experience sampling presentation
DESCRIPTION
PresentationTRANSCRIPT
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experience samplingdesign, data collection & analysis
Ben Richardson
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experience sampling• a form of moment-to-moment
data collection• increased ecological validity• minimise retrospective bias• participant burden• different kinds of questions
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experience sampling• a form of moment-to-moment
data collection• increased ecological validity• minimise retrospective bias• participant burden• different kinds of questions
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experience sampling• a form of moment-to-moment
data collection• increased ecological validity• minimise retrospective bias• participant burden• different kinds of questions
![Page 5: Experience sampling presentation](https://reader033.vdocuments.us/reader033/viewer/2022060119/558ea6f01a28abfc118b45c9/html5/thumbnails/5.jpg)
experience sampling• a form of moment-to-moment
data collection• increased ecological validity• minimise retrospective bias• participant burden• different kinds of questions
![Page 6: Experience sampling presentation](https://reader033.vdocuments.us/reader033/viewer/2022060119/558ea6f01a28abfc118b45c9/html5/thumbnails/6.jpg)
experience sampling• a form of moment-to-moment
data collection• increased ecological validity• minimise retrospective bias• participant burden• different kinds of questions
![Page 7: Experience sampling presentation](https://reader033.vdocuments.us/reader033/viewer/2022060119/558ea6f01a28abfc118b45c9/html5/thumbnails/7.jpg)
Jeffrey S. Simons, Raluca M. Gaher, Matthew N.I. Oliver, Jacqueline A. Bush, Marc A. Palmer
An Experience Sampling Study of Associations between Affect and Alcohol Use and Problems among College Students
example
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a quick note; I am focused on self report studies but passive data collection is also
possible
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design considerations• appropriate measurement
resolution
couple satisfaction
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
blood glucose monitoring
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design considerations• appropriate measurement
resolution
event-based
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium interval-based
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design considerations• appropriate measurement
resolution
!
Mass & Hox (2005) Sufficient Sample Sizes for Multilevel
Modeling
• rough rule of thumb: 50 individuals
• although power depends on many factors and is often most usefully estimated based on power analysis
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
• honorarium
• usability
• length / frequency
• feedback
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium PDAs
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
web surveys
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resources for optimising web forms for mobile
• detecting whether participant is using mobile
• optimise webpage for iOS
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
mobile application
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design considerations• appropriate measurement
resolution
• event-based versus interval-based response cues
• sample size and power
• engaging participants
• response medium
mobile application
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analysis• main difference between ‘regular’ analysis and
analysis of ESM data is the hierarchical structure of the data
level 1: time points
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analysis• main difference between ‘regular’ analysis and
analysis of ESM data is the hierarchical structure of the data
{ { { { { {
level 1: time points
level 2: individuals
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analysis• multilevel modeling (MLM) addresses the lack of
independence between the observations
• can also use regression with robust standard errors
• in addition, MLM opens up some possibilities for some novel questions not so easily answered in single level analyses
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example• using ESM to study risky single occasion drinking
• presentation that follows is mostly visual, do not take the diagrams too literally. for more comprehensive / technical overview of MLM as applied to ESM data please see
• Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research
• Models for intensive longitudinal data
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intercept only model
risky drinking
fun seeking
level 1 variable
level 2 variableclustering variable = participant id
positive moodeveningpositive mood
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intercept only• Intraclass correlation (degree of variance explained
in the outcome variable by the clustering / nesting variable)
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intercept only• Intraclass correlation (degree of variance explained
in the outcome variable by the clustering / nesting variable)
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rsod on positive mood
risky drinking
fun seeking
level 1 variable
level 2 variableclustering variable = participant id
positive mood
evening
positive mood
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level 1 variables• level 1 variables actually capture two sources of
variance: • within participant variation (e.g., fluctuations around
an individual’s average level of mood) • between participant variation (e.g., individual
differences in level of positive mood)
• these are often usefully represented using separate variables in the model • achieved by person mean centring
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level 1 variables
level 1 positive mood = score - person’s mean !
!
level 2 positive mood = individual’s average across time points
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level 1 variables• fixed component of an effect
• average relationship between variables for all participants
• e.g., on average, how does positive mood relate to drinking? !
• random component • between participant variance in relationship • e.g., how much variation is there in the relationship
between positive mood and drinking? Does positive mood more strongly associate with drinking for some participants compared to others?
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rsod on positive mood
risky drinking
fun seeking
level 1 variable
level 2 variableclustering variable = participant id
positive mood
evening
positive mood
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level 2 moderators• can we explain variation in level 1 relationships
using level 2 variables?
• E.g., does an individual’s fun seeking explain variation in the relationship between positive mood and drinking?
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some extensions
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piecewise regressionsome resources
• http://www.ats.ucla.edu/stat/stata/faq/piecewise.htm
• http://www3.nd.edu/~rwilliam/stats2/l61.pdf
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dose-response model• Hunt & Rai (2003). A threshold dose-response model with random
effects in teratological experiments. doi: 10.1081/STA-120021567
4.5
9
13.5
18
1 2 3 4 5 6 7 8 9 10 11 12 13 14
ControlDose
4.5
9
13.5
18
1 2 3 4 5 6 7 8 9 10 11 12 13 14
ControlDose
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risk versus time to onset
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photo credits
Couple photo: https://flic.kr/p/4SDwWz !"Couple in Covent Garden" by Mark Hillary (https://www.flickr.com/photos/markhillary/)!!
Diabetes photo!"My "kit"" by Jessica Merz (https://www.flickr.com/photos/jessicafm/)!!
Alcohol photo!"Alcohol and Ulcerative Colitis" by Kimery Davis (https://www.flickr.com/photos/117025355@N05/)!!
Timer photo!"Microwave Timer" by Pascal (https://www.flickr.com/photos/pasukaru76/)!!
PDA photo!"I Used To Be Cool..." by H. Michael Karshis (https://www.flickr.com/photos/hmk/)!!
Piecewise regression graph http://www3.nd.edu/~rwilliam/stats2/l61.pdf