Time Use Surveys: a Review of their Aims, Methods, and Results

Chenu, Alain; Lesnard, Laurent

doi:10.1017/s0003975606000117

Cited by 39 publications

(27 citation statements)

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“…Light diaries may be a simpler, self-instructing instrument, and are a sufficient alternative method for respondents that are able to read but not write. Time diaries have their critics as well: Chenu and Lesnard (2006) argue that time diaries are also exposed to normative bias by the underreporting of “extra-occupational” activities like flirting or drinking.…”

Section: Section 2 Literaturementioning

confidence: 99%

“…3 In addition, researchers have found that open-ended recall methods that do not cap hours tend to overestimate time use. Hawes et al (1974), Verbrugge and Gruber-Baldine (1993) and Robinson and Godbey (1999) all find that the average daily (or weekly) activity times summed to greater than 24 (or 168) hours, although Chenu and Lesnard (2006) find evidence of underreporting of hours using the recall method.…”

Section: Section 2 Literaturementioning

confidence: 99%

“…Low literacy rates make self-completed contemporaneous diaries impossible, thus forcing researchers to rely on recall-based methods. In addition, rural and poor populations may not commonly use watches and clocks, casting doubt on the accuracy of simple (and widely-used) questions like, “How many minutes did you spend on that activity?” (Paolisso and Hame 2010; Chenu and Lesnard 2006; Reynolds 1991). …”

Section: Section 1 Introduction1mentioning

confidence: 99%

“…Robinson (1985) found that respondents over-reported socially desirable activities and under-reported activities of short duration. Chenu and Lesnard (2006) reported that people overestimated time spent on paid employment by understating interruptions. Several other authors, however, have found this problem to be minimal and unsystematic (Juster and Stafford 1985; Robinson 1977; Robinson 1985; Szalai 1973; Bonke 2005; Robinson and Godbey 1999).…”

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Pictorial Approaches for Measuring Time Use in Rural Ethiopia

et al. 2012

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Time use researchers working in least developed countries (LDCs) face difficulties collecting data from illiterate populations who may conceptualize time differently than those in industrialized countries. We identify existing gaps in time use data collection methods and discuss two novel, pictorial methods to collect time use data from these populations. The first method is a modified recall interview modeled on participatory rural appraisal (PRA) methods that asks respondents to place macaroni on pictures of activity categories in proportion to the amount of time spent on that activity during the previous day. The second is a simplified pictorial time diary that uses a timer and sequentially-numbered stickers to re-create the temporal order of activities in 30-minute increments. The latter method also avoids recall bias problems. We present time use data collected in 2009 using these methods in a study examining the impacts of water infrastructure on women and children’s time use in rural Ethiopia. In total, we collected information using the first method from 263 household members over age 10, including 167 water collectors, and pilot-tested the pictorial diary approach with 10 adult respondents.

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Section: Section 2 Literaturementioning

confidence: 99%

Section: Section 2 Literaturementioning

confidence: 99%

Section: Section 1 Introduction1mentioning

confidence: 99%

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confidence: 99%

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Pictorial Approaches for Measuring Time Use in Rural Ethiopia

et al. 2012

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“…Quantitative methods such as questionnaires and surveys capture qualitative information in formalised ways for computational processing, and are widely used in large scale studies on demographics, household economics and social attitudes (e.g., [11,12]). Time-use diaries are also used to log activity sequences [13], and to seek evidence of life changes and social evolution [14]. Efforts to harmonise time use surveys across Europe have delivered guidelines (HETUS) [15] on activity coding for analysing the time use data, but interviews and observations are commonly used to cross-validate what goes on, and to calibrate and amplify the meaning of the diary evidence, including the use of activity sensors and video cameras [16].…”

Section: Introductionmentioning

confidence: 99%

Using Sensors to Study Home Activities

Jiang

Pozza

Gunnarsdóttir

et al. 2017

JSAN

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Abstract:Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.

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Time: Gershuny’s Changing Times

Southerton¹

2009

Doing Social Science

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Time Use Surveys: a Review of their Aims, Methods, and Results

Cited by 39 publications

References 0 publications

Pictorial Approaches for Measuring Time Use in Rural Ethiopia

Pictorial Approaches for Measuring Time Use in Rural Ethiopia

Using Sensors to Study Home Activities

Time: Gershuny’s Changing Times

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