Alternative Measurement Approaches Research Articles

The problem of obtaining vehicles speed input for road traffic noise microscopic models: a comparison between field measurements and a stochastic approach

Mitigating environmental noise harmful effects is a relevant goal in the EU "Zero Pollution Action Plan", which targets a 30% reduction in chronic annoyance from transportation noise by 2030. Since road traffic is a major noise source in urban and non-urban settings, much effort has been invested in analyzing and modeling it. To assess noise levels, numerous Road Traffic Noise Models (RTNMs) exist, each requiring multiple inputs like vehicle flows, percentage of heavy vehicles, and average speed. Specifically, when dealing with microscopic models, the single vehicle speeds are needed. To obtain them, on-site measurements offer accurate data but are time consuming and expensive. This paper explores two approaches: an alternative measurement approach, based on video processing and object detection tools, and a stochastic approach, which randomly assigns a speed to each vehicle from a distribution curve, depending on traffic conditions and vehicle category. These methodologies are used to provide input data to a road traffic noise microscopic model, whose results are compared with field measured data. This comparison will allow to get useful insights on the performances of the proposed techniques in providing reliable inputs and their potential applications in different scenarios where single vehicle measured speeds are not available.

Behave Yourself! Behavioral Indicators of Trust in Human-Agent Teams

As advancements in artificial intelligence accelerate, there is a rise in the complexity and number of autonomous agents placed in human-agent teams (HATs). With this expansion, it is important to understand how trust in agent teammates evolves and is influenced by contextual events. In support of this, significant research has focused on the factors that influence human trust in an agent and elements that negatively impact this trust. In this research, human trust in agent teammates is typically measured via self-report surveys. Although a reliable format, surveys are not without limitations, as they can be disruptive and lack the temporal resolution needed to capture nuanced and dynamic changes in trust. As a result, researchers are adopting alternative measurement approaches that aim to unobtrusively capture measures that correlate with trust using behavioral and physiological sensors that capture measures such as voice pitch and eye tracking. However, most of these studies utilize only one unobtrusive measure, which can be indicative of various states (e.g., heart rate varies with psychological and physical responses). Given the lack of selectivity in such measures, it may be necessary to utilize multiple unobtrusive measures at once, such as real-time recorded behavioral indicators, to effectively capture changes in these indicators that are due to changes in trust over time. This paper investigated the following research questions: (1) do multiple unobtrusive indicators provide a more holistic picture of trust compared to a single behavioral indicator, and (2) are certain behavioral indicators more indicative of self-reported trust at different times during a mission (e.g., before/after a trust violation event)? Results indicated that following a significant event, changes in self-reported trust are associated with specific behaviors related to improving or mitigating perceived deficiencies in their agent teammates. These insights highlight the opportunity to integrate dynamic behavioral measures in trust assessment frameworks, specifically in scenarios where a significant event compromises trust.

Structural Colored Based Humidity Sensor Consisting of High Resolution 3D Printed Photonic Crystal Coated with Ultrathin Responsive Hydrogels.

Today, humidity sensors have become an integral part of the daily lives. In particular, humidity sensors using an electronic measuring principle have become the standard. Although these sensors have proven to be a stable measurement method, they have some disadvantages, such as their long response time or the danger of using them in explosiveenvironments. This work introduces photonic crystals as an alternative optical measurement approach. The novel technology of ultra-fast two-photon polymerisation printing is combined with a thin-film deposition process, namely iCVD. This allows to print large area high-precision 3D templates, which are subsequently coated with a humidity responsive hydrogel thin film (p(HEMA) of 20nm.The limits of 2PP technology are being pushed allowing the production ofs table and periodic large-area 3D structures. The flexible customization of hydrogels for ambient conditions make them exceptionally promising for a wide range of sensing applications. Additionally, optical methods for measuring humidity seem to be an excellent alternative to overcome the limitations for current state of the art humidity sensors. The optical detection of changes in ambient air humidity is achieved by observing color changes of the printed structure within the visible wavelength range.

Toward an alternative measure of board diversity: an exploratory study on board polarization in German stock exchange-listed companies

PurposeThe effects of board composition on performance have long been discussed in management research using fractionalization measures. In this study, we propose an alternative measurement approach based on board polarization.Design/methodology/approachUsing an exploratory analysis and applying the polarization measure to German Deutscher Aktienindex (DAX)-, Midcap-DAX (MDAX)- and Small Cap-Index (SDAX)-listed companies, this paper applies the polarization index to examine the relationship between board diversity and performance.FindingsThe results show that the polarization concept is well suited to measure principal-agent problems between the members of the management and supervisory boards. We reveal that board polarization is negatively associated with firm performance, as measured by return on investment (ROI).Originality/valueThis exploratory study shows that the measurement of board polarization can be linked to performance differences between companies, which offers promising starting points for further research.

