Manipulated vs. Measured: Using an Experimental Benchmark to Investigate the Performance of Self-Reported Media Exposure

Jerit, Jennifer; Barabas, Jason; Pollock, William; Banducci, Susan; Stevens, Daniel; Schoonvelde, Martijn

doi:10.1080/19312458.2016.1150444

Cited by 15 publications

(15 citation statements)

References 44 publications

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“…Instead, we found that self-reported accuracy was statistically similar regardless of whether or not farmers received detailed information about how their data would be used for satellite yield estimation and about potential study benefits. These findings support a previous study where authors did not find a significant difference in the accuracy of self-reported responses when study design explanations were framed differently (Jerit et al, 2016). It is important to note, however, that our intervention was limited.…”

Section: Discussionsupporting

confidence: 90%

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

Paliwal

Jain

2020

Front. Sustain. Food Syst.

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Despite recent advances in remote sensing, one of the major constraints that still remains is collecting the ground data needed to calibrate and validate remote sensing algorithms at large spatial and temporal scales. This is particularly challenging when mapping continuous variables such as yield, where calibration data often do not exist at the field-scale and are difficult to obtain through visual interpretation of high-resolution imagery. While crop cut estimates of crop yield are widely used to calibrate satellite yield estimation models, these data are time and cost intensive to collect. In this study, we examine the ability of self-reported yield estimates, which are much faster and easier to collect at large scales, to train satellite yield estimation models. We assess the accuracy of self-reported yield data and identify whether it is possible to increase self-reported accuracy by providing more information to farmers about the study design and potential benefits. Our results showed that farmers' self-reported crop yields were not accurate, and that self-reported crop yields led to inaccurate satellite yield estimation models when used for calibration. We also found that providing more information to farmers about the study design and benefits of satellite yield estimation did not improve self-reported accuracy. These results suggest that even though self-reported yield estimates may be a faster and lower cost way to collect field-level yield estimates, they likely are not an adequate data source to train satellite yield prediction models and should be used with caution.

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Section: Discussionsupporting

confidence: 90%

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

Paliwal

Jain

2020

Front. Sustain. Food Syst.

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“…As digital media platforms become central to the mass media landscape, health communication campaigns are increasingly using these digital platforms to efficiently deliver messages to their target audience. Given the challenges associated with objectively measuring exposure to digital content, such as restrictions put in place by social media companies and users' concerns about privacy, researchers continue to rely on self-reported measures to estimate exposure to ad campaigns [34]. Our results indicate that self-reported ad recall provides a meaningful measure of exposure to public health messages delivered on digital platforms.…”

Section: Discussionmentioning

confidence: 83%

Validating Self-Reported Ad Recall as a Measure of Exposure to Digital Advertising: An Exploratory Analysis Using Ad Tracking Methodology

Romberg

Bennett

Tulsiani

et al. 2020

IJERPH

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Many mass media campaigns aimed at changing young people’s health behavior air on digital platforms rather than on broadcast media (e.g., television), given the intended audience’s preference for web-based communication. While research suggests self-reported ad recall correlates with exposure to television advertising, it remains unclear whether self-report measures are correlated with exposure to digital advertising. This study examined the association between an objective measure of digital ad exposure and self-reported recall of digital ads from the truth® tobacco prevention campaign. Digital ad tracking methodology was employed to identify members of an online panel (ages 18−34) who had been exposed to ads during their regular web browsing. Demographics of exposed participants were used to develop a matched control group of non-exposed panel members. Members of the Exposed group (n = 458) and matched Control participants (n = 506) were surveyed on recall of truth ads, media use, and demographics. Results indicated that Exposed participants had significantly higher odds of reporting ad recall compared to Control participants. With each additional ad exposure, the odds of self-reporting higher frequency of ad exposure increased by 8% (OR = 1.08, 95% CI = 1.01−1.16). Findings suggest self-reported measures of ad recall are a valid measure of campaign exposure in a digital media environment.

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“…If available, population averages can provide useful cues for respondents to accurately estimate the frequency of their own behavior (Burton & Blair, 1991;Prior, 2009a;Schwarz & Oyserman, 2001). Alternatively, cues on individual situations of media use might improve response accuracy (Jerit et al, 2016;Potts & Seger, 2013). The internet is typically used at scattered moments throughout the day and oftentimes for incidental aims, such as checking e-mails or the news.…”

Section: Factors Influencing Response Accuracymentioning

confidence: 99%

“…Second, the use of anchor questions that primed the individual context of internet use (Jerit et al, 2016;Potts & Seger, 2013) was tested with an experimental design. Triggering respondents to think about their previous day and week was not found to facilitate more accurate estimations and reports.…”

Section: No Differencesmentioning

confidence: 99%

How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use

Araujo

Wonneberger

Neijens

et al. 2017

Communication Methods and Measures

172

149

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How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use Araujo, T.B.; Wonneberger, A.; Neijens, P.C.; de Vreese, C.H. : 10.1080/19312458.2017.1317337 Link to publication Published in: Communication Methods and Measures DOI Citation for published version (APA):Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use. Communication Methods and Measures, 11(3), 173-190. DOI: 10.1080173-190. DOI: 10. /19312458.2017 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Given the importance of survey measures of online media use for communication research, it is crucial to assess and improve their quality, in particular because the increasingly fragmented and ubiquitous usage of internet complicates the accuracy of self-reported measures. This study contributes to the discussion regarding the accuracy of self-reported internet use by presenting relevant factors potentially affecting biases of self-reports and testing survey design strategies to improve accuracy. Combining automatic tracking data and survey data from the same participants (N = 690) confirmed low levels of accuracy and tendencies of over-reporting. The analysis revealed biases due to a range of factors associated with the intensity of (actual) internet usage, propensity to multitask, day of reference, and the usage of mobile devices. An anchoring technique could not be proved to reduce inaccuracies of reporting behavior. Several recommendations for research practice follow from these findings.

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Manipulated vs. Measured: Using an Experimental Benchmark to Investigate the Performance of Self-Reported Media Exposure

Cited by 15 publications

References 44 publications

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

The Accuracy of Self-Reported Crop Yield Estimates and Their Ability to Train Remote Sensing Algorithms

Validating Self-Reported Ad Recall as a Measure of Exposure to Digital Advertising: An Exploratory Analysis Using Ad Tracking Methodology

How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use

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