Meta-Psychology, 2022, vol 6, MP.2020.2538, https://doi.org/10.15626/MP.2020.2538 Article type: Replication Report Published under the CC-BY4.0 license Open data: Yes Open materials: Yes Open and reproducible analysis: Yes Open reviews and editorial process: Yes Preregistration: Yes Edited by: Rickard Carlsson Reviewed by: Evelina Thunell & Sarahanne Field Analysis reproduced by: Lucija Batinović All supplementary files can be accessed at the OSF project page: https://doi.org/10.17605/OSF.IO/KVGWR Super-Size Me: An Unsuccessful Preregistered Replication of the Effect of Product Size on Status Signaling Burak Tunca Department of Business Administration, Lund University School of Economics and Management burak.tunca@fek.lu.se Ignazio Ziano Department of Marketing, Grenoble Ecole de Management, ignazio.ziano@grenoble-em.com Wenting Xu Department of Marketing, Grenoble Ecole de Management, Wenting.XU@grenoble-em.com Dubois, Rucker, and Galinsky (2012, Experiment 1) found that consumers view larger-size options as a signal of higher status. We conducted a close replication of this finding (N = 415), and observed a nonsignificant effect in the opposite direction (small vs. large prod- uct size: doriginal = 1.49, 95%CI [1.09, 1.89], dreplication = 0.09 95%CI [-0.15, 0.33]; medium vs. large: doriginal = 0.89 95%CI [0.52, 1.26], dreplication = 0.11 95%CI [-0.13, 0.34]; small vs. me- dium: doriginal = 0.62 95%CI [0.26, 0.98], dreplication = -0.01 95%CI [-0.25, 0.23]). We discuss potential reasons for this unsuccessful replication as well as implications for the status- signaling literature in consumer psychology. Keywords: status, inferences, product size, replication, open science Why do consumers choose larger portions, for example at a fast-food restaurant, despite the po- tential negative consequences of such choices on their health and well-being? Given that larger por- tion sizes give rise to increased consumption (i.e, the portion size effect; Vandenbroele et al., 2019; Zlat- evska et al., 2014), understanding the factors influ- encing portion choice is of importance to efforts in reducing excessive eating-related problems like obesity. Various underlying mechanisms of prefer- ences for larger portions are available in the litera- ture. These explanations include value for money (larger portions have a lower price per unit), mind- less eating (consumers are inattentive to their choices, for example while dining with others), and estimation bias (consumers are often uninformed about reference portion sizes and they fail to esti- mate appropriate amount to eat; for a review, see Steenhuis & Poelman, 2017). To add to these expla- nations, Dubois et al. (2012) presented a novel pos- tulation: consumers choose larger-sized food op- tions to signal social status. Put simply, people do not choose larger portions to satiate the need for food, but to satiate the need for status. The evidence for this novel hypothesis attracted attention. At the time of writing, the study has been cited over 290 times on Google Scholar, and also ap- peared in mainstream media such as The New York Times (Warren, 2011), Scientific American (Grewal, 2011), and The Atlantic (Villarica, 2011). To illustrate the extent of publicity, Warren (2011) reported the study in The New York Times as a “fascinating re- search that links obesity with status” and proposed that anti-obesity campaigns could benefit from al- tering the size-to-status relationship. While the im- pact, theoretical importance, and relevance of Du- bois et al.’s (2012) research make it a worthy candi- date for replication, Field et al. (2019) recommend also evaluating the statistical findings in the original work to judge the necessity of a replication. Following the guidelines in Field et al. (2019), we converted the reported statistical differences among portion sizes in the original study into Bayes 2 TUNCA, ZIANO, AND XU factors using the BayesFactor package in R statisti- cal software (Morey et al., 2018). Dubois et al. (2012) reported that perceived status was higher in the large choice versus small choice (t(182) = 4.66, p = .001, d = 