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Scale
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Description
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Examples
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Nominal
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Catergorical; order doesn't matter
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Gender: 1 (male), 2 (female)
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Ordinal
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Ordered values. Order matters, but not difference between values
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Agreement: 1 (SD), 2 (D), 3 (Neutral), 4 (A), 5 (SA). Pain Scale (1-10)
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Interval
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Numeric. Difference between values is meaningful
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Relative Temperature: °C, °F, pH
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Ratio
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Numeric. Zero and ratios are meaningful
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Height, Weight, Absolute Temperature (K)
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eg. How does your technique...
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• Compare with alternative techniques?
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Techniques
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• For which target population?
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Target users
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• For what tasks?
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Tasks
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• In terms of what measures?
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Performance measures
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• In what context?
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Other factors
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IV
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• Factors manipulated in the experiment
• Have multiple levels |
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DV
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• Factors being measured
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Control variables
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• Attributes fixed throughout the experiment
• Confounders - attributes that vary and aren't accounted for |
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Random variables
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• Attributes that are randomly sampled
• Increases generalisability |
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List the IV and their levels
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eg.
Technique (2 levels: Gesture, Marking) Menu depth (2 levels: 1, 2) |
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Determine counter-balancing strategies for each IV
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• Full counter-balancing (n! conditions)
• Latin Square (n conditions) • No counter-balancing (sequential) (1 condition) |
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Determine minimum no. of participants
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Multiply all conditions together
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Determine factorial arrangement of conditions
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Put the permutations together
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Determine arrangement for each participant
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Basic Idea: ANOVA tries to find the sources of this variance:
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• due to difference between groups
• Variability within each group |
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Total Variability = BetweenGroup + WithinGroup
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𝑆𝑆𝑇=𝑆𝑆𝑀+𝑆𝑆𝑅
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Ratio of Variability
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F = (𝑆𝑆𝑀/DFB) / (𝑆𝑆𝑅/DFW)
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If the experiment is successful, then 𝑆𝑆𝑀>𝑆𝑆𝑅.
Between-group variability will explain more variance than within-group. |
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The bigger the F value, the smaller the p value, and the less like the null hypothesis (no difference) is true.
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Steps:
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1. Calculate 𝑆𝑆𝑇
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𝑆𝑆𝑇=𝑠_𝑔𝑟𝑎𝑛𝑑2 (𝑁−1)
DFT = (N-1) |
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2. Calculate 𝑆𝑆𝑀
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𝑆𝑆𝑀=∑_i𝑛_𝑖 (𝑥 ̅_𝑖−𝑥 ̅_𝑔𝑟𝑎𝑛𝑑 )2
• sum of n difference of means from the grand mean DFM = (No. of groups - 1) |
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3. Calculate 𝑆𝑆𝑅
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𝑆𝑆𝑅=∑_𝑖𝑠_𝑖2 (𝑛_𝑖−1)
• sum of varianceno. of results in each group DFR = total no. of results - no. of groups |
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Double check: 𝑆𝑆𝑇=𝑆𝑆𝑀+𝑆𝑆𝑅 & DFT = DFM - DFR
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4. Calculate Mean Squared Error
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MSM = SSM/DFM
MSR = SSR/DFR |
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5. Calculate F-ratio
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F = MSM / MSR
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if F is lower than value in F-table, then p < 0.05 results are statistically significant
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Health Belief Model
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Perceived Benefits v Perceived Barriers, Perceived Theat, Self-Efficacy, Cues to Action all contribute to Likelihood of Engaging in Health-Promoting Behaviour
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Theory of Reasoned Action
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Self-belief + Influenced beliefs, Attitudes, Intention Behaviour
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Self-Determination Theory
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Intrinsic (self-benefit) v Extrinsic motivation (external benefits)
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Goal Setting Theory
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Basic idea: goal serves as a motivator, work harder as long as they believe goal is achievable. Importance in Clarity, Challenge and Feedback
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Social Cognitive Theory
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Cognitive, Environmental and Behavioural factors determine human behaviour
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Fogg Behavioural Model
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Behaviour = Motivators, Ability, Triggers
• Motivators: Sensation, Anticipation, Social Cohesion • Ability: Train or Simplify • Triggers: Spark, Signal or Facilitator |
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Estimate the time for each trial
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around 5-10 seconds?
