Network models of driver behavior

The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.Peer reviewe

Exploring emotional expressions in e-word-of-mouth from online communities

Electronic word-of-mouth communication (eWOM) is an important force in building a digital marketplace. The study of eWOM has implications for how to build an online community through social media design, web communication and knowledge exchange. Innovative use of eWOM has significant benefits, especially for start-up firms. We focus on how users on the web communicate value related to online products. It is the premise of this paper that generating emotional value (E-value) in social media and networking sites (SMNS) is critical for the survival of new e-service ventures. Hence, by introducing a formal value theory as a coding scheme, we report a study on E-value in SMNS by analyzing how a Swedish start-up industrial design company attempted to build a global presence by creating followers on the web. The aim of the study was to investigate how the company\u27s website design and communication can affect eWOM over time. This was done by capturing a series of “emoticon and value expressions” generated by community members from three different e-communication campaigns (2011–2012) with changing website content, hence giving different stimuli to viewers. Those members who expressed emotional value, often incorporating emoticons, displayed both shorter verbal expressions and reaction time. These value expressions, we suggest, are important aspects of eWOM and need to be actively taken into account. The study has implications for information management strategies through using eWOM. © 2016 Elsevier Lt

All Happy Emotions Are Alike but Every Unhappy Emotion Is Unhappy in Its Own Way : A Network Perspective to Academic Emotions

Quantitative research into the nature of academic emotions has thus far been dominated by factor analyses of questionnaire data. Recently, psychometric network analysis has arisen as an alternative method of conceptualizing the composition of psychological phenomena such as emotions: while factor models view emotions as underlying causes of affects, cognitions and behavior, in network models psychological phenomena are viewed as arising from the interactions of their component parts. We argue that the network perspective is of interest to studies of academic emotions due to its compatibility with the theoretical assumptions of the control value theory of academic emotions. In this contribution we assess the structure of a Finnish questionnaire of academic emotions using both network analysis and exploratory factor analysis on cross-sectional data obtained during a single course. The global correlational structure of the network, investigated using the spinglass community detection analysis, differed from the results of the factor analysis mainly in that positive emotions were grouped in one community but loaded on different factors. Local associations between pairs of variables in the network model may arise due to different reasons, such as variable A causing variation in variable B or vice versa, or due to a latent variable affecting both. We view the relationship between feelings of self-efficacy and the other emotions as causal hypotheses, and argue that strengthening the students' self-efficacy may have a beneficial effect on the rest of the emotions they experienced on the course. Other local associations in the network model are argued to arise due to unmodeled latent variables. Future psychometric studies may benefit from combining network models and factor models in researching the structure of academic emotions.Peer reviewe

Driver Behavior Questionnaire -kyselyn mittausinvarianssi suomalaisten ja irlantilaisten nuorten kuljettajien otoksissa