1.10), large choice versus medium choice (t(182) = 2.95, p = .01, d = .65), and medium choice versus small choice (t(182) = 2.27, p = .05, d = .46). When converted into Jeffreys-Zellner-Siow Bayes factors (see Field et al., 2019, p. 2), these differences corresponded to Bayes factors of 2026 (large vs. small), 5 (large vs. medium), and 1 (medium vs. small). According to the classification recommended by Field et al. (2019), while the difference between large and small portion sizes show very strong evidence for the effect (i.e., the data are 2026 times more likely to occur under the alternative hypothesis rel- ative to the null hypothesis), the Bayes factor values drop substantially for the remaining comparisons and indicate ambiguous evidence for the compari- son between medium and small size portions with a Bayes factor of 1. We therefore argue that these sta- tistical discrepancies in the original results also war- rant replication. Despite the abovementioned implications of Du- bois et al.’s (2012) findings for marketing theory as well as policymaking, independent replications of this study are not available in the literature, to the best of our knowledge. In this replication report, we present a preregistered direct replication of the first experiment in Dubois et al. (2012), which tested the primary hypothesis that choosing larger sizes in a set of food options is associated with greater status but does not significantly influence other dimen- sions that are not related to status such as perceived niceness, honesty, or attractiveness. Method Choice of direct replication Direct replications strive to keep as many factors as possible identical to the original (e.g., survey pro- cedure, wording, measures), while conceptual repli- cations aim to test the central notion of a past find- ing, for instance by changing the procedure or the stimuli involved. While some authors argue that rep- lications should focus on conceptual extensions of original hypotheses (e.g., Lynch et al., 2015; Stroebe & Strack, 2014), Simons (2014) contends that the space of possible effects that can moderate a finding is infinite and all findings, reliable or unreliable, can be attributed to some unidentified moderators, thereby hindering accumulation of scientific evi- dence. Thus, Simons (2014) maintains that the pur- pose of replications is to determine reliability of findings, which can be achieved most successfully via direct replications by independent researchers. In accord with this perspective, we chose to conduct a direct replication to test the primary hypothesis in Dubois et al. (2012) and examine the reliability of the portion size effect on status perceptions. Open science practices Data collection and analyses plans were prereg- istered prior to data collection (see https://aspre- dicted.org/BUC_HUQ). Data, analyses, and materials are available at https://osf.io/ue458/. Design and sample As with the original experiment (Dubois et al., 2012, Study 1), our replication comprised a 3 (size of observed choice: small, medium, large) x 3 (product: coffee, pizza, smoothie) x 2 (dimension: status, non- status) mixed design with size and product as be- tween-subject factors and dimension as a within- subject factor. Participants were randomly assigned to one of nine conditions. The original experiment was based on 183 under- graduates (74 males); data for the replication exper- iment were collected from the Amazon MTurk par- ticipant pool (N = 415, 214 males, Mage = 37.9, SDage = 11.8). Participants received $0.50 as compensation. Power analyses indicated that our sample size had 99% power to detect the original effect size of prod- uct size on status inferences and 99.9+% power to detect the original mixed interaction effect between product size and status dimensions (alpha level of 3 SUPER-SIZE ME: AN UNSUCCESSFUL PREREGISTERED REPLICATION 5%; see supplementary materials for effect size con- version and power analyses). Procedure and Measures Identical to the original study, participants were asked to read about a consumption scenario in which a consumer chose small, medium, or large option for one