Name: measuring light-switching behavior using an occupancy and light data logger
Uploaded: 2020-01-16T15:00:00
Description: Watch this Scientific Journal Video about Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger at JoVE.com

The study was approved by the ethics committee of the SWPS University of Social Science and Humanities in Warsaw (number 46/2016).
1. Choosing an experimental site for logger deployment
<ol>
	<li>Choose an indoor experimental site that will allow mounting the logger in close proximity to the light source (for adequate light changes detection) as well as to gather the data on the behavior regarding the room occupancy status (for adequate movement detection) of individual participants (i.e., o...

<ol>
	<li>Steg, L., Vlek, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Encouraging+pro-environmental+behaviour:+An+integrative+review+and+research+agenda.">Encouraging pro-environmental behaviour: An integrative review and research agenda.</a> Journal of Environmental Psychology. 29 (3), 309-317 (2009).</li><li>Doli&#324;ski, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Is+psychology+still+a+science+of+behaviour.">Is psychology still a science of behaviour.</a> Social Psychological Bulletin. 13, 25025 (2018).</li><li>Grzyb, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Why+can't+we+just+ask?+The+influence+of+research+methods+on+results.+The+case+of+the+"bystander+effect".">Why can't we just ask? 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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coding+observational+data:+A+software+solution.">Coding observational data: A software solution.</a> Behavior Research Methods. 37 (2), 224-231 (2005).</li><li>Bergquist, M., Nilsson, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=I+saw+the+sign:+promoting+energy+conservation+via+normative+prompts.">I saw the sign: promoting energy conservation via normative prompts.</a> Journal of Environmental Psychology. 46, 23-31 (2016).</li><li>Dwyer, P. C., Maki, A., Rothman, A. 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D., Trafimow, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Replication,+falsification,+and+the+crisis+of+confidence+in+social+psychology.">Replication, falsification, and the crisis of confidence in social psychology.</a> Frontiers in Psychology. 6, 1-11 (2015).</li><li>van Aert, R. C., van Assen, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Examining+reproducibility+in+psychology:+A+hybrid+method+for+combining+a+statistically+significant+original+study+and+a+replication.">Examining reproducibility in psychology: A hybrid method for combining a statistically significant original study and a replication.</a> Behavior research methods. 50 (4), 1515-1539 (2018).</li><li>Mehl, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Electronically+Activated+Recorder+(EAR):+A+device+for+sampling+naturalistic+daily+activities+and+conversations.">The Electronically Activated Recorder (EAR): A device for sampling naturalistic daily activities and conversations.</a> Behavior Research Methods, Instruments, &amp; Computers. 33 (4), 517-523 (2001).</li><li>Ali, A. S., Zanzinger, Z., Debose, D., Stephens, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Open+Source+Building+Science+Sensors+(OSBSS):+A+low-cost+Arduino-based+platform+for+long-term+indoor+environmental+data+collection.">Open Source Building Science Sensors (OSBSS): A low-cost Arduino-based platform for long-term indoor environmental data collection.</a> Building and Environment. 100, 114-126 (2016).</li><li>Popoola, O., Munda, J., Mpanda, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+analysis+and+assessment+of+ANFIS-based+domestic+lighting+profile+modelling.">Comparative analysis and assessment of ANFIS-based domestic lighting profile modelling.</a> Energy and Buildings. 107, 294-306 (2015).</li><li>Tetlow, R. M., Beaman, C. P., Elmualim, A. A., Couling, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simple+prompts+reduce+inadvertent+energy+consumption+from+lighting+in+office+buildings.">Simple prompts reduce inadvertent energy consumption from lighting in office buildings.</a> Building and Environment. 81, 234-242 (2014).</li><li>van Someren, K., Beaman, P., Shao, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Calculating+the+lighting+performance+gap+in+higher+education+classrooms.">Calculating the lighting performance gap in higher education classrooms.</a> International Journal of Low-Carbon Technologies. 13 (1), 15-22 (2017).</li><li>Landis, J. R., Koch, G. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+measurement+of+observer+agreement+for+categorical+data.">The measurement of observer agreement for categorical data.</a> Biometrics. 33 (1), 159-174 (1977).</li><li>McGraw, K. O., Wong, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forming+inferences+about+some+intraclass+correlation+coefficients.">Forming inferences about some intraclass correlation coefficients.</a> Psychological methods. 1 (1), 30 (1996).</li><li>Hallgren, K. 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When planning to use more than one site (for logger deployment) at the same time, it should be ensured that each site has an identical architectural layout in order to exclude the possibility of occurrence of different behavioral patterns from participants (i.e., resulting from occupancy times and light-switching possibilities). A suitable site should be equipped with one or more light sources with only one corresponding light switch, visible to the occupant. If otherwise, one should plann to use one logger fir each ligh...

