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Funding Information: Open access funding provided by University of Helsinki. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 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 mixedmethods 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.

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.

Detta är en fallstudie där två olika Laserskannrar jämförs. Dessa skannrar skiljer sig då de använder två skilda metoder för punktmolnsregistrering. Dessa två metoder är: SLAM (Simultaneous Localization and Mapping) -baserad punktmolnsregistrering och punktmolnsregistrering med den indirekta två-stegs-metoden. Målet med projektet var således att granska den statiska SLAM-baserade skannern Leica RTC 360 3D och jämföra den med den mer traditionsenliga terrestra laserskannern Leica Scanstation P40. Denna undersökning är relevant eftersom det finns en tydlig skillnad mellan dessa laserskannrar både ur en planerings- och en effektivitetssynpunkt. Den statiska SLAM skannern RTC 360 har möjligheten att vara väldigt tidseffektiv då den använder sig av SLAM algoritmen VIS (Visual Inertial System) för punkmolnsregistrering i fält, samt att skannern använder en IMU (Inertial Measurement Unit) som möjliggör skanningar med hög kvalitet utan att instrumentet är ordentligt horisonterat. Vilket är en förutsättning för att kunna genomföra en skanning med P40. Punktmolnen från dessa två laserskannrar jämförs med varandra och granskas visuellt för att analysera vilka skillnader som finns mellan dem. Samma punktäthet används och skanningarna genomförs från samma positioner med båda instrumenten. Utöver detta görs en statistisk undersökning med hjälp av kontrollpunkter och RMS-värden beräknas med hjälp av dessa. För att tillåta denna statistiska analys, georefererades båda punktmolnen med indirekt georeferering till referenssystemet SWEREF 99 16 30 i plan samt RH 2000 i höjd. Just denna process var något enklare att genomföra med P40 än med RTC 360. Då P40 data var något bättre anpassat för bearbetning i Leica Cyclone. Vilket var de program som främst användes för bearbetning av data. RTC 360 använder två pulser för att skanna in varje punkt, vilket resulterade i att den skannade in något fler punkter. Framförallt märktes denna funktion på områden som traditionellt är svåra att skanna med en laserskanner, främst blanka, svarta områden. Det visade sig av kontrollpunkterna att det fanns en liten variation mellan punktmolnenskvalitet där lasersdata från P40 höll 1–2 millimeters kvalitet. Ur detta resultat kunde slutsatsen dras att Leica Scanstation P40 är något bättre för geodetiska ändamål med mycket höga krav på georefereringen. I det flesta andra sammanhang är RTC 360 att rekommendera. Leicas RTC 360 är ett bra exempel på hur SLAM-algoritmen kan användas för att förenkla många laserskanningsprojekt. Även i de projekten med höga krav på detaljrikedom. This is a case study which aims to compare two different laser scanners. The main difference between these two scanners is that they use different solutions for registration of point clouds. These two solutions are SLAM (Simultaneous Localization and Mapping) as well as the indirect two-step approach. The thesis aims at comparing the static SLAM-based scanner Leica RTC 360 with the more traditional scanner Leica Scanstation P40. This is a relevant study due to the big differences between these two scanners, in the aspect of both how much preparation that is need and how effective both scanners are. The static SLAM scanner RTC 360 has the possibility to be very time efficient due to use of the SLAM-algorithm called VIS (Visual Inertial System) that are used for alignment of different point clouds as early as in the fieldwork. The RTC 360 also uses an IMU (Inertial Measurement Unit) that allows the laser scanner to do complete and detailed scans without the need to be perfectly levelled. The P40 on the other hand do need to be precisely levelled to be able to complete a scan. The point clouds from these two laser scanners are compared with each other by reviewing the visual features of the two clouds and finding differences between the point clouds. The same point density was used in both clouds and the scans took place from the same positions with both scanners. A statistical comparison was also made. This statistical analysis was made with use of control points and RMS values that were established with the help of these. This statistical analysis was made possible by the fact that both point clouds were georeferenced to the reference system SWEREF 99 16 30 as well the system RH 2000 for height. This process of georeferencing both clouds was easier to perform with the P40 than the RTC 360. Because the laser data from P40 were slightly better suited for the program Leica Cyclone, which were the program that was used for most of the data processing. RTC 360 uses two individual laser-pulses for each scanned point. This resulted in that the RTC 360 scanned some more points compared to the P40. This difference was extra noticeable on surfaces that usually are difficult for laser scanners to scan, such as plain, black surfaces. The control points showed that quality of both point clouds was very similar to each other. The P40 showed slightly higher accuracy, about 1-2millimeter, relative to the RTC 360 scanner. This resulted in the conclusion that P40 were slightly better for geodic purposes with very high demands on the georeferencing. In most of the other cases RTC 360 is the recommended scanner. Leica RTC 360 is a good example of how the SLAM-algorithm can be used to make many laser scanning projects easier and more efficient.

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 behaviours are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, traffic safety work is unlikely to progress by attributing dangerous behaviour to stable properties of individuals, whereas investigating direct relationships between traffic behaviours offers a fruitful way forward. Network models of driver behaviour and background factors influencing behaviour 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 behaviours is reported. Finally, a model predicting crashes based on prior behaviour 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 behaviours, while network analysis offers a way to do so for self-report studies.

Research into the nature of academic emotions has thus far been dominated by factor analyses of self-report questionnaire data. Recently, network analysis has arisen as an alternative method of conceptualizing the nature and composition of psychological phenomena such as emotions. In this contribution we assess the structure and functioning of a Finnish questionnaire of academic emotions using both network analysis and factor analysis on cross-sectional data obtained during a single course. In network analysis the correlational structure of the questionnaire is understood as consisting of local interactions among the items. Our results show that items related to individual emotions such as boredom appeared as tightly connected clusters in the network. Items related to anger and boredom were positively related. Those relationships, explained in previous research by positing the underlying latent variable of frustration, can also be understood as family resemblance between the semantic contents of the respective items in the network model. From the theoretical point of view, network analysis is most naturally compatible with perceiving emotions as wholes arising out of the interactions among their component parts. From the practical point of view, the present study offers a critical perspective on the use of self-report measures in the study of academic emotions, focusing especially on currently prevailing practices of data analysis.