of three products (coffee, pizza, or smoothie). While price was not presented in the pizza and smoothie conditions, the coffee was pre- sented as free. An example scenario is presented below: “You’re at a local smoothie shop. An individual enters in the smoothie shop, and asks for a smoothie. The cashier explains to him that the smoothies come in three sizes: small (16 oz), medium (24 oz) and large (30 oz), and asks him which size he would like to choose. The individ- ual orders the largest size.” Following the consumption scenario, partici- pants were asked to make judgments of the target person on status-related (this person has high sta- tus, is respected; a = 0.75) and nonstatus-related (this person is honest, nice, attractive; a = 0.80) di- mensions. These judgments were recorded with a 7-point Likert-type scale (1 = “strongly disagree” to 7 = “strongly agree”). Participants also responded to an attention check, in which they had to identify which among six traits had been presented to them (Roughness, Ruggedness, Niceness, Brutality, Suavity, Intelli- gence). 338 participants correctly identified “nice- ness” in the attention check (171 males, 167 females, Mage = 39.05, SDage = 11.90). Excluding the partici- pants who failed to identify the attention check did not affect the results. To maximize statistical power and give the original effect the best chance to be detected, we chose to report all data without any exclusion (results obtained excluding partici- pants who failed the attention check are available on the project OSF page). Results Main Analyses Descriptive statistics for each condition and measure are presented in Table 1. We first analyzed the full 3 (size) x 3 (product) x 2 (dimension) mixed- factorial model using repeated measures ANOVA. Note that this analysis for the entire design has not been reported in the original study. As seen in Ta- ble 2, the only significant effect was the main effect of dimension, such that, regardless of the experi- mental condition, participants rated the target consumers lower on the status-related dimensions (M = 4.43, SE = 0.05) than the nonstatus-related di- mensions (M = 4.77, SE = 0.05; t(406) = 9.56, p < .001). Next, following the analysis steps in the orig- inal study, we collapsed the different product sce- narios and conducted a 3 (size) x 2 (dimension) re- peated measures ANOVA. Again, the main effect of dimension was significant (F(1, 412) = 91.69, p < .001, η²p = 0.18), indicating that participants rated the in- dividual higher on nonstatus compared to status dimension; yet, more pertinent to the main find- ings of the original study that larger size choices would be associated with perceived status, the in- teraction between size and dimension was not sig- nificant (F(2, 412) = 0.83, p = .435, η²p < 0.01; original result: F(1, 177) = 4.06, p = .03, η²p = 0.05). For sta- tus-related dimensions, choice size did not have any effect (F(2, 412) = 0.48, p = .620, η²p = 0.002). As seen in Figure 1, perceived status of the consumer did not differ across small (M = 4.46, SE =0.10), me- dium (M = 4.48, SE = 0.09), and large (M = 4.36, SE = 0.09) choice conditions (for comparisons, all ps > .350). Similarly, judgments on nonstatus-related dimensions were also not affected by the choice size manipulation (F(2, 412) = 0.94, p = .391, η²p = 0.005; Msmall = 4.86, SE = 0.09; Mmedium = 4.77, SE = 0.08; Mlarge = 4.70, SE = 0.08; for comparisons, all ps > .170). 4 TUNCA, ZIANO, AND XU Table 1 Means and standard deviations (in parentheses) for status- and nonstatus-related dimensions across exper- imental conditions. Status-related dimensions Nonstatus-related dimensions High Status Respected Status (com- bined) Honest Nice Attractive Nonstatus (com- bined) Coffee: Small (n = 40) 4.47 (1.32) 5.10 (1.06) 4.79 (0.93) 5.42 (1.11) 5.45 (1.22) 4.72 (1.11) 5.20 (0.84) Medium (n = 49) 4.35 (1.35) 4.63 (1.27) 4.49 (1.24) 4.96 (1.22) 4.86 (1.32) 4.47 (1.26) 4.76 (1.14) Large (n = 48) 4.21 (1.29) 4.42 (1.18) 4.31 (1.14) 4.90 (1.26) 4.56 (1.13) 4.31 (1.07) 4.59 (0.97) Smoothie: Small (n = 45) 3.98 (1.36) 4.64 (1.17) 4.31 (1.15) 5.00 (1.04) 