Scales of MeasurementScale | Description | Examples | Nominal | Catergorical; order doesn't matter | Gender: 1 (male), 2 (female) | Ordinal | Ordered values. Order matters, but not difference between values | Agreement: 1 (SD), 2 (D), 3 (Neutral), 4 (A), 5 (SA). Pain Scale (1-10) | Interval | Numeric. Difference between values is meaningful | Relative Temperature: °C, °F, pH | Ratio | 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 Questionseg. How does your technique... | • Compare with alternative techniques? | Techniques | • For which target population? | Target users | • For what tasks? | 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 VariablesIV | • Factors manipulated in the experiment
• Have multiple levels | DV | • 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 | eg.
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 ANOVABasic Idea: ANOVA tries to find the sources of this variance: | • due to difference between groups
• Variability within each group | Total Variability = BetweenGroup + WithinGroup | 𝑆𝑆𝑇=𝑆𝑆𝑀+𝑆𝑆𝑅 | 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. | Steps: | 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 | MSM = SSM/DFM
MSR = SSR/DFR | 5. Calculate F-ratio | F = MSM / MSR | if F is lower than value in F-table, then p < 0.05 results are statistically significant |
Behaviour TheoriesHealth 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 QuestionsWeak questions are untestable and broad
Stronger questions are more testable, but less generalizable Step 4: Define TrialsEstimate 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) Cognition ProcessesAttention | Perception | Memory | Learning | Reading, speaking & listening | Problem-solving, planning, reasoning & decision-making |
AttentionSelecting 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 |
PerceptionHow information is acquired from the world, and transformed into experiences | Design representations that are readily perceivable | Implication: | • Group information
• Text should bne legible and distinguishable from the background |
MemoryStages of memory: | • Encoding
• Storage
• Retrieval | Encoding: | • 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 | Implications: | • Focus attention/no complicated procedures
• Recognition over recall
• Provide various ways of encoding and retrieving info (searching v history) | Storage: | 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) | Retrieval: | • Internal/External stimuli for retrieval cues
• Encoded at same time as memory |
Cognitive System PrinciplesUncertainty Principle | 𝑇=𝐼𝐶𝐻 | 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 Models5 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. | | StatisticsWe 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) |
Central Limit TheoremAs 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 | 𝑆𝐸=𝑠/√𝑛=√(𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒/𝑛) |
2-Sample t-testSmall sample sizes not normal distribution | Use t-distribution | Steps: | 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 | Assumptions: | • 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 HeuristicsAffects | where emotions influence decisions | Availability | 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 ChangeAbstract & Reflective | Unobtrusive | Public | Aesthetic | Positive | Controllable | Trending/Historical | Comprehensive |
Nielsen HeuristicsVisibility 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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