participants temporarily returned to

“novice” mode when they had been away from the system for a while

Summary Formal studies can reveal detailed information but

but take extensive time/effort Human participants entail special requirements Experiment design involves

– Factors, levels, participants, tasks, hypotheses– Important to consider which factors are likely to have real

effects on the results, and isolate these Analysis

– Often need to involve a statistician to do it right– Need to determine statistical significance– Important to make plots and explore the data

References

Kurtenbach, Sellen, and Buxton, Some Articulartory and Cognitive Aspects of “Marking Menus”, Graphics Interface ‘94, http://reality.sgi.com/gordo_tor/papers

Kurtenbach and Buxton, User Learning and Performance with Marking Menus, Graphics Interface ‘94, http://reality.sgi.com/gordo_tor/papers

Jain, The art of computer systems performance analysis, Wiley, 1991

http://www.statsoft.com/textbook/stanman.html Gonick and Smith, The Cartoon Guide to Statistics,

HarperPerennial, 1993 Dix et al. textbook