4.91 (1.02) 4.67 (1.26) 4.86 (0.91) Medium (n = 43) 4.33 (1.34) 4.65 (1.31) 4.49 (1.21) 4.98 (1.26) 4.93 (1.08) 4.51 (1.26) 4.81 (1.03) Large (n = 46) 4.07 (1.20) 4.57 (1.42) 4.32 (1.11) 4.98 (1.29) 4.89 (1.27) 4.33 (1.28) 4.73 (1.10) Pizza: Small (n = 43) 4.21 (1.17) 4.44 (1.03) 4.33 (0.99) 4.72 (1.18) 4.58 (0.96) 4.37 (0.93) 4.56 (0.77) Medium (n = 53) 4.34 (1.33) 4.58 (1.18) 4.46 (1.17) 4.98 (1.32) 4.83 (1.22) 4.40 (1.23) 4.74 (1.13) Large (n = 48) 4.33 (1.46) 4.56 (1.11) 4.45 (1.12) 4.92 (1.18) 4.90 (1.06) 4.50 (1.15) 4.77 (0.98) Average Small (n = 128) 4.21 (1.29) 4.72 (1.12) 4.47 (1.05) 5.04 (1.14) 4.97 (1.12) 4.59 (1.11) 4.87 (0.88) Medium (n = 145) 4.39 (1.33) 4.62 (1.24) 4.48 (1.20) 4.97 (1.26) 4.87 (1.21) 4.46 (1.24) 4.77 (1.10) Large (n = 142) 4.20 (1.32) 4.51 (1.24) 4.36 (1.12) 4.93 (1.24) 4.78 (1.16) 4.38 (1.17) 4.70 (1.01) 5 SUPER-SIZE ME: AN UNSUCCESSFUL PREREGISTERED REPLICATION Table 2 ANOVA summary table for the effects of product type, portion size, and status dimensions. F p η²p Within-subjects effects Dimension 91.33 < .001 0.18 Dimension*Size 0.78 0.461 <0.01 Dimension*Product 1.57 0.209 0.01 Dimension*Size*Product 0.54 0.708 0.01 Residual Between-subjects effects Size 0.74 0.476 <0.01 Product 0.73 0.482 <0.01 Size*Product 1.58 0.179 0.02 Residual Figure 1 Comparisons of status- and nonstatus-related dimensions across different product sizes. 6 TUNCA, ZIANO, AND XU In Table 3, we also present a comparison be- tween the results of the original study and the rep- lication study, including following the replication classification of LeBel et al. (2019). Although the original study reported rather large effects of product size on status perceptions, in the replica- tion study effects were nonsignificant in the oppo- site of the hypothesized direction (status percep- tions were lowest in the large product size condi- tion). Table 3 Comparisons for status-related dimensions between the replication and original study. Note. see supplementary materials for a note on how the original effect sizes were recalculated Supplementary Bayesian Analyses Because frequentist statistics and interpretation of p-values are generally not informative in quanti- fying evidence for the null hypothesis, Bayesian analyses are recommended for establishing evi- dence of absence (Keysers et al., 2020). We therefore supplemented our analyses with Bayes factors, which provide us the plausibility of the observed data under different models employing the null ver- sus alternative hypothesis. These were not prereg- istered. Open source software JASP was used to conduct the Bayesian ANOVAs reported in this sec- tion (Bergh et al., 2020; JASP Team, 2020). First, we ran a Bayesian repeated measures ANOVA for the 3 (size: small, medium, large) x 2 (di- mension: status, nonstatus) mixed model. Table 4 compares the likelihood of all possible models rela- tive to the null model; however, given that our focus is on the predictive performance of each compo- nent, analysis of effects presented in Table 5 is more informative (Bergh et al., 2020; Keysers et al., 2020). To generate the analysis of effects, we used the rec- ommended matched models’ option, which, in our case, compares the model with the in- teraction effect only with the models that exclude the interaction, thereby providing a more conserva- tive estimate for each factor’s contribution (Keysers et al., 2020; Mathôt, 2017). As seen in Table 5, the re- sults revealed that data are 16 times more likely to occur under the models that exclude the interaction effect, which can be concluded as decisive evidence for the null hypothesis with respect to Kass and Raftery’s (1995) reference values. We also conducted a Bayesian ANOVA to exam- ine the effect of portion size on status dimensions only, as we did in the main analyses. As seen in Ta- bles 6 and 7, data are about 24 times more likely to occur under the models excluding the portion size effect, thereby providing decisive evidence for the null hypothesis stating that portion size choices are not associated with status perceptions. We further illustrated this lack of evidence by plotting the