One of the most commonly used measures of pro-environmental behavior in psychology are self-reports in the form of surveys, interviews, or questionnaires1. Among the reasons indicated for this trend is simply the difficulty in conducting field experiments, which usually require a fair amount of resources and precise operationalization2,3. However, the tradeoff is worth the effort since it is well established that relying on self-reporting measures can be misleading in the prediction of objective behavior4,5,6.
While trying to avoid this problem, researchers that are focused on studying energy conservation behavior generally use observational (nominal categorization of observed events, e.g., turning on/off lights) or residual (quantifiable evidence of a past behavior, e.g., energy consumption in kWh) data as measurements of dependent variables7. Although both types of measurements are valuable, observational data is most commonly used in field experiments2,3,8, particularly when their dependent variables concern light-switching behavior.
Before obtaining observational data, researchers should consider several methodological issues, which are: 1) sample representativeness; 2) the number of observers in order to exclude possible human errors; 3) inter-observer agreement in order to exclude experimenter bias; 4) observer location, which should be concealed in order to reduce the possibility of being spotted by participants; 5) clearly and specifically defined observation coding; 6) pretest of observational measures; 7) observer training; and 8) establishing systematic timing of observation9. Even though most of the mentioned issues were already addressed&#8212;for example those that concern reliability analysis10 or coding observational data11&#8212;it seems that not all of them receive much attention in articles that describe experiments on light-switching behavior.
An analysis of four studies12,13,14,15 that were chosen for their similarity in experimental context (all of them concerned light-switching behavior in public bathrooms/restrooms) showed that even though the location details in each of the studies were precise, the observation measurement details varied. Since each study employed naturalistic observation, gathering information about the behavior of participants that were the opposite sex of observers was not always possible14 due to possible interference or violation of social norms (e.g., if a male experimenter were to enter a women&#39;s restroom or vice versa). In some instances, the precise data of the participants&#39; genders were not provided15. This seems to be a limitation when taking into consideration that gender can be an important factor in predicting pro-environmental behavior16.
The biggest differences, however, emerged in the description of observers and measurement times. Even though these descriptions will naturally differ on the basis of experimental location, the precise number of observers was not always provided14. Furthermore, the exact location of observers was not explicit12,14,15 which makes it hard to conduct possible replications and ensure that participants are unaware of being observed. Across four analyzed articles, only one provided a detailed description of the observer&#39;s location13.
Moreover, the exact times of observation intervals were provided only by one study12 whereas other studies either described overall study times (with a general description of how many times on each study day the observation took place)13,15 or did not describe it at all14. This can again impede replicating and establishing whether the observation timing was systematic and sufficient for the purposes of the study aims.
The limitations of these experiments are presented as guidelines and important points that should be taken into consideration in future research. In no case it was intended to undermine the importance of these studies. The indicated areas should be considered for maximizing study operationalization in order to facilitate replications, which play an important role in psychology17,18, and simplify the conduction of field experiments. However, it is questionable whether all of the mentioned issues can be dealt with by improving observation methods that ultimately rely on human observers.
