Agreement between raters on a categorical scale is not only a subject of scientific research but also a problem frequently encountered in practice. Whenever a new scale is developed to assess individuals or items in a certain context, inter-rater agreement is a prerequisite for the scale to be actually implemented in routine use. Cohen's kappa coeffcient is a landmark in the developments of rater agreement theory. This coeffcient, which operated a radical change in previously proposed indexes, opened a new field of research in the domain. In the first part of this work, after a brief review of agreement on a quantitative scale, the kappa-like family of agreement indexes is described in various instances: two raters, several raters, an isolated rater and a group of raters and two groups of raters. To quantify the agreement between two individual raters, Cohen's kappa coefficient (Cohen, 1960) and the intraclass kappa coefficient (Kraemer, 1979) are widely used for binary and nominal scales, while the weighted kappa coefficient (Cohen, 1968) is recommended for ordinal scales. An interpretation of the quadratic (Schuster, 2004) and the linear (Vanbelle and Albert, 2009c) weighting schemes is given. Cohen's kappa (Fleiss, 1971) and intraclass kappa (Landis and Koch, 1977c) coefficients were extended to the case where agreement is searched between several raters. Next, the kappa-like family of agreement coefficients is extended to the case of an isolated rater and a group of raters (Vanbelle and Albert, 2009a) and to the case of two groups of raters (Vanbelle and Albert, 2009b). These agreement coefficients are derived on a population-based model and reduce to the well-known Cohen's kappa coefficient in the case of two single raters. The proposed agreement indexes are also compared to existing methods, the consensus method and Schouten's agreement index (Schouten, 1982). The superiority of the new approach over the latter is shown. In the second part of the work, methods for hypothesis testing and data modeling are discussed. Firstly, the method proposed by Fleiss (1981) for comparing several independent agreement indexes is presented. Then, a bootstrap method initially developed by McKenzie et al. (1996) to compare two dependent agreement indexes, is extended to several dependent agreement indexes (Vanbelle and Albert, 2008). All these methods equally apply to the kappa coefficients introduced in the first part of the work. Next, regression methods for testing the effect of continuous and categorical covariates on the agreement between two or several raters are reviewed. This includes the weighted least-squares method allowing only for categorical covariates (Barnhart and Williamson, 2002) and a regression method based on two sets of generalized estimating equations. The latter method was developed for the intraclass kappa coefficient (Klar et al., 2000), Cohen's kappa coefficient (Williamson et al., 2000) and the weighted kappa coefficient (Gonin et al., 2000). Finally, a heuristic method, restricted to the case of independent observations, is presented (Lipsitz et al., 2001, 2003) which turns out to be equivalent to the generalized estimating equations approach. These regression methods are compared to the bootstrap method extended by Vanbelle and Albert (2008) but they were not generalized to agreement between a single rater and a group of raters nor between two groups of raters. / Sujet d'intenses recherches scientifiques, l'accord entre observateurs sur une échelle catégorisée est aussi un problème fréquemment rencontré en pratique. Lorsqu'une nouvelle échelle de mesure est développée pour évaluer des sujets ou des objets, l'étude de l'accord inter-observateurs est un prérequis indispensable pour son utilisation en routine. Le coefficient kappa de Cohen constitue un tournant dans les développements de la théorie sur l'accord entre observateurs. Ce coefficient, radicalement différent de ceux proposés auparavant, a ouvert de nouvelles voies de recherche dans le domaine. Dans la première partie de ce travail, après une brève revue des mesures d'accord sur une échelle quantitative, la famille des coefficients kappa est décrite dans différentes situations: deux observateurs, plusieurs observateurs, un observateur isolé et un groupe d'observateurs, et enfin deux groupes d'observateurs. Pour quantifier l'accord entre deux observateurs, le coefficient kappa de Cohen (Cohen, 1960) et le coefficient kappa intraclasse (Kraemer, 1979) sont largement utilisés pour les échelles binaires et nominales. Par contre, le coefficient