At first glance, this might pose a threat to the generalizability mentioned above. However, it is important to note, that all observed effects were of small or even tiny effect size in accordance with common classifications e. Likewise, both our hypotheses, assuming differences in self-esteem and Extraversion, respectively, could not be confirmed and were henceforth rejected. Bearing this in mind, it appears legitimate to assume that in spite of minor differences between iOS and Android users, none of the found differences are sufficiently strong to be of actual practical relevance.

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However, this impression may be misleading. On the contrary, we would like to stress that whereas it is relatively easy to statistically eliminate the influence of sociodemographic variables, it is by far less so when it comes to gathering actual samples via certain technologies. Replicating the classic study by Buchanan and Reips [ 33 ] the present results hint that in the given context, sociodemographic factors are a force to be reckoned with that exerts a sizable impact on the studied effects.

This is reflected in the fact, that the only other significant predictor of smartphone OS, aside from Openness to Experience in Study 1 was monthly budget. In a similar vein, the observation, that most effects in Study 2 vanished after sociodemographic variables were controlled for, attests to the same possibility. Unless being accounted for by matched samples, by nature, the distribution of such sociodemographic variables may vary profoundly between operating systems.

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In spite of our efforts to conduct the present research in the most beneficial and effective way, some drawbacks persisted nonetheless, which we intend to address in the following section. To start with, both studies used ad-hoc samples with very little recruitment restrictions. Although these community-based samples are more diverse in sample characteristics than common student samples and do henceforth generate a higher usability of the resulting data [ 77 ], some disadvantages need to be considered. Notably, as a direct consequence, arising from our recruitment strategy, we faced a skewed sex distribution in the sample of Study 2, with roughly two thirds of the sample being women.

This might sound worrying, because Big Five personality traits have been shown to vary as a function of sex, especially in well-developed wealthy and egalitarian societies just like Germany [ 78 ]. Both samples featured a rather wide range in terms of age, which is of interest as Big Five personality traits have also been reported to change dynamically across the lifespan [ 79 ].

This being said, one might turn this heterogeneity into an asset, as it reflects the actual age composition of the target population better than traditional psychological studies that are notoriously prone to draw from college student samples only [ 30 ]. Nonetheless, in keeping with the findings above on the link between sociodemographic variables and Big Five personality traits, we controlled statistically for sex, age, and educational level in Study 2. Of note, this had a strong influence on the obtained results that merits further attention. From a methodological point of view, Study 2 may receive the critique that most people tend to own and use both, a smartphone and a computer system.

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Consequently their placement in the respective compared groups could be perceived as reflecting an arbitrary snapshot rather than a clear-cut, permanent membership in one particular user group i. Against this backdrop, it appears reasonable that a pronounced preference for a single electronic device exists in most people which allows to sort participants into the user groups that we employed throughout Study 2.

Furthermore, although we have mounted our best efforts to ascertain a holistic and balanced assessment of user personality, with a strong emphasis on the Big Five taxonomy, acknowledging its role as a key concept in smartphone-based personality research [ 11 , 12 ], we cannot rule out the possibility that we have failed to detect significant differences across users of different OS along unmeasured personality dimensions. Faced with a length-breadth tradeoff, when designing our questionnaire, we chose to pursue a holistic, yet parsimonious approach to maintain participant motivation, reduce fatigue, boredom and dropout and yield high-quality data [ 64 , 66 , 67 ].

However, future research should expand on our findings and consider other personality traits. While it is very clear, that our research leaves some room for improvement, it benefits from an array of assets that deserve to be mentioned. To start with, we would like to stress that thanks to its design, Study 2 can be interpreted as an in-built replication of Study 1, although with a somewhat narrower focus, concentrating on Big Five personality traits in a German-speaking sample.

Beyond that, it makes two valuable contributions in extension of Study 1. Notably, we assessed OS, the grouping variable in question automatically, unlike Study 1 where we relied on self-reports. Moreover, it widens the horizon of the study, by taking desktop computer OS into account as well. Due to the novelty of smartphones in general and science apps in particular, a refined research philosophy as well as best practices to accommodate their use as data collection tools are currently still lacking.

