The association between depression and digital media use (DMU) has received substantial research and popular attention in recent years. While meta-analytic evidence indicates that there is a small, positive relationship between DMU and depression, almost all studies rely on self-report measures of DMU. Evidence suggests these measures are poor reflections of usage measures derived from digital trace data. Additionally, a recent study showed that the error in self-reported DMU is likely biased systematically by factors that are fundamental to the effect being investigated: respondents’ volume of use and level of depression. The present study harnesses cubic response surface analysis—a novel analytical approach in this domain—to advance our understanding of how inaccuracies in self-report measures of DMU can be explained by respondent attributes, in this case their level of depression and actual iPhone usage. A sample of 325 iPhone users provided estimates of their total iPhone use over the past week, their actual iPhone use as recorded by the Apple Screen Time application, and a measure of their depression. The results of the analysis indicate that depression is (a) more strongly associated with estimated than device-logged DMU; (b) more associated with overestimating than underestimating of DMU; and (c) more associated with inaccuracy at lower versus higher levels of DMU. The findings raise important questions concerning the validity of conclusions in this area and provide insight into the structure of measurement error in self-report estimates of DMU.
Keywords: digital technology, depression, communications media, data accuracy, measurement error
Open Science Disclosures: Data, code, and supplementary material are openly available on the Open Science Framework at https://osf.io/mzywt/.
Conflicts of Interest: The authors have no conflicts of interest to disclose.
Funding: This study was supported by the Robert and Sally Schwartz Endowed Resource Fund, an internal University of Pittsburgh School of Social Work award. The funding source was not involved in the study design or the collection, analysis, or interpretation of data.
Interactive Content: 3D models (full cubic, RRCA and RRCL) using R markdown.
Correspondence concerning this article should be addressed to Craig J. R. Sewall, School of Social Work, University of Pittsburgh 2117 Cathedral of Learning, 4200 Fifth Ave, Pittsburgh, PA 15260, United States. Email: CJS227@pitt.edu
Rates of depression and digital media use (DMU) have increased over the previous decade (Pew Research Center, 2020; Weinberger et al., 2018), leading to an abundance of research investigating whether the two phenomena are related (see Dickson et al., 2019; Odgers & Jensen, 2020; and Orben, 2020a for comprehensive reviews on the subject). Meta-analytic evidence indicates that there is a small, positive relationship between DMU and depression (Liu & Baumeister, 2016; Yoon et al., 2019). Following the general trend in social psychology of using estimates of behavior as a proxy for actual behavioral measures (Sassenberg & Ditrich, 2019), most studies, however, rely on self-reported measures of DMU (Griffioen et al., 2020). When compared to more objective measures of DMU (i.e., digital trace data or device usage logs), such estimates are generally inaccurate (Parry et al., 2021). Crucially, evidence suggests that the error in self-reported DMU is likely biased systematically by factors that are fundamental to the effect being investigated: Respondents’ volume of use (Araujo et al., 2017; Boase & Ling, 2013; Ernala et al., 2020; Scharkow, 2016; Vanden Abeele et al., 2013) and level of depression (Sewall et al., 2020). The questionable validity of estimated DMU raises serious doubts about the validity of conclusions drawn from studies using these types of measures (Flake & Fried, 2020). Recent evidence, for instance, indicates that associations with various mental health outcomes differ substantially between self-reported and logged measures of DMU (Shaw et al., 2020). Given the level of academic and popular interest in this subject, this has major potential implications for policy recommendations and public perception, with measurement discrepancies likely contributing to inaccurate associations between DMU and mental health outcomes. Yet, due to methodological constraints in prior validation studies, our understanding of how individual differences in depression severity and volume of DMU impact the (in)congruency between self-reported and objective DMU remains limited. The present study harnesses a novel analytical approach to help address this limitation.