For these reasons, the occupancy and light data logger (see Table of Materials) is a valuable tool that can be effectively used to gather information about a particular type of energy conservation behaviors, light-switching, without the limitations of using observers or ethical restrictions (the logger does not gather the audio-visual data). Overall, the aim of this article is to present the technical description and possibilities of one model of the occupancy and light data logger. To the authors&#39; knowledge, this is the first attempt to present this tool thoroughly in the context of its use in field experiments in psychology.
Loggers&#39; technical description 
The model of occupancy/light data logger (see Table of Materials) that was used for this article was equipped with standard memory capacity of 128 kB. The logger weights 30 g and its size is 3.66 cm &#215; 8.48 cm &#215; 2.36 cm. Additional details and the product manual can be found on the manufacturer&#39;s website19.
Control buttons, the light sensor and the battery tray are located on the top panel. The front panel consists of the occupancy sensor and an LCD screen, whereas the back panel is equipped with mounting magnets and loops (Figure 1). The USB 2.0 port is located on the bottom panel, to allow the connection of the logger to the computer with a USB cable in order to enable set-up before deployment and to later obtain readouts using analysis software package dedicated to this data logger.
The integrated light sensor (photocell) threshold is greater than 65 lx, which works with different light types (LED, CFL, fluorescent, HID, incandescent, natural) that can be found in most public spaces. Overall, the logger interprets light status changes (ON/OFF) depending on the strength of the light signal, more precisely, whether it drops below or rises above levels of the calibration threshold. It should also be noted that the sensor is secured from false detection of ON and OFF states by a built-in hysteresis level of approximately &#177;12.5%19.
A motion sensor determines whether the room is occupied or unoccupied. With the use of a pyroelectric infrared (PIR) sensor, it detects the motion of people by their body temperature (which differs from the temperature of the surroundings). The detection range of the discussed logger has a maximum of 5 m and the extended version of the logger has a range of 12 m. Horizontal detection performance works up to 94&#176; (&#177;47&#176;), and vertical up to 82&#176; (&#177;41&#176;).
The described model of occupancy/light data logger has been validated alongside Open Source Building Science Sensors and appears to provide a reliable measurement of light intensity and occupancy frequency21. Furthermore, these models of loggers have been shown useful in built-environment research, precisely in lighting applications22,23,24.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>HOBO Occupancy/Light (5m Range) Data Logger</td>
			<td>ONSET</td>
			<td>UX90-005</td>
			<td>As advertised by Onset - The HOBO UX90-005 Room Occupancy/Light Data Logger is available in a standard 128 KB memory model (UX90-005) capable of 84,650 measurements and an expanded 512KB memory version (UX90-005M) capable of over 346,795 measurements. For details and other products visit: <a href="https://www.onsetcomp.com/products/data-loggers/ux90-005" target="_blank">https://www.onsetcomp.com/products/data-loggers/ux90-005</a></td>
		</tr>
		<tr>
			<td>HOBO Light Pipe</td>
			<td>ONSET</td>
			<td>UX90-LIGHT-PIPE-1</td>
			<td>An optional fiber optic attachment or light pipe that eliminates effects of ambient light to ensure the most accurate readings. For details visit: <a href="https://www.onsetcomp.com/support/manuals/17522-using-ux90-light-pipe-1" target="_blank">https://www.onsetcomp.com/support/manuals/17522-using-ux90-light-pipe-1</a></td>
		</tr>
		<tr>
			<td>HOBOware</td>
			<td>ONSET</td>
			<td>-</td>
			<td>Setup, graphing and analysis software for Windows and Mac. There are two versions of HOBOware: HOBOware (available for free) and HOBOware Pro (paid version which allows for additional analysis with different loggers). Each of them are dedicated to HOBO loggers. For details visit: <a href="https://www.onsetcomp.com/products/software/hoboware" target="_blank">https://www.onsetcomp.com/products/software/hoboware</a></td>
		</tr>
	</tbody>
</table>