In this Master's thesis I examine the measurement invariance of the Driver Behavior Questionnaire (DBQ), the perhaps most widely used questionnaire instrument in traffic psychology, across samples of Finnish and Irish young drivers (18 - 25 years of age). The DBQ was developed in the beginning of the 1990s based on principal component analyses. The questionnaire was originally based on a well-tested theory in cognitive ergonomics (the Generic Error Modeling System, GEMS), but in the research that has ensued, the item pool and the factor structure has been determined in an exploratory fashion. This has resulted in an abundance of DBQ versions, which comprise anything from nine to over one hundred items and from one to seven factors. Further, in research articles based on the DBQ, it is a common practice to calculate sum or average scores and compare them across subgroups of respondents. The 28-item version of questionnaire, which is currently perhaps most widely used, is thought to measure two, three or four latent variables. In this thesis I use confirmatory factor analysis and, specifically, analysis of measurement invariance to examine which of the three alternative factor structures functions as the most fitting description of the responses of Finnish and Irish young drivers. The analysis of measurement invariance is based on fitting a series of increasingly restrictive models to data. At each stage of the analysis, an increasing set of parameters are constrained to equality across the samples under comparison. In case the constrained model does not fit the data worse than the unconstrained model, the constrained model can be applied in all (in this thesis both) data sets. The models that are fit to data are, in order: 1) The configural model in which only the number of factors is constrained, 2) the weak invariance model, in which factor loadings are constrained to equality, 3) the strong invariance model, in which also the intercept terms of each item are constrained to equality and 4) the strict invariance model, in which also the error terms of each item are constrained to equality. In addition, models of partial invariance are applied. In these models, only some of the constraints related to each stage of the analysis are preserved. In addition to comparing the models statistically, their fit to data is examined using various descriptive statistics and graphical representations. As a central result I propose that the four-factor model offers the best fit to both data sets, even though the model needs to be modified in an exploratory mode of analysis to ensure sufficient fit to data. Further analyses show that two of the four factors are different in nature in the two samples and that only in the Irish data set do all of the items load on the factors they are expected to. On the other hand, the analysis of the other two factors shows that the items that load on them are interpreted essentially similarly in the two samples and that weak invariance can be assumed on their part. In addition, partial strong invariance can be assumed in the case of one factor, even though even then the values of most of the intercept terms need to be freely estimated in the two data sets. As a conclusion I suggest that, in contrast to the prevailing practice, comparing sum scores based on DBQ factors is dubious and that comparing latent variables scores may be justified only in the case of one factor out of four. As a practical recommendation, I suggest that the factor structure of the DBQ be further developed based on theories of cognitive ergonomics and cognitive psychology and that invariance analyses be performed as a matter of routine before carrying out