Although self-reported estimates are prevalent in studies of DMU, there is strong evidence that these measures do not capture what they are intended to measure: actual use (Parry et al., 2021). Rather, as is common with self-reports of behavior in many domains (see, e.g., Jenner et al., 2006; Kormos & Gifford, 2014), self-report measures of DMU capture respondents’ perceptions of their use rather than their actual use (Scharkow, 2016; Sewall et al., 2020). As such, the myriad factors that impact perception—as well as the other cognitive and affective processes that are called upon when estimating DMU—will influence respondents’ reports. In this way, self-report estimates of DMU may capture some of the respondent’s actual use—as is evident in the moderate correlations found between self-reported and device-logged DMU (Parry et al., 2021)—but also unintentionally capture elements of the respondent’s attitudes, perceptions, feelings, cognitions, etc. that are unrelated to actual use (Ellis, 2019).
The fact that depression causes impairments across cognitive, affective, and behavioral processes (American Psychiatric Association, 2013) means that there are a variety of ways that the processes involved with estimating DMU may be impacted by depression and lead to systematic bias. This was recently borne out in a study by Sewall et al. (2020) who found that depression was positively related to the amount of incongruence between self-reported and logged iPhone use. However, their analysis was constrained in three important ways: (a) it assumed that the incongruency—depression relationship was linear and thus did not test for higher-order effects; (b) by taking the absolute difference between estimated and actual iPhone use as a measure of error, they were unable to examine whether depression is differentially related to overestimation versus underestimation; and (c) they did not test whether incongruence at lower levels of use is differentially related to depression than incongruence at higher levels of use (i.e., interaction effects).
Extending the work of Sewall et al. (2020), the present study applies cubic response surface analysis (RSA; Humberg et al., 2020) to address the limitations of prior studies. Cubic RSA is a statistical approach that uses polynomial regression to estimate the response variable z from two predictor variables x and y, their higher-order terms, and their interactions (see Edwards, 2002; Edwards & Parry, 1993; Humberg et al., 2019). This approach is well-suited to investigate (in)congruence phenomena, where the level of (in)congruence between two commensurable variables is associated with an outcome variable, and overcomes the bias inherent to conventional approaches where the (absolute or squared) difference between the variables are correlated with an outcome (Edwards, 2002). As detailed by Humberg et al. (2019), RSA has been used to investigate hypotheses relating to the consequences of person-group similarity, dyadic similarity, person–environment fit, and self-other agreement. For example, Human et al. (2016) examined how the congruence between adolescent and parent perceptions of family dynamics relate to adolescents’ psychological adjustment; Franken et al. (2017) examined how parent–offspring personality similarities relate to offspring externalizing problems, and Barranti et al. (2017) investigated how self-other (dis)agreement about moral character is related to interpersonal costs.
While RSA has occasionally been used to explore congruence hypotheses in the psychological and social sciences, this is the first study to apply cubic RSA to the field of DMU studies. By mapping the complex patterns of associations between measurement inaccuracy and depression, the present study provides novel insight into how levels of depression and actual DMU may impact self-reported estimates of use in ways that the statistical methods employed in the original study by Sewall et al. (2020), as well as similar work in this area (e.g., Araujo et al., 2017; Boase & Ling, 2013; Ellis et al., 2019; Shaw et al., 2020), could not explicate. As such, the present study used cubic RSA in an exploratory manner to gain insight into several research questions (Table 1) that have important implications for understanding the nature of depression and self-reported DMU measurement error.
Estimated Cubic Polynomial Models, Their Specifications/Constraints, and Associated Research Questions
A.) Is depression severity more/less associated with over/underestimating actual use?
Z = β0 + β1 x + β2 y + β3 x 2 + β4 xy + β5 y 2 + β6 x 3 + β7 x 2 y + β8 xy 2 + β9 y 3 + ϵ
B.) Does the association between depression severity and (in)congruency vary across levels of smartphone use?
C.) Is depression severity associated with higher/lower levels of estimated or actual use?
A.) Is depression severity more/less associated with over/underestimating actual use?