kappa pondéré (Cohen, 1968) est recommandé pour les échelles ordinales. Schuster (2004) a donné une interprétation des poids quadratiques tandis que Vanbelle and Albert (2009c) se sont interessés aux poids linéaires. Les coefficients d'accord correspondant au coefficient kappa de Cohen (Fleiss, 1971) et au coefficient kappa intraclasse (Landis and Koch, 1977c) sont aussi donnés dans le cas de plusieurs observateurs. La famille des coefficients kappa est ensuite étendue au cas d'un observateur isolé et d'un groupe d'observateurs (Vanbelle and Albert, 2009a) et au cas de deux groupes d'observateurs (Vanbelle and Albert, 2009b). Les coefficients d'accord sont élaborés à partir d'un modèle de population et se réduisent au coefficient kappa de Cohen dans le cas de deux observateurs isolés. Les coefficients d'accord proposés sont aussi comparés aux méthodes existantes, la méthode du consensus et le coefficient d'accord de Schouten (Schouten, 1982). La supériorité de la nouvelle approche sur ces dernières est démontrée. Des méthodes qui permettent de tester des hypothèses et modéliser des coefficients d'accord sont abordées dans la seconde partie du travail. Une méthode permettant la comparaison de plusieurs coefficients d'accord indépendants (Fleiss, 1981) est d'abord présentée. Puis, une méthode basée sur le bootstrap, initialement développée par McKenzie et al. (1996) pour comparer deux coefficients d'accord dépendants, est étendue au cas de plusieurs coefficients dépendants par Vanbelle and Albert (2008). Pour finir, des méthodes de régression permettant de tester l'effet de covariables continues et catégorisées sur l'accord entre deux observateurs sont exposées. Ceci comprend la méthode des moindres carrés pondérés (Barnhart and Williamson, 2002), admettant seulement des covariables catégorisées, et une méthode de régression basée sur deux équations d'estimation généralisées. Cette dernière méthode a été développée dans le cas du coefficient kappa intraclasse (Klar et al., 2000), du coefficient kappa de Cohen (Williamson et al., 2000) et du coefficient kappa pondéré (Gonin et al., 2000). Enfin, une méthode heuristique, limitée au cas d'observations indépendantes, est présentée (Lipsitz et al., 2001, 2003). Elle est équivalente à l'approche par les équations d'estimation généralisées. Ces méthodes de régression sont comparées à l'approche par le bootstrap (Vanbelle and Albert, 2008) mais elles n'ont pas encore été généralisées au cas d'un observateur isolé et d'un groupe d'observateurs ni au cas de deux groupes d'observateurs. / Het bepalen van overeenstemming tussen beoordelaars voor categorische gegevens is niet alleen een kwestie van wetenschappelijk onderzoek, maar ook een probleem dat men veelvuldig in de praktijk tegenkomt. Telkens wanneer een nieuwe schaal wordt ontwikkeld om individuele personen of zaken te evalueren in een bepaalde context, is interbeoordelaarsovereenstemming een noodzakelijke voorwaarde vooraleer de schaal in de praktijk kan worden toegepast. Cohen's kappa coëfficiënt is een mijlpaal in de ontwikkeling van de theorie van interbeoordelaarsovereenstemming. Deze coëfficiënt, die een radicale verandering met de voorgaande indices inhield, opende een nieuw onderzoeksspoor in het domein. In het eerste deel van dit werk wordt, na een kort overzicht van overeenstemming voor kwantitatieve gegevens, de kappa-achtige familie van overeenstemmingsindices beschreven in verschillende gevallen: twee beoordelaars, verschillende beoordelaars, één geïsoleerde beoordelaar en een groep van beoordelaars, en twee groepen van beoordelaars. Om de overeenstemming tussen twee individuele beoordelaars te kwantificeren worden Cohen's kappa coëfficiënt (Cohen, 1960) en de intraklasse kappa coëfficiënt (Kraemer, 1979) veelvuldig gebruikt voor binaire en nominale gegevens, terwijl de gewogen Kappa coëfficiënt (Cohen, 1968) aangewezen is voor ordinale gegevens. Een interpretatie van de kwadratische (Schuster, 2004) en lineaire (Vanbelle and Albert, 2009c) weegschema's wordt gegeven. Overeenstemmingsindices die overeenkomen met Cohen's Kappa (Fleiss, 1971) en intraklasse-kappa (Landis and Koch, 1977c) coëfficiënten kunnen worden gebruikt om de overeenstemming tussen verschillende beoordelaars te beschrijven. Daarna wordt de familie van kappa-achtige overeenstemmingscoëfficiënten uitgebreid tot het geval van één geïsoleerde beoordelaar en een groep van beoordelaars (Vanbelle and Albert, 2009a) en tot het geval van twee groepen van beoordelaars (Vanbelle and Albert, 2009b). Deze overeenstemmingscoëfficiënten zijn afgeleid