In recognition of the arising challenges, the present investigation represents an attempt to mark another step towards a robust, unified methodology for smartphone- and computer-based social science studies. Such studies provide an easy, yet cost-effective way of collecting vast amounts of ecologically valid data from diverse, geographically widely scattered samples. Events can be recorded in real time, as they occur. Still, special care has to be taken, when employing smartphones and science apps, as an inadequate manner of using them for research purposes, may both, undermine data quality and compromise ethical standards.

Against this backdrop, we aimed to shed new light on a potentially harmful selection bias that emerges following the widespread use of science apps that are compatible with one OS only. Thankfully for less tech-savvy scholars, according to our findings, this effort is not to be considered a necessity, in spite of potentially distorting differences in sociodemographic composition that researchers should be aware of. More to the point, minor differences in personality do exist, but they are of negligible effect size.

I…iOS, A…Android. Resources: UDR. Writing — original draft: FMG. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract The increasingly widespread use of mobile phone applications apps as research tools and cost-effective means of vast data collection raises new methodological challenges. Introduction Following the advent and proliferation of smartphones, app-based research has spread across the scientific landscape, ranging from fields as diverse as physics [ 1 ], tourism [ 2 , 3 ] and geology [ 4 , 5 ] to medicine [ 6 , 7 ].

The reasons for this momentum are manifold: Smartphone technology enables researchers to collect an abundance of data high volume , that arrives as a continuous stream in real time i. To that end, we come up with two studies that complement each other. Research questions and hypotheses In marketing research and consumer psychology, brands are believed to have a personality, featuring a unique set of characteristics usually attributed to humans [ 56 ].

While we do not want to give in to mere speculations, drawing from the presented findings, we formulated the following hypotheses: Hypothesis 1 Study 1. Hypothesis 2 Study 1, Study 2. Materials Questionnaire length in electronically distributed online surveys deserves special attention, as the same content may appear longer on Web sites, stretching across several pages, as opposed to traditional paper-and-pencil questionnaires [ 62 ], also see [ 63 ], for the one-item-one-screen design. Statistical analysis Following a twofold analysis procedure, we initially checked for potential differences in demographic variables, between self-reported iOS and Android users.

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Personality traits Given the multitude of variables and the risk of type I error that would have resulted from multiple testing, when conducting individual ANOVAs for every trait, we decided to run a binary logistic regression model instead, whereby smartphone OS i. Download: PPT. Table 1. Table 2. Descriptives of study 1 variables separated for operating systems Android, iOS. Materials and methods—study 2 Participants Our second sample differed from the first sample insofar, as it was larger and more homogeneous with respect to its cultural composition.

This snowball sampling procedure led to a community-based convenience sample of German-speaking participants, which was effectively reduced to 2, German-speaking participants due to the following reasons: First, we excluded data sets from 26 participants who had exhibited suspicious responses that raised doubts about their seriousness in answering the questionnaire always giving the same highly implausible answer to the delinquency questions, e.

Materials In line with the approach adopted in Study 1 we chose to design the online questionnaire in a fashion that would allow participants to complete it in no more than 15 minutes for the sake of enhanced retention rates and increased data quality. Results—study 2 We followed a similar analysis procedure as outlined in Study 1. Personality traits In order to give a general overview, Table 3 exhibits descriptive parameters.

Table 3. Table 4. General discussion Today, the rise of smartphones is already transforming our lives and will most likely continue to do so in the next years to come, as mobile technology becomes more and more ubiquitous all around the world. Strengths and limitations In spite of our efforts to conduct the present research in the most beneficial and effective way, some drawbacks persisted nonetheless, which we intend to address in the following section. Conclusion Due to the novelty of smartphones in general and science apps in particular, a refined research philosophy as well as best practices to accommodate their use as data collection tools are currently still lacking.

Supporting information. S1 Table.

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Journal of Individual Differences.