Cubic asymmetric congruence (CA)
β1 = 0, β2 = 0, β4 = −2β3, β5 = β3, β7 = −3β6, β8 = 3β6, β9 = −β6
B.) Does the association between depression severity and (in)congruence vary across levels of smartphone use?
Level-dependent congruence (CL)
β1 = 0, β2 = 0, β4 = −2β3, β5 = β3, β7 = −β6, β8 = −β6, β9 = −β6
A.) Is depression severity more/less associated with over/underestimating actual use? AND
Rising ridge cubic asymmetric congruence (RRCL)
β1 = β2, β4 = −2β3, β5 = β3, β7 = −3β6, β8 = 3β6, β9 = −β6
aC2.) Is depression severity associated with higher/lower levels of smartphone use?
B.) Does the association between depression severity and (in)congruence vary across levels of smartphone use? AND
Rising ridge level-dependent congruence (RRCA)
β1 = β2, β4 = −2β3, β5 = β3, β7 = −β6, β8 = −β6, β9 = −β6
aC2.) Is depression severity associated with higher/lower levels of smartphone use?
Note. The full cubic model is unconstrained, all other models are nested under the full cubic model with constraints estimated as shown. RRCL = rising ridge cubic level; RRCA = rising ridge cubic asymmetry; CL = strict level-dependent congruence; CA = cubic asymmetry.
A more detailed description of the data collection methodology used for this study is provided elsewhere (Sewall et al., 2020). Briefly, participants (N = 399) were recruited from Amazon Mechanical Turk (MTurk) in late January to early February of 2019 to complete an online survey about iPhone use and well-being. Participants were eligible for the study if they (a) used an iPhone with iOS version 12 or later, (b) spoke English, (c) resided in the United States, (d) were ≥18 years old, and, to help ensure quality responses, (e) had a task acceptance rate of ≥95% (Peer et al., 2014). Participants were compensated $1.00 for completing the survey. This study was approved by the University of Pittsburgh Institutional Review Board.
Participants first provided numeric estimates of their total iPhone use over the past week, without consulting any applications that tracked their usage. Then, after navigating to their “Screen Time” application—an Apple application that automatically tracks iPhone usage metrics—they manually entered the amount listed for “Total Screen Time” (i.e., the total duration of active iPhone use over the past week) into the survey. Participants then completed the 10-item Center for Epidemiologic Studies Depression Scale-Revised (CESD-R-10; Haroz et al., 2014; Radloff, 1977), as well as the Satisfaction with Life Scale (Diener et al., 1985) and the eight-item UCLA Loneliness Scale (Hays & DiMatteo, 1987), which were not analyzed here.1 The CESD-R-10 presents 10 items characteristic of symptoms associated with depression (e.g., my sleep was restless, I felt like a bad person). Through a Likert-type scale with response options ranging from 0 (rarely or none of the time) to 3 (all of the time) the participants indicated how often they experienced each symptom over the preceding week. To produce a total score, the responses for each item are summed (items 5 and 8 are reverse-scored), with higher scores suggestive of greater depressive symptoms.
Following the data screening procedures described in Sewall et al. (2020), participants were dropped if they failed one or more attention checks (n = 57) or reported usage data >3 standard deviations (SD) outside the mean (n = 17), resulting in a final analytical sample of N = 325. We also checked for evidence of “straight-lining” (i.e., potentially inattentive responders who selected all minimum or maximum items across all scales), which yielded no additional exclusions. The independent variables—estimated total iPhone use (x) and actual total iPhone use (y)—were assessed on the same numerical scale (0–168 hr), which satisfies the requirement that the independent variables be commensurable for analyses of congruence effects. To ensure that the independent variables retained their commensurability, they were centered on their combined grand mean and scaled by dividing both variables by their combined grand SD. The dependent variable, depression (z), was left untransformed. There were no missing data.