van een populatie-gebaseerd model en kunnen worden herleid tot de welbekende Cohen's coëfficiënt in het geval van twee individuele beoordelaars. De voorgestelde overeenstemmingsindices worden ook vergeleken met bestaande methodes, de consensusmethode en Schoutens overeenstemmingsindex (Schouten, 1982). De superioriteit van de nieuwe benadering over de laatstgenoemde wordt aangetoond. In het tweede deel van het werk worden hypothesetesten en gegevensmodellering besproken. Vooreerst wordt de methode voorgesteld door Fleiss (1981) om verschillende onafhankelijke overeenstemmingsindices te vergelijken, voorgesteld. Daarna wordt een bootstrapmethode, oorspronkelijk ontwikkeld door McKenzie et al. (1996) om twee onafhankelijke overeenstemmingsindices te vergelijken, uitgebreid tot verschillende afhankelijke overeenstemmingsindices (Vanbelle and Albert, 2008). Al deze methoden kunnen ook worden toegepast op de overeenstemmingsindices die in het eerste deel van het werk zijn beschreven. Ten slotte wordt een overzicht gegeven van regressiemethodes om het eect van continue en categorische covariabelen op de overeenstemming tussen twee of meer beoordelaars te testen. Dit omvat de gewogen kleinste kwadraten methode, die alleen werkt met categorische covariabelen (Barnhart and Williamson, 2002) en een regressiemethode gebaseerd op twee sets van gegeneraliseerde schattingsvergelijkingen. De laatste methode was ontwikkeld voor de intraklasse kappa coëfficiënt (Klar et al., 2000), Cohen's kappa coëfficiënt (Williamson et al., 2000) en de gewogen kappa coëfficiënt (Gonin et al., 2000). Ten slotte wordt een heuristische methode voorgesteld die alleen van toepassing is op het geval van onafhankelijk waarnemingen (Lipsitz et al., 2001, 2003). Ze blijkt equivalent te zijn met de benadering van de gegeneraliseerde schattingsvergelijkingen. Deze regressiemethoden worden vergeleken met de bootstrapmethode uitgebreid door Vanbelle and Albert (2008) maar werden niet veralgemeend tot de overeenstemming tussen een enkele beoordelaar en een groep van beoordelaars, en ook niet tussen twee groepen van beoordelaars.

Fragestellung und Zielsetzung: Ausgehend von dem Ziel, kommunikative Fähigkeiten von Studierenden der Humanmedizin objektiv zu messen, sollte in dieser Studie untersucht werden, ob eine ins Deutsche übersetzte Kurzversion des „Calgary-Cambridge Observation Guide for the Medical Interview“ (CCOG) von Kurtz und Silverman (1996) valide und reliabel ist und sich damit die Kommunikationsfähigkeit von Studierenden der Medizin bewerten lässt. Methode: Eine Auswahl von Ärzten, wissenschaftlichen Mitarbeitern und Studierenden des klinischen Studienabschnittes evaluierten im Abstand von mindestens drei Monaten fünf Anamnese-Videos mittels der ins Deutsche übersetzten „CCOG-Kurzversion“. Die Videos bestanden aus je einem Gespräch zwischen einem Studierenden des ersten klinischen Semesters in der Rolle des Arztes bzw. einer approbierten Ärztin und einem Schauspielpatienten – in unterschiedlicher Kommunikationsqualität. Die Auswertung erfolgte deskriptiv nach folgenden Kriterien: Bewertungszeitpunkt, Geschlecht bzw. Gruppe der Rater, Qualität der Videogespräche. Darüberhinaus wurden eine explorative und eine konfirmatorische Faktorenanalyse berechnet und die Retest-Reliabilität (Intra-Rater-Reliabilität) sowie die Intra-Class-Correlation (Inter-Rater-Reliabilität) bestimmt. Ergebnisse: 30 Rater beteiligten sich an der Studie, davon drei als sog. „Goldstan-dard“. Die Gesamtbewertung aller fünf Anamnese-Videos zeigte eine geringfügige Verbesserung in der Notenvergabe beim zweiten Bewertungszeitpunkt. In der Benotung waren professionelle Rater generell etwas strenger als Laien, Ärzte etwas strenger als Studierende und weibliche Rater etwas strenger als männliche. „Gold-standard“ und übrige Rater unterschieden sich bei einzelnen Items bis zu 1,6 Notenpunkten (z.B. beim Item „verbale/nonverbale Unterstützung des Patienten“). In der Originalversion enthält der CCOG 28 Items, die in sechs Skalen (mit jeweils 3 bis 7 Items) zusammengefasst sind. Diese Struktur ließ sich in der hier gerechneten Faktorenanalyse nur bedingt abbilden. Gemäß Eigenvalue > 1 genügten 5 Faktoren zur Abbildung bzw. Aufteilung der Items. Darüber hinaus zeigte sich eine andere Skalenzuordnung als im Original und über die Hälfte der Items (15/28) lud auf demselben Faktor. Auch die Inter-Rater-Übereinstimmung in der Beantwortung einzelner Items war nicht optimal (ICC-Range: 0,05 bis 0,57). Schlussfolgerungen: Die CCOG-Kurzversion zeigte relativ gute Übereinstimmun-gen bei der Retest-Reliabilität. Schwierigkeiten zeigten sich bei der Benotung einiger Items im Vergleich zwischen dem „Goldstandard“ und den übrigen Ratern. Die Skalen-Struktur der Items und die Inter-Rater-Reliabilität sind nur bedingt akzeptabel. Vielleicht hätte eine dreistufige Bewertungsskala oder eine homogene Rater-Gruppe oder auch eine bessere Schulung das Ergebnis der ICC verbessert. Es sollten einige Items gestrichen, sprachlich trennschärfer formuliert oder anders zusammengefasst, neue Items hinzugefügt und Skalen neu strukturiert werden.