measuring light-switching behavior using an occupancy and light data logger

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. Although direct behavioral observation may increase the external validity and generalizability of a study, it may be time-consuming and be subject to experimenter or observer bias. To address these issues, the use of data loggers as an alternative to natural observation can allow researchers to conduct broad studies without interrupting the participants&#39; naturally occurring behaviors. This article describes one of such tools&#8212;the occupancy and light data logger&#8212;with its technical description, deployment protocol, and information about its possible applications in psychological experiments. The results of testing the reliability of the logger in comparison to human observation is provided alongside an example of the gathered data during a 15-day measurement in public restroom (N = 1,148) that includes: 1) room occupancy changes; 2) indoor light changes; and 3) room occupancy time.

Loggers&#39; reliability test in comparison to human observation 
In order to test the reliability of the logger in comparison to human observation, a 4 h field test was conducted in a single-stall male restroom located on the University campus. Two male observers waited outside the restroom (approximately 5 m away from the front door) and independently recorded the visitors&#39; behavior in terms of occupancy rates/times and light switching (lights left ON or OFF upon exiting). Simultaneously, two ...

Watch this Scientific Journal Video about Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger at JoVE.com

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

This article describes a procedure for using and deploying an occupancy and light data logger which allows collecting data about light-switching behavior of participants in field settings.

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. ...

The occupancy and light data logger can be used in the field settings to gather information about the occurrence of a particular type of energy conservation behavior, which is light-switching. As opposed to natural observation, using data logger allows for fast, reliable, and uninterrupted measurement of participant's behavior, as well as provides an explicit operationalization of a given field experiment. To begin, connect the logger via USB cable to the computer by plugging the larger end of the USB interface cable into a USB port on the computer, and the smaller end of the USB interface cable into the port on the device.Launch the software by clicking the launch icon on the toolbar, which opens the logger setup window. Enter a name for the launch. Select the light sensor.Set the measurement to log state from the drop down list and choose the state description off and on from the drop down list. Select the occupancy sensor. Set the measurement to log state from the drop down list and choose the state description unoccupied occupied from the drop down list.Click the filters button to enable automatic calculation of additional values. Select the sensor type of choice. Select the type of filter and the interval to use.Edit the name and click create new series. Select the drop down menu choices then click done. Click the advanced button to access the sensor properties.Select the light sensor. Select set to maximum sensitivity for calibration and click the save button. Select the occupancy sensor.Select a preset timeout value, or select custom and enter a value in minutes and seconds if needed. Click the save button. It is advisable to run a pilot test of the logger's deployment on the experimental site.Choose the most optional timeout value of occupancy sensor based on the actual occupancy frequencies of participants. Select when to launch the logger depending on the experimental plan:immediately, at intervals which is available when logging run time, on a specified date and time, or by manually using the start button. Select when the logger should stop logging:when memory fills, or never stop which results in the newest data overwriting the oldest, stop manually, or stop at a specified date and time.Click the start button upon finishing the configuration. Unplug the logger from the computer. Equip the logger with an additional fiber optic light pipe by connecting it to the back of the logger in order to filter out any ambient light and ensure the most accurate readings.Mount the logger with the light pipe next to the designated light source. If previously configured in the software, press the start button at the logger to begin recording. The placement of logger mounting should not be in proximity to any kind of heating sources to ensure that only the body heat of the room users will be recorded.After finishing recording, revisit the experimental site and remove the logger for the purpose of data read-out. Connect the logger via USB cable to the computer and launch the analysis software package dedicated to the data logger. Click the read-out device button from the control panel, or select to read-out from the device menu, which will enable the logger to unload the gathered data.Choose a location and the file name to save the data. Click save, and select the sensors and events to display in a graph and click plot. Select the series to view on the table data and plot.Click the all or none button to select or deselect all series, or click the checkboxes to select or deselect individual series. Select export table data from the control panel. Choose a destination folder for the export.Data loggers could serve as a useful tool for conducting field experiments in psychology. This table presents recorded data of light-switching behavior frequencies, as well as occupancy time of users that visited two public restrooms during a 15 day measurement. Data obtained by the logger can be analyzed by performing a logistic regression analysis for light status and restroom type.This table shows the coefficient of the built model. When following this procedure, researchers can additionally test the effectiveness of various behavioral interventions such as prompts with persuasive appeals aimed at fostering light-switching behavior. In addition to nominal outcomes of light and occupancy status, data logger allows to collect and analyze quantitative information about time of room occupancy status, as well as time between occupancy events.

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