We first examined the psychometric properties of the CESD-R-10 using unidimensional confirmatory factor analysis (CFA) with robust standard errors. Inspection of the factor loadings revealed that the two reverse-scored items loaded very poorly on the latent depression variable (Item 5 λ = 0.14; Item 8 λ = 0.30), while all other items loaded well (λs > 0.65). Dropping these two items resulted in substantially improved model fit (8-item CESD fit statistics (robust): Comparative Fit Index/Tucker-Lewis Index (CFI/TLI) = 0.99/0.98, Standardized Root Mean Square Residual (SRMR) = 0.03, Akaike Information Criterion (AIC) = 5692.36; 10-item CESD fit statistics (robust): CFI/TLI = 0.93/0.91, SRMR = 0.07, AIC = 7450.37). Thus, we used the sum score (range 0–24) for this eight-item scale as the measure of depression (McDonald’s (1999) reliability coefficient Ω = 0.91) in the RSA.2 See Supplemental Table S1 of the OSF repository for results of the CFA.
We calculated descriptive statistics for sample characteristics and primary variables. Additionally, in line with recent recommendations (Vanden Abeele et al., 2013), we computed the percentage error to describe the level of (in)accuracy of the self-reported estimates. Briefly, percentage error—calculated as x−y/y * 100%, where x = estimate and y = actual—accounts for the fact that a 10-hr discrepancy between estimated and actual weekly use, for example, is more substantial if the actual amount of use is 15 hr/week versus 75 hr/week. Furthermore, as opposed to absolute difference scores (cf. Sewall et al., 2020), percentage error can take positive or negative values; thus, preserving information about over- versus underestimation (Johannes et al., in press). We calculated descriptive statistics for percentage error and, due to the nonnormality of the percentage error variable (see below), computed Spearman’s correlation coefficient to examine the association between percentage error and depression severity.;
For the cubic RSA (Humberg et al., 2020), we fit five 3rd-order polynomial regression models to predict depression score (z) as a function of estimated (x) and actual iPhone use (y), their higher-order terms, and their interactions. Model equations, specifications/constraints, and how they map onto research questions for the present study are presented in Table 1. We used the corrected Akaike Information Criterion (AICc; Hurvich & Tsai, 1989)—which is sensitive to over− and underfitting and can compare nested and nonnested models (Burnham et al., 2011; Schonbrodt, 2016)—for model comparison. All analyses were completed in R version 4.02 (R Core Team, 2020) using the lavaan package (Rosseel, 2012) for the CFA, the semTools (Jorgensen et al., 2020) package to calculate coefficient Ω, and the RSA package (Schönbrodt & Humberg, 2020) for the RSA. Data management was completed with Stata version 16 (StataCorp, 2019). Data and code are available on the Open Science Framework at https://osf.io/mzywt/ (Sewall & Parry, 2021).
Descriptive statistics for the sample demographics and primary variables are provided in Table 2 and Table 3, respectively. Percentage error3 was right-skewed and kurtotic, with mean = 67.6% (median = −7.8%) and SD = 391.8%. The Spearman’s rank-order correlation between percentage error and depression was ρ = .21 (p < .01). Descriptive analysis of the levels of (in)congruence between estimated and actual iPhone use shows that 59% of estimates were roughly congruent (i.e., within 0.5 grand SD4 from one another), 22% were overestimated (i.e., estimated use exceeds actual use by >0.5 grand SD), and 19% were underestimated (i.e., actual use exceeds estimated use by >0.5 grand SD).
Sample (N = 325) Characteristics
American Indian/Alaskan Native
High school or less
Note. aMean (standard deviation) reported.
Summary Statistics for Primary Variables
Estimated total iPhone use
Actual total iPhone useb
Note. a Sum score from the Center for Epidemiologic Studies Depression Scale-Revised (CESD-R-10) after dropping Items 5 and 8. b Active iPhone use over the past week logged by the “Screen Time” application
Model comparison results are presented in Table 4.5 The full cubic model had the best fit to the data, followed closely by the rising ridge cubic level (RRCL) model. Overall, models that allowed for a rising ridge (i.e., full, RRCL, rising ridge cubic asymmetric [RRCA]) had a better fit to the data than those that did not (i.e., level-dependent congruence [CL], cubic asymmetric [CA]).