Made available in DSpace on 2016-08-12T19:15:26Z (GMT). No. of bitstreams: 1 RobertoPS_TESE.pdf: 1647938 bytes, checksum: dc34992c335e50d26416144e8fb502c1 (MD5) Previous issue date: 2013-11-29 / This study aims to determine the appropriate size of field plots for field experimentation with sunflower. An experiment was conducted in randomized complete blocks design with 14 cultivars of sunflower and 10 replications. The field plots consisted of four rows of six-meter long rows, spaced 0.7 m and 0.3 m between plants, with a total area of 16.8 m2. The useful area of the plot (7.56 m2), consisting of the two central rows, was divided into 12 basic units, each one consisting of three plants in the row (0.63 m2). The production of sunflower grains obtained in basic units was grouped in order to form portions of seven kinds of five different predefined sizes. The appropriate size of the experimental plot was estimated by the following methods: a) Intraclass correlation coefficient b) Maximum modified curvature c) Segmented linear model with plateau and d) Hatheway (1961). Were also estimated the soil heterogeneity coefficient (b) and the detectable difference among treatments (d). There was a reduction in the coefficient of variation with increasing the size of the plot. The soil of the experiment showed high heterogeneity ( = 1.0585). They were estimated by the methods of the intraclass correlation coefficient, maximum modified curvature and segmented linear model with plateau, respectively, the optimal plot sizes corresponding to 2.52, 3.74 and 2.48 m2. The maximum modified curvature method presented estimate of the optimum plot size more appropriate, together with the detectable difference between means of cultivars to accurately assess the yield of sunflower grain. The plot of 3.74 m2 of useful area was considered appropriate to assess the yield of sunflower grains and it was smaller than the size generally used in researches with sunflower. Though the Hatheway method (1961), they were estimated several very aplicable plot sizes. Considering all the cultivars for the same difference to be detected among means of cultivars, the use of a portion of smaller size with the largest number of replicates required less experimental area than the larger plots with a fewer number of replications / O objetivo desse trabalho foi determinar o tamanho adequado de parcela para experimentação de campo com girassol. Foi realizado um experimento no delineamento em blocos completos casualizados com 14 cultivares de girassol e 10 repetições. As parcelas foram constituídas de quatro fileiras de seis metros de comprimento, espaçadas de 0,7 m e entre plantas de 0,3 m, com área total de 16,8 m2. A área útil da parcela (7,56 m2), composta das duas fileiras centrais, foi dividida em 12 unidades básicas, cada uma constituída de três plantas na fileira (0,63 m2). A produção de grãos do girassol obtida nas unidades básicas foi agrupada de modo a formar sete tipos de parcelas de cinco tamanhos diferentes pré-estabelecidos. O tamanho adequado da parcela experimental foi estimado por meio dos seguintes métodos: a) Coeficiente de correlação intraclasse; b) Máxima curvatura modificado; c) Modelo linear segmentado com platô e d) Hatheway (1961). Estimaram-se também o coeficiente de heterogeneidade do solo (b) e a diferença detectável entre tratamentos (d). Ocorreu redução do coeficiente de variação com o aumento do tamanho da parcela. O solo do experimento apresentou alta heterogeneidade ( = 1,0585). Foram estimados pelos métodos do coeficiente de correlação intraclasse, máxima curvatura modificado e modelo linear segmentado com platô, respectivamente, os tamanhos ótimos de parcela correspondentes a 2,52, 3,74 e 2,48 m2. O método da máxima curvatura modificado apresentou estimativa do tamanho ótimo da parcela mais adequado, aliado à diferença detectável entre médias de cultivares para avaliar com precisão o rendimento de grãos do girassol. Parcela 3,74 m2 de área útil foi considerada adequada para avaliação do rendimento de grãos do girassol e foi menor que o tamanho geralmente usado nas pesquisas com o girassol. Pelo método de Hatheway (1961) estimaram-se diversos tamanhos de parcelas, muitos aplicáveis. Considerando todas as cultivares, para uma mesma diferença a ser detectada entre médias de cultivares, a utilização de parcela de menor tamanho com maior número de repetições requereu menos área experimental do que parcelas maiores com menor número de repetições