Does an alternative breast support garment provide symptomatic relief for larger breasted women with chronic non-specific back pain?

Nonsurgical guidelines recommend implementing a correctly fitted bra when managing back pain among larger breasted women. Achieving this is challenging with current bra solutions, sizing principles, and fitting approaches. Persistent wearing of an ill-fitting bra can cause negative health implications, including non-specific back pain. This study investigated immediate and short-term biomechanical and pain responses to changing breast support garment among larger breasted women with non-specific back pain. Participants (n = 24) performed a standing task, drop jumps, and seated typing tasks while bra and spinal kinematic data were recorded. Five breast support conditions were assessed: participants' usual bra (control), a professionally fitted bra in the immediate term (standard) and after 4 weeks wear (standard28), and a bra with an alternative design, measurement, and fitting approach in both the immediate term (alternative) and after 4 weeks wear (alternative28). A bra fit assessment and clinical pain/disability questionnaires were included. All participants failed the bra fit assessment in the control bra, compared with 87.5% (n = 21) in the standard and 4.2% (n = 1) in the alternative bras. The standard28 and alternative28 bras provided symptomatic relief, with the alternative28 bra improving a greater number of outcome measures. Reduced nipple-sternal-notch distance was observed only in the alternative28 bra condition. Symptomatic relief may be associated with the resting position of the breast tissue on the anterior chest wall. The alternative bra may provide potential clinical benefit if implemented as part of a nonsurgical or conservative pain management strategy. Alternative breast support garments should be considered to provide solutions to the problems associated with traditional bras.

An alternative flood measurement approach: Flood Meter

Flash floods commonly occur in urban areas due to a short period of heavy rainfall associated with the large impervious area and inadequate drainage system. An increase in the uncertainty of predicting flash floods, especially due to climate change, reduces the accuracy of the existing flood forecasting system. In such cases, the affected zone’s actual flood level data is required for more effective flood management and incident response. This study aims to develop a mobile application called Flood Meter to identify flood levels in real-time. It measured the flood level based on object scale and images taken by mobile phones. Although the validation using an actual flood event with three different types of mobile phones shows lack of accuracy but by considered the maximum flood level only 0.17 m and the result keep improving when flood level increase, it shows a potentially good agreement compared with manual measurement. The application can become an alternative method to disseminate accurate real-time flood situations as well as flood data collection for future flood risk management.

A novel conjugate heat transfer approach to determine the temperature distribution in single-point cutting tool under different conditions

Measurement of accurate temperature of the tooltip, rake face, and flank face during the machining operation has always been difficult and a topic of interest among researchers. Thermal modeling for determining the temperature distribution across the tool surface is one of the approaches to determining the other physical properties associated with temperature in machining. Various attempts have been made to predict the temperature during machining operations. The exact determination of temperature can predict the tool wear and minimize the tooling cost and ideal time in the metal cutting industries. The present work describes the development of an alternative approach for the online tool temperature measurement. The four conditions namely dry cutting with different boundary conditions, flooded water cooling, and the mist flow environment have been considered in the turning operation. Computational fluid dynamics has been used to determine the temperature distribution on tool inserts under different machining environments. The prediction model is developed to compute the temperature across the profile through different numerical equations. Furthermore, the simulated data has also been validated through experimentation with turning operation using a carbide tool and AISI 304 stainless steel as work material. The experimental results are in close agreement with simulated results and have a variation of 25% to 35% in all the machining environments. Among all the cases, the lowest temperature has been recorded in flood water cooling followed by the machining under a mist environment as compared to dry conditions.

Systematic Bias in Self-Reported Social Media Use in the Age of Platform Swinging: Implications for Studying Social Media Use in Relation to Adolescent Health Behavior.

Public health researchers are increasingly interested in the potential relationships between social media (SM) use, well-being, and health behavior among adolescents. However, most research has assessed daily SM time via self-report survey questions, despite a lack of clarity around the accuracy of such reports given the current tendency of youth to access SM on multiple electronic devices and cycle between multiple SM platforms on a daily basis (i.e., platform swinging). The current study investigates the potential for systematic reporting biases to skew findings. Three hundred and twenty incoming college students downloaded software on their computers, tablets, and smartphones to track their active use of Facebook, Twitter, Instagram, and Snapchat over a 2-week surveillance period and then self-reported their average daily minutes on each platform immediately after. Larger proportions of students over-estimated than under-estimated their use, with the largest overestimations found on the most heavily used platforms. Females logged significantly more SM time and were less accurate in reporting than were males and, independently, the likelihood of substantial inaccuracies in reporting total SM time and time on most individual platforms increased with each additional SM platform participants reported using. Findings demonstrate that self-reported estimates of SM time among adolescents in the age of SM platform swinging are prone to substantial error and may lead to biased conclusions about relationships between variables. Alternative measurement approaches are suggested to improve the validity of future research in this area.