Model Comparison (N = 325)
Adjusted R 2
To aid interpretation, the response surfaces for the two best-fitting models were plotted using the plotRSA() function from the RSA package (see Figure 1).6 Visual inspection of the plot for the full cubic model reveals several effects. First, the slope along the x-axis (estimated use) rises more rapidly than the slope along the y-axis (actual use), indicating that depression is more strongly associated with estimated use than actual use. Second, the average distance from the line of congruence increases as usage increases, suggesting that heavier amounts of usage are associated with greater incongruence. Third, there is an asymmetric incongruence effect: Overestimating is more strongly associated with depression than underestimating. To illustrate, say person A overestimated their use by 2 units (i.e., estimated use = 2 on the x-axis and actual use = 0 on the y-axis) and person B underestimated by 2 units (i.e., estimated use = 0 and actual use = 2), the predicted depression score for person A is around 10 and the predicted depression score for person B is around 2.5. Finally, there is a level-dependent effect: The association between depression and incongruence depends on the level of usage. Specifically, at low(er) levels of usage (i.e., estimated and actual use < 2) the shape of the response surface is concave up and flattens out and eventually becomes concave down as usage level increases—suggesting that the effect of depression on incongruence may be stronger at lower levels of use. For parameter estimates of the full and RRCL models, see Supplemental Table S2 of the OSF repository.
To advance our understanding of how inaccuracies in self-report measures of DMU can be explained by respondent attributes, this study investigated how respondents’ level of depression and actual volume of DMU impact their self-reported estimates of DMU. Leveraging a novel analytical approach to extend earlier work, our findings indicate that depression is (a) more strongly associated with estimated than device-logged DMU; (b) more associated with overestimating than underestimating of DMU; and (c) more associated with inaccuracy at lower versus higher levels of DMU. Given the broad interest in the potential link between DMU and depression, and the widespread reliance on self-reported estimates to measure DMU, these findings raise important questions concerning the validity of conclusions in this area. More optimistically, the findings also represent a step toward understanding the structure of measurement error and the factors that account for aspects of this error. However, given the exploratory nature of this analysis and the limitations detailed below, the results of this study should be interpreted with caution. Future confirmatory studies that are well-powered are needed to test the robustness and transferability of these results.
The structure of measurement error is an important area of research as, with few exceptions, investigations of the effects of DMU on various well-being indicators are, first, correlational and, second, reliant on self-reports of DMU (Griffioen et al., 2020). Although both random and nonrandom measurement error can lead to either inflated or attenuated effects, random error in self-reports generally attenuates correlational effects (Kobayashi & Boase, 2012; Schimmack & Carlsson, 2017). If the inaccuracies in DMU self-reports are primarily indicative of random errors, it is likely that effect size estimates are attenuated. This would suggest that current evidence for a positive relationship between DMU and depression is conservative. In contrast, if the inaccuracies in DMU self-reports are indicative of systematic error, correlational effect sizes are at the very least biased and, depending on the nature of the nonrandom error, effects could either be inflated or attenuated. While some studies provide evidence for random error (Jones-Jang et al., 2020), the results of this analysis corroborate findings showing that specific respondent attributes and DMU characteristics are systematically related to measurement error in DMU estimation and that this has likely inflated correlational effects (Araujo et al., 2017; Kahn et al., 2014; Kobayashi & Boase, 2012; Scharkow, 2016; Sewall et al., 2020; Shaw et al., 2020).
Two findings from the present study provide support for the notion that the DMU—depression effect may be inflated in studies that rely on self-reported estimates of DMU. First, as was also demonstrated by Shaw et al. (2020), self-reported DMU is more strongly associated with depression than logged use and, second, depression is associated with overestimating. The indication that depression may be linked to overestimating DMU is particularly noteworthy, as this would suggest that, on average, the relationship between DMU and depression is artificially inflated in studies relying on self-reported estimates of DMU.