Made available in DSpace on 2016-08-12T19:18:46Z (GMT). No. of bitstreams: 1 RobertoPS_TESE.pdf: 1647938 bytes, checksum: dc34992c335e50d26416144e8fb502c1 (MD5) Previous issue date: 2013-11-29 / This study aims to determine the appropriate size of field plots for field experimentation with sunflower. An experiment was conducted in randomized complete blocks design with 14 cultivars of sunflower and 10 replications. The field plots consisted of four rows of six-meter long rows, spaced 0.7 m and 0.3 m between plants, with a total area of 16.8 m2. The useful area of the plot (7.56 m2), consisting of the two central rows, was divided into 12 basic units, each one consisting of three plants in the row (0.63 m2). The production of sunflower grains obtained in basic units was grouped in order to form portions of seven kinds of five different predefined sizes. The appropriate size of the experimental plot was estimated by the following methods: a) Intraclass correlation coefficient b) Maximum modified curvature c) Segmented linear model with plateau and d) Hatheway (1961). Were also estimated the soil heterogeneity coefficient (b) and the detectable difference among treatments (d). There was a reduction in the coefficient of variation with increasing the size of the plot. The soil of the experiment showed high heterogeneity ( = 1.0585). They were estimated by the methods of the intraclass correlation coefficient, maximum modified curvature and segmented linear model with plateau, respectively, the optimal plot sizes corresponding to 2.52, 3.74 and 2.48 m2. The maximum modified curvature method presented estimate of the optimum plot size more appropriate, together with the detectable difference between means of cultivars to accurately assess the yield of sunflower grain. The plot of 3.74 m2 of useful area was considered appropriate to assess the yield of sunflower grains and it was smaller than the size generally used in researches with sunflower. Though the Hatheway method (1961), they were estimated several very aplicable plot sizes. Considering all the cultivars for the same difference to be detected among means of cultivars, the use of a portion of smaller size with the largest number of replicates required less experimental area than the larger plots with a fewer number of replications / O objetivo desse trabalho foi determinar o tamanho adequado de parcela para experimentação de campo com girassol. Foi realizado um experimento no delineamento em blocos completos casualizados com 14 cultivares de girassol e 10 repetições. As parcelas foram constituídas de quatro fileiras de seis metros de comprimento, espaçadas de 0,7 m e entre plantas de 0,3 m, com área total de 16,8 m2. A área útil da parcela (7,56 m2), composta das duas fileiras centrais, foi dividida em 12 unidades básicas, cada uma constituída de três plantas na fileira (0,63 m2). A produção de grãos do girassol obtida nas unidades básicas foi agrupada de modo a formar sete tipos de parcelas de cinco tamanhos diferentes pré-estabelecidos. O tamanho adequado da parcela experimental foi estimado por meio dos seguintes métodos: a) Coeficiente de correlação intraclasse; b) Máxima curvatura modificado; c) Modelo linear segmentado com platô e d) Hatheway (1961). Estimaram-se também o coeficiente de heterogeneidade do solo (b) e a diferença detectável entre tratamentos (d). Ocorreu redução do coeficiente de variação com o aumento do tamanho da parcela. O solo do experimento apresentou alta heterogeneidade ( = 1,0585). Foram estimados pelos métodos do coeficiente de correlação intraclasse, máxima curvatura modificado e modelo linear segmentado com platô, respectivamente, os tamanhos ótimos de parcela correspondentes a 2,52, 3,74 e 2,48 m2. O método da máxima curvatura modificado apresentou estimativa do tamanho ótimo da parcela mais adequado, aliado à diferença detectável entre médias de cultivares para avaliar com precisão o rendimento de grãos do girassol. Parcela 3,74 m2 de área útil foi considerada adequada para avaliação do rendimento de grãos do girassol e foi menor que o tamanho geralmente usado nas pesquisas com o girassol. Pelo método de Hatheway (1961) estimaram-se diversos tamanhos de parcelas, muitos aplicáveis. Considerando todas as cultivares, para uma mesma diferença a ser detectada entre médias de cultivares, a utilização de parcela de menor tamanho com maior número de repetições requereu menos área experimental do que parcelas maiores com menor número de repetições