model with portion size effect. As seen Figure 2, 95% credible intervals for different levels of portion size overlap substantially, indicating no differences among the levels. Replication Study Original Study Size Com- parison Mdiff t(412) p Cohen’s d [95% CI] Mdiff t(182) p Cohen’s d* Recalculated Cohen’s d [95% CI] * Replication classification according to LeBel et al. 2019 Large vs. Small -0.11 -0.77 .442 -0.10 [-0.34, 0.14] 1.95 4.66 .001 1.10 1.49 [1.09, 1.89] No signal – inconsistent Large vs. Medium -0.12 -0.90 .367 -0.10 [-0.34, 0.14] 1.19 2.95 .01 0.65 0.89 [0.52, 1.26] No signal – inconsistent Medium vs. Small 0.01 0.11 .916 0.01 [-0.23, 0.25] 0.76 2.27 .05 0.46 0.62 [0.26, 0.98] No signal – inconsistent 7 SUPER-SIZE ME: AN UNSUCCESSFUL PREREGISTERED REPLICATION Table 4 Model comparison for all models under consideration for the replication data. Table 5 Analysis of effects of individual factors Effects P(incl) P(incl|data) BFexcl Dimension 0.400 0.993 3.671e -17 SIZE 0.400 0.114 7.731 Dimension  ✻  SIZE 0.200 0.007 16.241 Note. Compares models that contain the effect to equivalent models stripped of the effect. Higher- order interactions are excluded. Table 6 Comparison of the portion size effect model with the null model for the replication data Models P(M) P(M|data) BFM BF01 error % Null model 0.500 0.960 23.899 1.000 SIZE 0.500 0.040 0.042 23.899 0.025 Table 7 Analysis of the effect of portion size Effects P(incl) P(incl|data) BFexcl SIZE 0.500 0.040 23.899 Figure 2. Model averaged posterior distributions for the portion size effect model Note on the Effect Sizes In order to compare the effect sizes, we recalcu- lated the original effect sizes from the reported F- values using two independent tools (Lakens, 2013; Uanhoro, 2017). For both the interaction and the ef- fect of product size on status, we found smaller ef- fect sizes and slightly higher p-values than the orig- inal. We compared effect sizes in the present repli- cation, effect sizes reported in the original paper, and effect sizes we recalculated based on the sum- mary statistics provided in the original paper, and we provided 90% confidence intervals (as it is cus- tomary with η²p , which cannot be smaller than 0). The original interaction was F(1, 177) = 4.06, orig- inal p = .03, recalculated p = .045, original η²p = 0.05, but we recalculated it as η²p = 0.02, 90% CI [0.0002, 0.07]. In our replication, we obtained the following Models P(M) P(M|data) BFM BF01 error % Null model (incl. subject) 0.200 3.271e -17 1.308e -16 1.000 Dimension 0.200 0.879 29.132 3.720e -17 0.939 Dimension + SIZE 0.200 0.114 0.513 2.876e -16 6.109 Dimension + SIZE + Dimension  ✻  SIZE 0.200 0.007 0.028 4.671e -15 8.726 SIZE 0.200 3.741e -18 1.497e -17 8.742 5.093 Note. All models include subject 8 TUNCA, ZIANO, AND XU results: F(2, 412) = 0.83, p = .435, η²p < 0.01, 90% CI [0, 0.017] The original main effect of product size on status inferences yielded F(1, 177) = 10.22, original p = .001, recalculated p = .002, original η²p = 0.10, recalculated η²p = 0.05, 90% CI [0.012, 0.12]. Note that the recal- culated p could be due to a different rounding. Our replicated effect size on status: F(2, 412) = 0.48, p = .620, η²p < 0.01, 90% CI [0, 0.012]. The same can be said about the Cohen’s ds reported at page 1051. Re- calculating them yields much larger effect sizes (see supplementary materials for details), which we re- port in Table 3. General Discussion In this work, we failed to replicate the first exper- iment from Dubois et al. (2012), which found that consumers associated larger portion choices with higher status. What could be the reasons for this replication failure? First, although we conducted a direct replication in terms of materials and proce- dure, one major difference from the original study was the study sample. The original experiment was based on 183 undergraduate students (average age not available); the replication was based on 415 par- ticipants from MTurk (Mage = 37.9). Assuming that the undergraduate sample was much younger, age might have influenced the results, such that while students could associate mundane