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Estimate the time for each condition
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Time for each trial no. of trials for each condition
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Balance the trials (so experiment is within 45 min)
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Combine with the condition arrangement
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Essentially, find the total time the experiment will take
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Attention
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Perception
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Memory
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Learning
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Reading, speaking & listening
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Problem-solving, planning, reasoning & decision-making
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Selecting things to concentrate on at a point in time from the mass of stimuli around us
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Focus on information that's relevant to what we are doing
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Involves audio/visual senses
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Design implications:
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• Make information salient if it needs attending to
• make things stand out •avoid cluttering interface |
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How information is acquired from the world, and transformed into experiences
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Design representations that are readily perceivable
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Implication:
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• Group information
• Text should bne legible and distinguishable from the background |
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Stages of memory:
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• Encoding
• Storage • Retrieval |
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Encoding:
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• 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 |
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Implications:
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• Focus attention/no complicated procedures
• Recognition over recall • Provide various ways of encoding and retrieving info (searching v history) |
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Storage:
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Sensory Memory:
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• 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 |
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Long-term Memory:
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•Declarative Memory (factual info):
• Semantic Memory (general) + Episodic Memory (personal knowledge) • Procedural Memory (skills/habits) |
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Retrieval:
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• Internal/External stimuli for retrieval cues
• Encoded at same time as memory |
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Uncertainty Principle
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𝑇=𝐼𝐶𝐻
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where T = Decision time, H = log2(n+1) (where n is the no. of choices)
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Variable Rate Principle
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More effort Faster processing (ie. cycle time
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Cycle time also diminishes with practice: 𝑇_𝑛=𝑇_1 𝑛−𝛼
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Fitts' Law
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𝑇_𝑀=𝑎+𝑏 log_2(𝐴/𝑊+1)
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where A = distance to target, W = error tolerance
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5 Stages of Change
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Pre-contemplation, Contemplation, Preparation, Action, Maintenance
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Processes of Change
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Consciousness raising, Social liberation, Goal setting, Helping relationships, Rewards
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We use sample statistics to estimate/make inferences about population parameters
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Due to uncertainty and variability, conclusions and estimates may not always be correct.
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Need measures of reliability
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• Confidence interval
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• the confidence that the true population value of a parameter falls within a confidence interval
• affected by: variation & sample size |
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• Level of significance
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•“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 |
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Level of Significance Thresholds
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• Not significant (p>.1; p=n.s.)
• Marginally significant (p<0.1) • (Fairly) significant (p<.05) • (Good) significant (p<.01) • (Excellently) significant (p<.001) |
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As the sample size gets larger...
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The mean of sample means approaches the population mean
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The standard error of the sameple means = the standard deviation of the population mean
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𝑆𝐸=𝑠/√𝑛=√(𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒/𝑛)
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Small sample sizes not normal distribution
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Use t-distribution
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Steps:
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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) |
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If given desired confidence interval, steps:
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1. Given desired CI
2. Get no. of SDs away from 0 from t-table 3. Calculate margin of error in units ((2) SD) |
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Difference between groups more likely to be significant if:
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• Large difference between means
• Small SD or large n in each group |
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Assumptions:
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• Continuous variable
• Independent samples |
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Affects
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where emotions influence decisions
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Availability
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where people overestimate the importance of information available to them
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Confirmation Bias
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where we only listen to information that confirms out preconceptions
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Halo Effect
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where an outcome in one area is due to factors from another
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Framing Effect
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where the words used push listeners in a certain direction
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Abstract & Reflective
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Unobtrusive
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Public
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Aesthetic
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Positive
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Controllable
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Trending/Historical
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Comprehensive
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Visibility of system status
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Match system and real world
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User control and freedom
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Consistency and standards
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Error prevention
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Recognition over recall
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Flexibility and efficiency of use
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Aesthetic and minimalist design
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Help users recognise, diagnose, recover from errors
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Help and documentation
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