abstract: Through a two study simulation design with different design conditions (sample size at level 1 (L1) was set to 3, level 2 (L2) sample size ranged from 10 to 75, level 3 (L3) sample size ranged from 30 to 150, intraclass correlation (ICC) ranging from 0.10 to 0.50, model complexity ranging from one predictor to three predictors), this study intends to provide general guidelines about adequate sample sizes at three levels under varying ICC conditions for a viable three level HLM analysis (e.g., reasonably unbiased and accurate parameter estimates). In this study, the data generating parameters for the were obtained using a large-scale longitudinal data set from North Carolina, provided by the National Center on Assessment and Accountability for Special Education (NCAASE). I discuss ranges of sample sizes that are inadequate or adequate for convergence, absolute bias, relative bias, root mean squared error (RMSE), and coverage of individual parameter estimates. The current study, with the help of a detailed two-part simulation design for various sample sizes, model complexity and ICCs, provides various options of adequate sample sizes under different conditions. This study emphasizes that adequate sample sizes at either L1, L2, and L3 can be adjusted according to different interests in parameter estimates, different ranges of acceptable absolute bias, relative bias, root mean squared error, and coverage. Under different model complexity and varying ICC conditions, this study aims to help researchers identify L1, L2, and L3 sample size or both as the source of variation in absolute bias, relative bias, RMSE, or coverage proportions for a certain parameter estimate. This assists researchers in making better decisions for selecting adequate sample sizes in a three-level HLM analysis. A limitation of the study was the use of only a single distribution for the dependent and explanatory variables, different types of distributions and their effects might result in different sample size recommendations. / Dissertation/Thesis / Doctoral Dissertation Educational Psychology 2016

The evaluation of shoulder abduction and flexion strength is important in the rehabilitation after rotator cuff tear. The purpose of this work is to assess the intra and inter-rater reliability of measurement data from an isokinetic dynamometer used to evaluate shoulder abduction and flexion strength, with the aim to identify sources of measurement errors and suggest improvements. The measurement data was collected by a research group at Karolinska Institute and contained load and torque data from thirteen healthy subjects in the ages of 25 to 87 years. The measurements were carried out on two occasions, one week apart. Systematic differences between occasions are analyzed using the Shapiro Wilk test, the paired t-test, and Wilcoxon signed rank test. The agreement of the measurements is analyzed quantitatively using the coefficient of variation and the Bland Altman plot, and quantitively, using the intraclass correlation coefficient. A significant systematic difference in shoulder abduction and flexion load measurements was found, and the recommendation to prevent this is that components should be calibrated in a standardized way. The measurements showed varying reliability within and between measurement occasions and that after familiarization with the isokinetic dynamometer, repeatability improved. The findings indicate a need of a standardized protocol for patient education and placement. Measurements from the position sensor contributed to more random torque values. To improve the repeatability in measurements from the position sensor, axis of rotation should be kept aligned. An increasing variability in measurements with increasing load and torque was found. The recommendation is to use a preload for patients using more force in the movement, to make sure a preset speed is not exceeded, which contributes to more reliable measurements. / Utvärderingen av axelabduktion och flexionsstyrka är viktig i rehabiliteringen efter skada i axelleden. Syftet med det här arbetet är att bedöma intra- och interbedömartillförlitligheten hos mätdata från en isokinetisk dynamometer som används