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Scales of Measur­ement

Caterg­orical; order doesn't matter
Gender: 1 (male), 2 (female)
Ordered values. Order matters, but not difference between values
Agree­ment: 1 (SD), 2 (D), 3 (Neutral), 4 (A), 5 (SA). Pain Scale (1-10)
Numeric. Difference between values is meaningful
Relative Temper­ature: °C, °F, pH
Numeric. Zero and ratios are meaningful
Height, Weight, Absolute Temper­ature (K)
Measur­ement is the process of observing and recording the observ­ations collected as a part of a research effort.

Step 1: Define Research Questions

eg. How does your techni­que...
• Compare with altern­ative techni­ques?
• For which target popula­tion?
Target users
• For what tasks?
• In terms of what measures?
Perf­ormance measures
• In what context?
Other factors
Target users: need to be specific - students who have been using the desired medium consi­ste­ntly, for example
Perf­ormance measur­es: like speed, accuracy
Other factors: other than different techni­ques, what factors can influence the measures?

Step 2: Define Variables

• Factors manipu­lated in the experiment
• Have multiple levels
• Factors being measured
Control variab­les
• Attributes fixed throughout the experiment
Conf­oun­ders - attributes that vary and aren't accounted for
Random variab­les
• Attributes that are randomly sampled
• Increases genera­lis­ability
Conf­oun­ders rather than IVs could have caused changes in DV.
They make it diffic­ult­/im­pos­sible to draw conclu­sions.
Order of presen­tation and prior experi­ence are two important confou­nders that we need to control. (by counte­r-b­ala­ncing and proper sampling)

Step 3: Arranging Conditions (Withi­n-S­ubj­ects)

List the IV and their levels
Technique (2 levels: Gesture, Marking)
Menu depth (2 levels: 1, 2)
Determine coun­ter­-ba­lancing strate­gies for each IV
• Full counte­r-b­ala­ncing (n! condit­ions)
• Latin Square (n condit­ions)
• No counte­r-b­ala­ncing (seque­ntial) (1 condition)
Determine minimum no. of partic­ipants
Multiply all conditions together
Determine fact­orial arrang­ement of conditions
Put the permut­ations together
Determine arrang­ement for each partic­ipant
Cond­ition reduction strate­gies:
• Pick the most import­ant­/in­ter­esting factors to test
• Run a few IVs at a time - if strong effect, include IV in future studies, otherwise, pick fixed control value for it

One-way ANOVA

Basic Idea: ANOVA tries to find the sources of this variance:
• due to difference between groups
• Variab­ility within each group
Total Variab­ility = Betwee­nGroup + Within­Group
Ratio of Variab­ility
F = (𝑆𝑆𝑀­/DFB) / (𝑆𝑆𝑅­/DFW)
If the experiment is succ­ess­ful, then 𝑆𝑆𝑀>­𝑆𝑆𝑅.
Betwee­n-group variab­ility will explain more variance than within­-group.
The bigger the F value, the smaller the p value, and the less like the null hypothesis (no differ­ence) is true.
1. Calculate 𝑆𝑆𝑇
𝑆𝑆𝑇=­𝑠_𝑔­𝑟𝑎𝑛𝑑2 (𝑁−1)
DFT = (N-1)
2. Calculate 𝑆𝑆𝑀
𝑆𝑆𝑀=∑_i𝑛_𝑖 (𝑥 ̅_𝑖­−𝑥 ­̅_𝑔𝑟𝑎𝑛𝑑 )2
• sum of n difference of means from the grand mean
DFM = (No. of groups - 1)
3. Calculate 𝑆𝑆𝑅
𝑆𝑆𝑅=∑_𝑖𝑠_𝑖2 (𝑛_𝑖−1)
• sum of varianceno. of results in each group
DFR = total no. of results - no. of groups
Double check: 𝑆𝑆𝑇=­𝑆𝑆­𝑀+­𝑆𝑆𝑅 & DFT = DFM - DFR
4. Calculate Mean Squared Error
5. Calculate F-ratio
if F is lower than value in F-table, then p < 0.05 results are statis­tically signif­icant

Behaviour Theories

Health Belief Model
Perceived Benefits v Perceived Barriers, Perceived Theat, Self-E­ffi­cacy, Cues to Action all contribute to Likelihood of Engaging in Health­-Pr­omoting Behaviour
Theory of Reasoned Action
Self-b­elief + Influenced beliefs, Attitudes, Intention Behaviour
Self-D­ete­rmi­nation Theory
Intrinsic (self-­ben­efit) v Extrinsic motivation (external benefits)
Goal Setting Theory
Basic idea: goal serves as a motivator, work harder as long as they believe goal is achiev­able. Importance in Clarity, Challenge and Feedback
Social Cognitive Theory
Cognitive, Enviro­nmental and Behavi­oural factors determine human behaviour
Fogg Behavi­oural Model
Behaviour = Motiva­tors, Ability, Triggers
• Motiva­tors: Sensation, Antici­pation, Social Cohesion
• Ability: Train or Simplify
• Triggers: Spark, Signal or Facili­tator

Testable Research Questions

Weak questi­ons are untes­table and broad
Stronger questi­ons are more testable, but less genera­liz­able

Step 4: Define Trials

Estimate the time for each trial
around 5-10 seconds?
Estimate the time for each condit­ion
Time for each trial no. of trials for each condition
Bala­nce the trials (so experiment is within 45 min)
Combine with the cond­ition arrang­ement
Essent­ially, find the total time the experiment will take
Trials: a single repetition of a single condition
Typically want to have at least 3 trials per condit­ion to increase reliab­ility
Consider time: trials should last for 45 minutes (excluding pre and post interv­iews)

Intera­ction Effect

Which t-test or ANOVA?