That depressed respondents would exhibit this pattern of systematic bias in self-reports is supported by the phenomenology of depressive symptoms and how these impairments interact with the perceptual and self-referential processes involved in the estimation of DMU. It is well-established that self-reports of behavior reflect what people believe they do rather than what they actually do (Scharkow, 2016; Schwarz & Oyserman, 2001). Acknowledging this, Sewall et al. (2020) framed estimated DMU as a measure of perceived use rather than actual use. Therefore, participants’ estimates of their DMU are not only impacted by their beliefs about their usage but, more importantly, their estimates are impacted by their perception and self-awareness, as well as the cognitive, affective, and behavioral factors that impact these functions. Many of the same perceptual and self-referential processes that are involved with estimating DMU are also those that are impaired by depression (American Psychiatric Association, 2013). Notably, depression can alter individuals’ perception of time (Droit-Volet, 2013). While results are mixed, meta-analytic evidence suggests that depression can engender a subjective slowing of time (Thönes & Oberfeld, 2015). This distorted perception of time, in addition to other attention-related impairments associated with depression, may explain the association between depression and the overestimation of DMU. Additionally, given the negative affective and cognitive biases associated with depression (Bradley & Mathews, 1983; Pyszczynski et al., 1989), it is plausible that depressed respondents interpret their DMU negatively. When asked to estimate their DMU they may think (for example) “I am always on my iPhone” and, therefore, overestimate their DMU. This negative bias may also interact with common beliefs in the cultural milieu, such as the moral panic surrounding the putative effects of DMU on well-being (Orben, 2020b), making it more likely that depressed respondents hold negative beliefs about their DMU. Finally, if actual DMU is positively associated with depression, then the factors causing systematic bias among high-volume users would also be more likely among depressed respondents.
Despite mounting evidence for the inaccuracy of estimates of DMU (Parry et al., 2021), given the difficulties and expenses involved in the collection of more objective device-logged data (Jürgens et al., 2019), especially in large-scale longitudinal studies, it is likely that most research involving DMU will continue to rely on self-report measures in some form. As we learn more about the structure of measurement error in estimates of DMU, it may become possible to implement error-correction models (Guolo, 2008) to correct for the incongruence between perceived and actual measures of DMU. These methods may allow for the continued use of self-reports in contexts in which the use of more objective measures is infeasible, while helping to correct for factors that make self-report measures inaccurate.
While the identification of factors that systematically relate to inaccurate estimates of DMU holds promise for the recalibration of prediction models, it is not without its challenges. First, as the cubic RSA demonstrated, the relationships between various respondent characteristics and measurement incongruence are likely nonlinear. In the present study, while overestimating of DMU was generally more strongly associated with depression, the analysis also revealed a level-dependent effect. At higher levels of DMU, underestimating was more associated with depression than overestimating. Consequently, when attempting to correct for various individual differences, it will likely be necessary to account for higher-order relations beyond linear interactions. A second challenge concerns the possibility that different measures of psychosocial variables might impact the outcomes of these analyses. Frequently, the same psychosocial construct can be measured by many distinct instruments and outcomes may differ substantially depending on the measure (Flake & Fried, 2020). A final challenge concerns the impacts of within-group variability on recalibration efforts. Though respondents may belong to specific groups—such as a psychiatric diagnosis or socioeconomic characteristic—the heterogeneity within these groups may cause recalibrations to attenuate error for some respondents while inflating it for others.