products like coffee, smoothie, or pizza with status, older con- sumers could not. Second, another possible expla- nation is that students on average have lower soci- oeconomic status, and given the link between low socioeconomic status and higher propensity for sta- tus consumption (Dubois & Ordabayeva, 2015), stu- dents were more likely to associate larger portion sizes with status. However, there are plenty of suc- cessful replications in which an original finding ob- tained with students was successfully replicated on MTurk samples (Ziano, Wang, et al., 2021; Ziano, Yao, et al., 2020). A third explanation could be the association be- tween status and health behaviors, which have long been associated with higher socioeconomic status (Pampel et al., 2010). Particularly, healthy food con- sumption is prevalent among middle and upper so- cial class while consumption of unhealthy choices such as fast food is common among lower social class (Hupkens et al., 2000; Pechey & Monsivais, 2016). This connection between status and health behaviors has been further augmented in today’s popular culture. For instance, several famous social media influencers are portraying a wealthy lifestyle coupled with healthy behaviors such as eating well, meditating, and doing physical exercise (Vaterlaus et al., 2015). Consequently, it is possible that con- sumers today perceive larger portion choices to be unhealthy, and do not associate such unhealthy be- haviors with status. The findings of Dubois et al. (2012) have been greatly influential in the marketing literature; yet our preregistered direct replication casts doubt on the reliability of the relationship between larger food portions and status. We therefore strongly rec- ommend conducting further preregistered concep- tual and direct replications to ascertain whether larger portions in fact signal higher status. Obesity and other excessive food consumption related health problems have significant consequences; thus, scientific research findings that inform policy- making in these areas must be robust. The postula- tion that consumers signal status via choosing larger portions is certainly novel and worthwhile to exam- ine. Nevertheless, we conclude that the evidence for this postulation remains inconclusive until further replications are available in the literature. Author Contact Burak Tunca Department of Business Administration, Lund University School of Economics and Manage- ment burak.tunca@fek.lu.se https://or- cid.org/0000-0001-6381-2979 Ignazio Ziano (corresponding author) Department of Marketing, Grenoble Ecole de Man- agement, ignazio.ziano@grenoble-em.com https://orcid.org/0000-0002-4957-3614 Xu Wenting Department of Marketing, Grenoble Ecole de Man- agement, Wenting.XU@grenoble-em.com Conflict of Interest and Funding We have no conflict of interest or specific fund- ing source to declare. 9 SUPER-SIZE ME: AN UNSUCCESSFUL PREREGISTERED REPLICATION Author Contributions Ignazio supervised Xu Wenting on her master’s thesis. Burak verified the analyses and conclusions and performed new ones and completed the manu- script submission draft. Ignazio and Burak jointly fi- nalized the manuscript for submission. Wenting Xu conducted the replication as part of her “Grand Memoire” (Master’s Thesis) at Grenoble Ecole de Management during the academic year 2018-19. Open Science Practices This article earned the Preregistration+, Open Data and the Open Materials badge for preregister- ing the hypothesis and analysis before data collec- tion, and for making the data and materials openly available. It has been verified that the analysis repro- duced the results presented in the article. The entire editorial process, including the open reviews, is published in the online supplement. References Bergh, D. van den, Doorn, J. van, Marsman, M., Draws, T., Kesteren, E.-J. van, Derks, K., Dab- lander, F., Gronau, Q. F., Kucharský, Š., Gupta, A. R. K. N., Sarafoglou, A., Voelkel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagen- makers, E.-J. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. 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