Cognition Processes

Reading, speaking & listening
Proble­m-s­olving, planning, reasoning & decisi­on-­making


Selecting things to concen­trate on at a point in time from the mass of stimuli around us
Focus on inform­ation that's rele­vant to what we are doing
Involves audio/­visual senses
Design implic­ations:
• Make inform­ation sali­ent if it needs attending to
• make things stand out
avoid clutte­ring interface


How inform­ation is acqu­ired from the world, and transf­ormed into experi­ences
Design repres­ent­ations that are readily percei­vable
• Group inform­ation
• Text should bne legible and distin­gui­shable from the background


Stages of memory:
• Encoding
• Storage
• Retrieval
• Determines which info is attended to in enviro­nment + how it's intepreted
• Context affects extent to which info can be retrieved - different context difficult to recall
• Focus attent­ion/no compli­cated procedures
• Recogn­ition over recall
• Provide various ways of encoding and retrieving info (searching v history)
Sensory Memory:
shor­tes­t-t­erm memory, acts like a buffer for stimuli retrieved
• Ability to remember and process info at same time
• Inform­ation will decay within 10-15s
• Extended by rehearsal, hindered by interf­erence
Long-term Memory:
•Decla­rative Memory (factual info):
 • Semantic Memory (general) + Episodic Memory (personal knowledge)
• Procedural Memory (skill­s/h­abits)
• Intern­al/­Ext­ernal stimuli for retr­ieval cues
• Encoded at same time as memory

Cognitive System Principles

Unce­rtainty Princi­ple
where T = Decision time, H = log2(n+1) (where n is the no. of choices)
Variable Rate Princi­ple
More effort Faster processing (ie. cycle time
Cycle time also diminishes with practice: 𝑇_𝑛=𝑇_1 𝑛−𝛼
Fitts' Law
𝑇_𝑀=𝑎+𝑏 log_2⁡­(𝐴/𝑊+1)
where A = distance to target, W = error tolerance

Trans-­the­ore­tical Models

5 Stages of Change
Pre-co­nte­mpl­ation, Contem­pla­tion, Prepar­ation, Action, Mainte­nance
Processes of Change
Consci­ousness raising, Social libera­tion, Goal setting, Helping relati­ons­hips, Rewards
Processes of change can be applied to 5 stages of change.
Each person will value different processes differ­ently.


We use sample statistics to estima­te/­make infere­nces about population parameters
Due to uncer­tainty and varia­bil­ity, conclu­sions and estimates may not always be correct.
Need measures of reli­abi­lity
Conf­idence interval
• the confidence that the true population value of a parameter falls within a conf­idence interval
• affected by: vari­ation & sample size
Level of signif­ica­nce
•“P value”, α
• the prob. of reje­cting the null hypoth­esis when it is actually true (Type I error)
• ie. concluding that there is a difference when there may be no actual difference
• signifies the probab­ility that the difference is due to chance
Level of Signif­icance Thre­sho­lds
• Not signif­icant (p>.1; p=n.s.)
• Marginally signif­icant (p<0.1)
• (Fairly) signif­icant (p<.05)
• (Good) signif­icant (p<.01)
• (Excel­lently) signif­icant (p<.001)

Some Formulae

Cumulative Percentage

Central Limit Theorem

As the sample size gets larger...
The mean of sample means approaches the popu­lation mean
The standard error of the sameple means = the standard deviation of the population mean

SED Between 2 Samples

2-Sample t-test

Small sample sizes not normal distri­but­ion
Use t-di­str­ibu­tion
1. Calculate mean difference
2. Calculate SD
3. Calculate no. of SDs away from 0
4. Calculate df = smaller n - 1
5. Calculate p-value, for signif­icance (which p-value is it closest to)
If given desired conf­idence interv­al, steps:
1. Given desired CI
2. Get no. of SDs away from 0 from t-table
3. Calculate margin of error in units ((2) SD)
Difference between groups more likely to be signif­icant if:
Large differ­ence between means
Small SD or large n in each group
• Continuous variable
• Indepe­ndent samples
Also called the inde­pen­den­t-s­amples t-test
Other tests:
• One-sample t-test (sample v constant)
• Paired­-sa­mpled t-test (withi­n-s­ubj­ects, repeated measures)
• One-way ANOVA

Cognitive Heuristics

where emot­ions influence deci­sions
where people over­est­imate the importance of inform­ation available to them
Conf­irm­ation Bias
where we only listen to inform­ation that confirms out prec­onc­ept­ions
Halo Effect
where an outcome in one area is due to factors from another
Framing Effect
where the words used push listeners in a certain direct­ion
Implic­ations: watch out for biasing your partic­ipants.

Structural Equation Modeling

Design Strategies for Lifestyle Behaviour Change

Abstract & Reflective

Nielsen Heuristics

Visibility of system status
Match system and real world
User control and freedom
Consis­tency and standards
Error prevention
Recogn­ition over recall
Flexib­ility and efficiency of use
Aesthetic and minimalist design
Help users recognise, diagnose, recover from errors
Help and docume­ntation

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