Between and Within Subject Measures of Affect William Revelle and Eshkol Rafaeli-Mor Northwestern University European Association of Personality Psychology Krakow, Poland, July,

Between and Within Subject Measures of Affect William Revelle and Eshkol Rafaeli-Mor With the collaboration of –Kris Anderson, GAO –Erin Baehr, Northwestern University –Douglas Billings, St. Marys College –Gregory Rogers, University of Chicago –Rishi Agrawal, Northwestern University –Neera Mehta, University of Illinois, Chicago Support from –US ARI contract MDA K-0008

Between and Within Subject Measures of Affect Personality traits and affective states Between versus within measures of mood and affect –Traditional measures of dimensionality, stability and variability of affect –Alternative within subject measures –Studies of the “tides of emotion” Applications to cognitive performance

Personality traits and affective states Personality: a musical metaphor –A tune may be recognizable even if played with different notes with a different instrument –A person is recognizable by the patterning of affective and cognitive states even though specific behavioral acts vary –Personality traits are coherent patterns of changes of states Multi-level modeling of parameters of affect –Level– Amplitude –Phase– Coherence –Synchrony (of multiple affects)

The long and short term predictability of affect How happy will you feel 12 years from today? –Are some people more likely to be happy than others? How happy will you feel 12 hours from now? –Are some people more predictable over time? How do affective rhythms allow for a better understanding of cognitive processes?

Multiple formulations of the measurement of affect Two dimensional models –Affective Valence and Arousal (Russell et al.) –Positive and Negative Affect (Tellegen, Watson & Clark) –Energetic and Tense Arousal (Thayer) Multidimensional models –Energetic and Tense Arousal, and Hedonic Tone (Matthews) –Hierarchical models (Watson and Tellegen)

Multiple sources of data-1 Between subject “snap shots” <-- –Adjective check lists –Rating scales Within subject “diary” studies –Very high frequency/continuous studies –High frequency sampling –Low frequency sampling

Adjective check lists (“I feel …”) –Energetic– Tense –Sleepy– Calm Rating scales (“I feel …”) –very happy, happy, sad, very sad (Bipolar) –not at all, somewhat, very happy (Unipolar) Between subject “snap shots”

Typical between subject structure Measures –Motivational State Questionnaire (MSQ) Item rating (0-3) scale –Items taken from Thayer’s Activation-Deactivation ACL Watson and Clark’s PANAS Diener and Larson Circumplex measures –Example Items: Alert Sleepy TenseCalm Lively Tired AnxiousRelaxed

Typical between subject structure Subjects –>2700 participants aggregated from > 40 studies of personality and cognition at NU over 6 years Method –Baseline measurements taken using the MSQ (R) –Studies done from 5:30 am to 10:30 pm –(additional analyses of effects of caffeine, exercise and movies on affect-not reported here)

Typical between subject structure Results –Factor extraction using PF and ML –Factor number determined by Very Simple Structure (VSS) –Clear 2 factor solution –Differential skew leads to suggestions of more factors 4 cluster solution representing +/- ends of two dimensions

Very Simple Structure => 2 Factors

2 Dimensions of Affect

Representative MSQ items (arranged by angular location)

Multiple sources of data-2 Between subject “snap shots” –Adjective check lists –Rating scales Within subject “diary” studies <-- –Very high frequency/continuous studies –High frequency sampling –Low frequency sampling

Within subject diary studies-1 Very High Frequency (continuous) measurements –Physiological assays Cortisol Body temperature <-- –Core body temperature collected for ≈ 2 weeks –Data taken by aggregating subjects from multiple studies conducted by Eastman and Baehr on phase shifting by light and exercise

Body Temperature as f(time of day) (Baehr, Revelle & Eastman, 2000)

Morningness/Eveningness and BT (Baehr, Revelle and Eastman, 2000)

Multiple sources of data-3 Between subject “snap shots” –Adjective check lists –Rating scales Within subject “diary” studies –Very high frequency/continuous studies –High frequency sampling <-- –Low frequency sampling

Within subject diary studies-2 Measures –Check lists –Rating scales High frequency sampling <-- –Multiple samples per day Low frequency sampling –Once a day –Sometimes at different times

High frequency measures of affect Measures taken every 3 hours during waking day for 6-14 days Paper and pencil mood ratings –Short form of the MSQ -- Visual Analog Scale –Sampled every 3 hours Portable computer (Palm) mood ratings <-- –Short form of the MSQ –Sampled every 3 hours

Palm Affect Survey

Palm affect and activity survey

Traditional measures Mean level –Energetic arousal –Tense arousal –Positive affect –Negative affect Variability Correlation across measures (Synchrony)

Phasic measures of affect Fit 24 hour cosine to data –Iterative fit for best fitting cosine –Permutation test of significance of fit Measure –Fit (coherence) –Amplitude –Phase

Affective rhythms can differ in phase (simulation - double plotted to show rhythm)

Phase differences of simulated daily data William Revelle: Should this come before the 24 hour slide William Revelle: Should this come before the 24 hour slide

Differences in coherence (fit) simulated daily data

Phase and Coherence differences (simulated data -- double plotted)

Multi-level analysis of patterns of affect across time-1: Method Within subject estimates of basic parameters –Level –Scatter (variability) –Phase –Coherence (fit) Between subject measures of reliability –Week 1/Gap/Week 2

Multi-level analyses of affect-2: 1-2 week Test-Retest Reliability

Affective rhythms and cognitive performance-1 Design: High frequency diary study of affect combined with a low frequency study of reaction time Subjects: 28 NU undergraduate volunteers Method: –1 week diary study 5 times a day –Simple reaction time once a day at 5 different times using a Mac program at home

Affective rhythms and cognitive performance-2 Low negative correlations of RT with concurrent measures of Energetic Arousal Stronger negative correlations of RT with Cosine fitted Energetic Arousal => Diurnal variation in RT may be fitted by immediate and patterns of arousal

Between and Within Subject Measures of Affect Personality traits and affective states Between versus within measures of mood and affect Alternative within subject measures- studying the “tides of emotion” Applications to cognitive performance More information found on links from the personality project -- and the Personality-Motivation lab