comparisons of groups based on results of factor analyses.Tässä pro gradu -työssä tarkastellaan liikennepsykologiassa ehkä eniten käytetyn kyselyinstrumentin, Driver Behaviour Questionnaire DBQ:n mittausinvarianssia suomalaisten ja irlantilaisten nuorten kuljettajien (18 - 25 v.) otoksissa. DBQ on kehitetty 1990-luvun alussa pääkomponenttianalyysin perusteella. Kysely perustui alun perin laajalti testattuun kognitiivisen ergonomian teoriaan (Generic Error Modeling System, GEMS), mutta sittemmin kyselyn osioiden joukko ja sen faktori- tai pääkomponenttirakenne ovat määräytyneet aineistolähtöisesti. Tämä on johtanut siihen, että kyselystä on olemassa runsaasti erilaisia versioita, joiden osiomäärä vaihtelee yhdeksästä yli sataan ja faktorimäärä yhdestä seitsemään. Kyselyn perusteella lasketaan yleisesti summa- tai keskiarvomuuttujia, joita vertaillaan vastaajien osajoukoissa. Mahdollisesti yleisimmin kyselystä käytetään 28 osion versiota, jonka ajatellaan mittaavan kahta, kolmea tai neljää latenttia muuttujaa. Tässä tutkimuksessa tarkastellaan konfirmatorisen faktorianalyysin ja erityisesti mittausinvarianssianalyysin keinoin, mikä esitetyistä faktorirakenteista sopii kuvaamaan suomalaisten ja irlantilaisten nuorten kuljettajien vastauksia parhaiten. Mittausinvarianssianalyysi perustuu ajatukseen siitä, että aineistoon sovitetaan sarja malleja, joissa rajoitetaan koko ajan kasvava joukko mallin parametreja identtisiksi vertailtavien otosten välillä. Mikäli rajoitettu malli ei sovi tilastollisessa mielessä rajoittamatonta mallia huonommin aineistoon, voidaan rajoitettua mallia soveltaa kaikkiin (tässä: molempiin) vertailtaviin aineistoihin. Aineistoon sovitettavat mallit ovat järjestyksessä: 1) mittausmalli, jossa ainoastaan faktoreiden määrä on rajoitettu, 2) heikon invarianssin malli, jossa faktorilataukset on rajoitettu samoiksi, 3) vahvan invarianssin malli, jossa lisäksi osioiden vakiotermit on rajoitettu samoiksi ja 4) tiukan invarianssin malli, jossa lisäksi osioiden virhetermit on rajoitettu samoiksi. Lisäksi sovelletaan osittaisen invarianssin malleja, joissa vain osa kyseisessä invarianssitestauksen vaiheessa asetettavista rajoituksista pidetään voimassa. Mallien tilastollisen vertailun lisäksi niiden sopivuutta aineistoon arvioidaan erilaisten kuvailevien tunnuslukujen ja graafisten esitysten avulla. Keskeisenä tuloksena esitetään, että vertailluista malleista neljän faktorin malli sopii parhaiten molempiin aineistoihin, vaikka mallia onkin muokattava aineistolähtöisesti, jotta riittävä yhteensopivuus aineiston kanssa saadaan varmistettua.Tarkempi tarkastelu osoittaa, että neljästä faktorista kaksi ovat luonteeltaan erilaisia kahdessa otoksessa, sillä vain irlantilaisessa otoksessa kaikki osiot latautuvat odotusten mukaisille faktoreille. Toisaalta kahden muun faktorin analyysi osoittaa, että niille latautuvat osiot tulkitaan olennaisesti samalla tavoin kahdessa otoksessa ja heikon invarianssin oletus voidaan tehdä. Lisäksi voidaan tehdä osittaisen vahvan invarianssin oletus yhden faktorin tapauksessa, vaikka tällöinkin suurin osa faktorille latautuvien vakiotermien arvoista on estimoitava vapaasti kahdessa aineistossa. Johtopäätöksinä esitetään, että nykyisestä käytännöstä poiketen DBQ:n faktoreiden pohjalta laskettujen summamuuttujien vertailu on arveluttavaa ja että latenttien muuttujien vertailu saattaa olla perusteltua vain yhden faktorin tapauksessa neljästä. Toimintasuosituksena esitetään faktorirakenteen jatkokehittämistä kognitiivisen ergonomian ja kognitiivisen psykologian teorioiden pohjalta sekä invarianssitarkastelujen suorittamista rutiininomaisesti ennen faktorianalyysin tuloksiin perustuvien ryhmävertailujen suorittamista