The results and generalizability of this investigation should be considered in the context of the following limitations. First, as noted in Sewall et al. (2020), the data were collected from a convenience sample of MTurk workers residing in the United States who owned an iPhone. While such samples are common in the social sciences, they are not without shortcomings. Research has shown that, compared to nationally representative samples, MTurk workers are roughly twice as likely to screen positive for depression (Walters et al., 2018). Additionally, evidence from the United Kingdom indicates that, compared to Android users, iPhone users are more likely to report higher levels of emotionality (Shaw et al., 2016). Given these factors, further research is needed to assess how our findings extend to other samples more representative of the general population. Research is also needed to evaluate whether our findings hold for specific demographics and, given our focus on depression, clinical populations. Relatedly, future confirmatory research should investigate patterns of (in)congruence across discrete operationalizations of depression, which would provide insight into whether there are latent thresholds of depression severity whereby systematic error becomes more prominent. An additional limitation is the sample size. While there currently are no guidelines for determining the sample size requirements for RSA, Humberg et al. (2020) note that, compared to the sample size needed to detect small to medium second-order effects, the sample required for cubic RSA would be substantially larger. Thus, in future studies seeking to confirm the findings presented herein, large samples would be required.
A second limitation concerns measurement. Although we implemented various data screening procedures, we did not independently verify that the actual iPhone use data the participants supplied corresponded to the figures provided by their Screen Time application. Therefore, despite similarities in usage statistics with other studies that collected logged usage data (Ellis et al., 2019; Jones-Jang et al., 2020; Ohme et al., 2020; Shaw et al., 2020), to address the possibility that some participants may have misreported their usage, our analysis should be replicated with data derived directly from actual usage measures (e.g., screenshots or direct data capturing). In the present study, self-reported DMU was collected through a single numeric estimate of total iPhone use over the past week. Studies have shown that the incongruence between logged and self-reported DMU is larger in open-ended questions than in closed questions and that estimates over a week are less accurate than estimates for a single day (Boase & Ling, 2013; Ernala et al., 2020). While open-ended estimates are commonplace and many studies assess weekly usage, how the current findings extend to other response periods and formats requires further study. Finally, across both the self-reported and logged measures of DMU, our data only concern overall usage of a single device. Outside the context of smartphone data, there is limited evidence of the incongruence between logged and self-reported measures of DMU. What evidence there is, however, suggests a similar level of incongruence (Parry et al., 2021). More research is needed to determine if the present results would hold for use of other devices (e.g., laptops) or use of specific platforms or services (e.g., social media).
Additionally, our measure of depression (the CESD-R-10) may have had an impact on the observed outcomes in two ways. First, acknowledging the substantial heterogeneity in depression symptoms, Fried (2017) demonstrates that there is little overlap among common depression scales. Particularly, based on the finding that the CESD (i.e., the full-scale version of the CESD-R-10) exhibits low overlap with the six other scales examined, Fried (2017) notes that findings identified with this scale are less likely to generalize to other depression measures. Consequently, to determine if different measures of depression lead to different response surfaces, the present analysis should be replicated with other relevant rating scales for depression and among clinical samples. Second, given that we used a self-report measure of depression, the validity and reliability of the depression scores observed in the study are subject to many of the flaws that limit self-report measures in general (e.g., recall and desirability bias).
The findings of this study suggest that the cognitive, affective, and behavioral impairments indicative of depression are likely important covariates to account for when seeking to correct for errors in DMU estimates. Given confirmation of the patterns of association found in this investigation in well-powered and well-designed studies, it may be possible to recalibrate respondents’ self-reported estimates by their level of depression to adjust for the systematic error that is prevalent among this group. Furthermore, the methods adopted in the present study can be extended to investigate whether other respondent characteristics (e.g., other psychopathologies or socioeconomic characteristics), types of DMU (e.g., social media or internet use), or question characteristics (e.g., closed vs. open-ended responses) systematically relate to inaccurate estimates of DMU. Such investigations will contribute to the development of a more nuanced conception of respondents’ perceptions of their DMU, further inform our understanding of the structure of measurement error and, dependent on the outcomes, support the development of methods to improve the accuracy of analyses involving self-reported DMU. While this study considered a specific incongruence phenomenon, given the general reliance on self-reports of behavior in DMU studies (Griffioen et al., 2020) and in the social sciences in general (Sassenberg & Ditrich, 2019), the analytical approach and findings presented herein suggest a promising role to be played by digital tools in the process of construct development and measurement validation across the sociobehavioral sciences.
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