för att utvärdera axelabduktion och flexionsstyrka, med syftet att identifiera källor till mätfel och föreslå förbättringar. Mätdatat samlades in av en forskargrupp vid Karolinska Institutet och innehöll belastnings- och vridmomentdata från tretton friska försökspersoner i åldrarna 25 till 87 år. Mätningarna utfördes vid två tillfällen med en veckas mellanrum. Systematiska skillnader mellan tillfällena analyseras med Shapiro Wilk-testet, det parade t-testet och Wilcoxon signed rank test. Mätningarnas överensstämmelse analyseras kvantitativt med hjälp av variationskoefficienten och Bland Altman-diagrammet, samt kvalitativt med hjälp av intraklasskorrelationskoefficienten. En signifikant systematisk skillnad i axelabduktion och flexionsbelastningsmätningar hittades, och rekommendationen för att förhindra detta är att komponenter bör kalibreras på ett standardiserat sätt. Mätningarna visade på en varierande tillförlitlighet inom och mellan mättillfällen och att efter bekantskap med den isokinetiska dynamometern, förbättrades repeterbarheten. Slutsatserna indikerar ett behov av ett standardiserat protokoll för patientutbildning och placering. Mätningar från positionssensorn bidrog till mer slumpmässiga vridmomentvärden. För att förbättra repeterbarheten i mätningar från positionssensorn bör rotationsaxeln hållas i linje. En ökande variation mellan mättillfällen med ökande belastning och vridmoment hittades. Rekommendationen är att använda en förspänning för patienter som använder mer kraft i rörelsen, för att säkerställa att en förinställd hastighet inte överskrids, vilket bidrar till mer tillförlitliga mätningar.

This research delves into the application of semantic segmentation in precision agriculture, specifically targeting the automated identification and classification of various irrigation system types within agricultural landscapes using high-resolution aerial imagery. With irrigated agriculture occupying a substantial portion of US land and constituting a major freshwater user, the study's background highlights the critical need for precise water-use estimates in the face of evolving environmental challenges, the study utilizes advanced computer vision for optimal system identification. The outcomes contribute to effective water management, sustainable resource utilization, and informed decision-making for farmers and policymakers, with broader implications for environmental monitoring and land-use planning. In this geospatial evaluation research, we tackle the challenge of intraclass variability and a limited dataset. The research problem centers around optimizing the accuracy in geospatial analyses, particularly when confronted with intricate intraclass variations and constraints posed by a limited dataset. Introducing a novel approach termed "dynamic contrastive learning," this research refines the existing contrastive learning framework. Tailored modifications aim to improve the model's accuracy in classifying and segmenting geographic features accurately. Various deep learning models, including EfficientNetV2L, EfficientNetB7, ConvNeXtXLarge, ResNet-50, and ResNet-101, serve as backbones to assess their performance in the geospatial context. The data used for evaluation consists of high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) captured in 2015. It includes four bands (red, green, blue, and near-infrared) with a 1-meter ground sampling distance. The dataset covers diverse landscapes in Lonoke County, USA, and is annotated for various irrigation system types. The dataset encompasses diverse geographic features, including urban, agricultural, and natural landscapes, providing a representative and challenging scenario for model assessment. The experimental results underscore the efficacy of the modified contrastive learning approach in mitigating intraclass variability and improving performance metrics. The proposed method achieves an average accuracy of 96.7%, a BER of 0.05, and an mIoU of 88.4%, surpassing the capabilities of existing contrastive learning methods. This research contributes a valuable solution to the specific challenges posed by intraclass variability and limited datasets in the realm of geospatial feature classification. Furthermore, the investigation extends to prominent deep learning architectures such as Segformer, Swin Transformer, Convexnext, and Convolution Vision Transformer, shedding light on their impact on geospatial image analysis. ConvNeXtXLarge emerges as a robust backbone, demonstrating remarkable accuracy (96.02%), minimal BER (0.06), and a high MIOU (85.99%).