Psychometrics of driver behavior

The role of human factors in crash causation is a central theme in traffic psychology. Human factors are often roughly categorized into cognitive errors and a tendency to break rules. In data analysis, these psychological properties are treated as measurable, continuous quantities, quite alike weight, length and temperature. Their existence is inferred based on covariation among individual traffic behaviors, which for their part function as measurements of the level of these properties: for instance, driving under the influence of alcohol and speeding are thought to reflect the tendency to break traffic rules. The thesis examines joint variation among traffic behaviors and compares two competing explanations for the phenomenon: 1) The latent variable view of errors and violations, according to which covariation among traffic behaviors is explained by latent, unobservable psychological properties that cause variation in them and 2) The network view, according to which traffic behaviors interact directly with one another, which makes it unnecessary to posit unobservable psychological properties as explanations of behavior. Within traffic psychology, questions such as these are usually not explicitly raised; rather, latent variable models are used as the default tool in data analysis. This practice entails certain assumptions, such as that of the latent variable models measuring the same unobservable properties in the same way across groups of respondents. Moreover, more fundamental questions, such as the theoretical status of latent variables in terms of realist vs. constructionist commitments and the nature of the relationship between latent and observed variables are seldom considered. The present thesis addresses these issues. Studies I and II examine a central property of latent variable models of driver behavior: whether the same psychological properties can be measured in the same way across different subgroups of drivers that are defined based on age, sex and nationality. Both studies utilize rigorous latent variable measurement equivalence analyses. Study I concludes that if the latent variable view is adopted, patterns of covariation among self-reported traffic behaviors are sufficiently different across subgroups of Finnish respondents formed based on age and gender that the latent variables may well be specific to the group in question. Study II reaches a similar conclusion concerning social behavior (breaking rules in traffic) based on a comparison of young Finnish and Irish drivers. On the other hand, it shows that cognitive errors can more readily be interpreted as being related to similar – but not identical – latent variables across countries. Study III assumes a novel point of view, and examines interactions among individual traffic behaviors using psychological network models. This shifts the focus from abstract psychological properties to potentially causal relationships between traffic behaviors: drivers who are more likely to exceed speed limits are also more likely to end up driving close to another vehicle, for instance. In other words, edges in the network models are interpreted as causal hypotheses. Study III also presents Poisson regression models that predict crashes from self-reported traffic behaviors instead of latent variables. This enables various self-reported traffic behaviors to have differential associations with crashes, which is intuitively plausible as, for instance, the violations range from driving under the influence of alcohol to honking at others. The models are built and tested in independent sets of data, making it possible to avoid overfitting the predictive models to data at hand. This procedure, together with selecting variables based on regularized regression, is argued to have useful properties in predicting crashes in traffic psychology. As a whole, the thesis presents two new interpretations for the relationship between individual traffic behaviors and the psychological properties investigated within traffic psychology. First, the psychological properties may reduce to nametags for behaviors that co-occur in certain kinds of contexts and have no causal power of their own. Second, they may prove to be emergent properties arising from the interaction among the behaviors. These alternatives are discussed together with an intermediate view that combines the latent variable view and the network view. The thesis, then, positions itself as a part of recent psychometric discussion in which psychological properties are seen as being formed through the interaction of different behaviors, thoughts and emotions without necessarily treating psychological properties as unidimensional, measurable quantities.Inhimillisten tekijöiden vaikutus tieliikenneonnettomuuksiin on yksi keskeisistä liikennepsykologian tutkimuskohteista. Inhimilliset tekijät jaetaan usein karkeasti kognitiivisiin virheisiin ja taipumukseen rikkoa sääntöjä. Aineiston analyysissa nämä psykologiset ominaisuudet oletetaan painon tai pituuden kaltaisiksi mitattaviksi, jatkuviksi suureiksi ja niiden olemassaolo päätellään yksittäisten liikennekäyttäytymisten yhteisvaihteluun perustuen. Liikennekäyttäytymiset puolestaan toimivat mittauksina ominaisuuksien tasosta: esimerkiksi alkoholin vaikutuksen alaisena tai ylinopeudella ajamisen ajatellaan heijastelevan vastaajan taipumusta rikkoa liikennesääntöjä. Tässä väitöstutkimuksessa tarkastellaan liikennekäyttäytymisten yhteisvaihtelua ja vertaillaan kahta kilpailevaa selitysmallia ilmiölle: 1) yhteisvaihtelu selittyy käyttäytymiseen vaikuttavilla psykologisilla ominaisuuksilla ja 2) liikennekäyttäytymisten välillä vallitsee suoria vuorovaikutussuhteita. Työn kaksi ensimmäistä osatutkimusta kysyvät, voidaanko samoja psykologisia ominaisuuksia mitata samalla tarkkuudella sukupuoleen, ikään ja kansallisuuteen perustuvissa ryhmissä ja vastaavat pääosin kieltävästi. Kolmas osatutkimus tarjoaa uudenlaisen verkostopsykometrisen tulkinnan liikennekäyttäytymisten yhteisvaihtelulle. Sen lähtökohtana on, että käyttäytymiset ovat suorassa vuorovaikutussuhteessa keskenään: esimerkiksi verkostomallissa itseraportoidun ylinopeuden ja lähellä edellä kulkevaa autoa ajamisen välille muodostuva linkki tulkitaan hypoteesiksi syy-seuraus-suhteesta näiden käyttäytymisten välillä. Kolmas osatutkimus raportoi myös koneoppimiseen perustuvan ennustemallin onnettomuusriskille. Malli perustuu yksittäisiin liikennekäyttäytymisiin abstraktien psykologisten ominaisuuksien sijaan ja sopii näin ollen luontevasti yhteen verkostopsykometristen mallien kanssa. Nyt käsillä oleva tutkimus esittää, että liikennepsykologian alalla tarkastellut psykologiset ominaisuudet ovat pikemminkin nimilappuja tietyissä yhteyksissä yhdessä esiintyville käyttäytymisen muodoille kuin niihin vaikuttavia taustatekijöitä. Tutkimus asettuu näin ollen osaksi viimeaikaista psykometrista keskustelua, jossa psykologisten ominaisuuksien katsotaan rakentuvan erilaisten käyttäytymisten, ajatusten ja tunteiden vuorovaikutuksessa ilman että niitä oletetaan yksiulotteisiksi mitattaviksi suureiksi

