Empirical audit and review and an assessment of evidentiary value in research on the psychological consequences of scarcity.

Empirical audit and review is an approach to assessing the evidentiary value of a research area. It involves identifying a topic and selecting a cross-section of studies for replication. We apply the method to research on the psychological consequences of scarcity. Starting with the papers citing a seminal publication in the field, we conducted replications of 20 studies that evaluate the role of scarcity priming in pain sensitivity, resource allocation, materialism, and many other domains. There was considerable variability in the replicability, with some strong successes and other undeniable failures. Empirical audit and review does not attempt to assign an overall replication rate for a heterogeneous field, but rather facilitates researchers seeking to incorporate strength of evidence as they refine theories and plan new investigations in the research area. This method allows for an integration of qualitative and quantitative approaches to review and enables the growth of a cumulative science

Empirical audit and review and an assessment of evidentiary value in research on the psychological consequences of scarcity

Empirical audit and review is an approach to assessing the evidentiary value of a research area. It involves identifying a topic and selecting a cross-section of studies for replication. We apply the method to research on the psychological consequences of scarcity. Starting with the papers citing a seminal publication in the field, we conducted replications of 20 studies that evaluate the role of scarcity priming in pain sensitivity, resource allocation, materialism, and many other domains. There was considerable variability in the replicability, with some strong successes and other undeniable failures. Empirical audit and review does not attempt to assign an overall replication rate for a heterogeneous field, but rather facilitates researchers seeking to incorporate strength of evidence as they refine theories and plan new investigations in the research area. This method allows for an integration of qualitative and quantitative approaches to review and enables the growth of a cumulative science

Individual differences in compliance and agreement for sleep logs and wrist actigraphy: A longitudinal study of naturalistic sleep in healthy adults

<div><p>There is extensive laboratory research studying the effects of acute sleep deprivation on biological and cognitive functions, yet much less is known about naturalistic patterns of sleep loss and the potential impact on daily or weekly functioning of an individual. Longitudinal studies are needed to advance our understanding of relationships between naturalistic sleep and fluctuations in human health and performance, but it is first necessary to understand the efficacy of current tools for long-term sleep monitoring. The present study used wrist actigraphy and sleep log diaries to obtain daily measurements of sleep from 30 healthy adults for up to 16 consecutive weeks. We used non-parametric Bland-Altman analysis and correlation coefficients to calculate agreement between subjectively and objectively measured variables including sleep onset time, sleep offset time, sleep onset latency, number of awakenings, the amount of wake time after sleep onset, and total sleep time. We also examined compliance data on the submission of daily sleep logs according to the experimental protocol. Overall, we found strong agreement for sleep onset and sleep offset times, but relatively poor agreement for variables related to wakefulness including sleep onset latency, awakenings, and wake after sleep onset. Compliance tended to decrease significantly over time according to a linear function, but there were substantial individual differences in overall compliance rates. There were also individual differences in agreement that could be explained, in part, by differences in compliance. Individuals who were consistently more compliant over time also tended to show the best agreement and lower scores on behavioral avoidance scale (BIS). Our results provide evidence for convergent validity in measuring sleep onset and sleep offset with wrist actigraphy and sleep logs, and we conclude by proposing an analysis method to mitigate the impact of non-compliance and measurement errors when the two methods provide discrepant estimates.</p></div

The relationship between daily sleep log and wrist actigraphy measurements.

<p>Bivariate histograms for (a) sleep onset (SON), (b) sleep offset (SOFF), and (c) total sleep time (TST). The white dotted line represents the equality line (perfect agreement); therefore, deviations from the line indicate a lack of agreement. The empirical distributions (right panels) show the distribution of differences (actigraphy minus sleep log values), with the black dotted line representing the bias, and boxes demarcating the three reference intervals included in our non-parametric Bland-Altman analysis (±0.5, ±1.0, ±1.5 hours).</p

Pearson’s correlation coefficients for compliance variables and personality traits.

<p>Pearson’s correlation coefficients for compliance variables and personality traits.</p

Compliance across time and in relation to agreement and personal traits.

<p>(a) Group compliance is shown across the 16-week data collection. The proportion of participants who completed the daily questionnaire is shown in red, and the amount of time delay between self-reported sleep offset and sleep log submission time is shown in blue. Solid lines represent best-fit linear models to the data. (b) Scatter plots illustrate the relationship between an individual’s mean compliance rate (left) and time delay (right) and their level of agreement (SON and SOFF combined). Agreement was defined as the percentage of data with an absolute difference less than one hour. (c) Scatter plots illustrate compliance rate and time delay in relation to behavioral avoidance measures derived from BIS/BAS scale.</p

Time series for an example participant showing actigraphy, sleep logs, and model output.

<p>Green dots represent the combined estimate for sleep onset time, which was computed as a weighted sum of the actigraphy and sleep log measurements. The text with arrows highlight days exemplifying good agreement, poor agreement, and non-compliance due to a missing sleep log. The upper violin-style plots represent the empirical distribution of all sleep onset measurements from actigraphy and sleep logs. The model was designed for robustness in cases where actigraphy and sleep logs have discrepant measurements (upper left) by producing a composite estimate closer to the more likely value considering the individual’s sleep history. When measurements have similar probability (upper right), the model produces a value that is near the average of the two measurements.</p