Understanding the multidimensional nature of student engagement during the first year of higher education

In the description of the complex relationship between individual students and their education context, as well as understanding of questions related to progression, retention or dropouts in higher education, student engagement is considered the primary construct. In particular, the significance of the first year of higher education in terms of engagement is decisive. We aim at developing a multidimensional conceptualization of engagement and utilized network analysis. Data were collected as part of the annual Student Barometer survey in Finland during the 2012-2013 academic year, and we gathered a nationally representative sample (n = 2422) of first-year students in different disciplines at 13 Finnish universities. Network analysis confirmed the multidimensional process model of engagement and its six dimensions. The central dimensions of engagement are identity and sense of belonging, which develop in the interplay between individual and collective dimensions as a long-term process. Additional network analyses with covariates identified positive and negative factors that affect engagement. The study adds new perspectives to existing knowledge of engagement. It is important to understand the process-like nature of engagement and make visible factors affecting the process. Based on these findings, we provide novel practical recommendations for interventions for university students who struggle with engagement during their first year.Peer reviewe

The complex relationship between emotions, approaches to learning, study success and study progress during the transition to university

The demands and pressures during the first study year at university are likely to arouse a variety of emotions among students. Nevertheless, there are very few studies on the role of emotions in successful studying during the transition phase. The present study adopts a person-oriented and mixed-method approach to explore, first, the emotions individual students experience during the first year at university. Hierarchical cluster analysis was used to group students (n = 43) on the basis of the emotions they described in an interview. Second, the study investigates how the students in the different clusters scored on approaches to learning (as measured on the Learn questionnaire) and how they succeeded (GPA) and progressed (earned credits per year) in their studies. Three emotion clusters were identified, which differed in terms of the deep and surface approaches to learning, study success and study progress: (1) quickly progressing successful students experiencing positive emotions, (2) quickly progressing successful students experiencing negative emotions and (3) slowly progressing students experiencing negative emotions. The results indicate that it is not enough to focus on supporting successful learning, but that attention should also be paid to promoting students' positive emotions and well-being at this time.Peer reviewe

Restoring Connectedness in and to Nature: Three Nordic Examples of Recontextualizing Family Therapy to the Outdoors

publishedVersio

Study-related exhaustion : First-year students’ use of self-regulation of learning and peer learning and perceived value of peer support

This study examines the profiles of self-regulation of learning, peer learning and peer support among students. The study investigates whether the profiles differ in terms of reported study-related exhaustion. Students completed a questionnaire regarding their use of self-regulation of learning and peer learning and perceived peer support and study-related exhaustion. Four different student profiles were found. The profiles differed in terms of self-reported study-related exhaustion. Self-regulated students with a low level of peer learning and low perceived value of peer support reported the lowest levels of study-related exhaustion, whereas students with self-regulation problems, a high level of peer learning and high perceived value of peer support reported the highest levels of study-related exhaustion. The results showed that problems in self-regulation were positively related to self-reported study-related exhaustion. Identifying different student profiles helps to recognise students who may need more support in studying.Peer reviewe

The Nexus between Study Burnout Profiles and Social Support —The Differences between Domestic (Finnish) and International Master’s Degree Students

The present study investigated the variation in higher education students’ study burnout experiences and how they are related to academic success and social support needs. Similarities and differences between the international and domestic students were also explored. In this mixed-methods study, the data were collected through a self-reported questionnaire, and a total of 902 (response rate 42%) first year master’s students from the fields of arts, business and technology responded. Using Latent Profile Analysis (LPA), we detected three distinct study burnout risk profiles (No exhaustion or cynicism; Exhausted; Exhausted and cynical). The following distinct forms of social support needs were found using theory-based qualitative content analysis: informational, instrumental, emotional, and co-constructional support. We found out that the students with highest risk of burnout had the lowest grade point averages (GPAs). Further investigation showed that international students pass their courses despite the experiences of study burnout, even though the GPAs might deteriorate. When the domestic students experience study burnout symptoms, they both gain fewer study credits and earn lower GPAs. Finally, a relationship between the form of support needed and the burnout profile was identified