Conventional and downside CAPM: The case of London stock exchange

Many studies on asset pricing have highlighted the importance of downside risk, in line with the actual losses of investors. In addition, the capital asset pricing model (CAPM), although presented as a universal theory, may provide significantly different rates of return in bull and bear markets. Using the CAPM under different conditions could be regarded as an alternative measurement and valuation approach to downside risk. This paper investigates conventional and downside approaches to risk taking into account different measures of downside beta coefficients. A further contribution of this research is the development of an alternative approach to testing the CAPM relationship. For this purpose, conditional relationships of the CAPM are proposed in which risk premiums are set separately in bull and bear periods. Using equity data and portfolios from the United Kingdom, we obtained positive and statistically significant downside risk premiums. We observed a slight advantage of downside measures over conventional beta measures. Conditional models provide evidence of a positive risk premium in rising markets and a negative risk premium in falling markets. The robustness analysis in subperiods indicates that these findings are largely unchanged for downside beta coefficients, which is not fulfilled by the model in a variance approach.

Artificial Neural Network and Near Infrared Light in Water pH and Total Ammonia Nitrogen Prediction

Water quality plays an important role in aquaculture. The operation of a freshwater aquaculture fish farming is highly dependent on the ability to understand, monitor, and control the physical and chemical constituents of the water. pH and total ammonia nitrogen (TAN) levels are two critical water quality parameters that affect fish growth rate and health. However, pH and TAN levels are affected by uncontrollable factorse.g.weather, temperature, and biological processes occurring in the water. Therefore, it is important to monitor changes in pH and TAN levels frequently to maintain optimal conditions for freshwater habitats. Near infrared spectroscopy (NIR) has been extensively investigated as an alternative measurement approach for rapid quality control without sample preparation. Therefore, this research aims to evaluate the feasibility of machine learning combined with NIR light in predicting the water pH and TAN values of a fish farming system. The proposed system contains three main components i.e.a multi-wavelength light emitting diode (LED), a light sensing element, and a machine learning model i.e.artificial neural network (ANN). First, the transmitted NIR light with different wavelengths of water samples was measured using the proposed system. Then, the actual pH and TAN values of the water samples were quantified using conventional methods. Next, ANN was used to correlate the measured NIR transmittance with the pH and TAN values. The results show that ANN with four hidden neurons achieved the best prediction performance with a mean square error (MSE) of 0.1466 and 0.3136 and a correlation coefficient (R) of 0.8398 and 0.9560 for the pH and TAN predictions, respectively. These results show that ANNcoupled with NIR light can be promisingly developed for in situ water quality prediction without sample preparation.

Staffing composition in large, US police departments: benchmarking workforce diversity

PurposePolicymakers have long suggested diversifying US police forces, which typically have white male majorities among officers. This article explores to what extent police diversity has changed over time in large agencies, as well as whether different diversity benchmarks may matter for agencies.Design/methodology/approachThe authors draw data from the Law Enforcement Management and Administrative Statistics (LEMAS) survey for 358 agencies that had at least 100 full-time sworn officers in 1997 and 2016 and that reported officer demographic data to the LEMAS in both years. For a selection of 12 communities – three randomly chosen in each of the four US Census regions – the authors compare officer diversity to Census data on population diversity for different benchmarks.FindingsThere has been some increase in diversity but policing largely remains a white male profession. The authors find only limited variation in diversity by type of benchmark – e.g. total population, working population or recruiting-age population – a community considers. This suggests communities may wish to choose a benchmark they can best measure and seek to increase diversity by it, and research on workforce representation may not be sensitive to benchmark choice. The authors also suggest communities and their police organizations consider other ways to assess diversity, including those that illustrate a broader range of attributes and representation throughout the organization, and that they research and test alternative forms of measurement to gauge whether these findings hold for different modeling approaches.Research limitations/implicationsOur analysis is limited to the largest police agencies and to overall staffing level diversity metrics pertaining to sex, race and Hispanic origin. Still, we find many police agencies have room for greater diversity, which could draw more qualified workers and lead to better policing.Originality/valueWhile there has been much attention to police diversity in recent decades, there have been few efforts to compare alternative measurement approaches. This research provides guidance to some initial measures, as well as further considerations communities may wish to make.

Brillouin confocal microscopy to determine biomechanical properties of SULEEI-treated bovine pericardium for application in cardiac surgery.

Heart valves are exposed to a highly dynamic environment and underlie high tensile and shear forces during opening and closing. Therefore, analysis of mechanical performance of novel heart valve bioprostheses materials, like SULEEI-treated bovine pericardium, is essential and usually carried out by uniaxial tensile tests. Nevertheless, major drawbacks are the unidirectional strain, which does not reflect the in vivo condition and the deformation of the sample material. An alternative approach for measurement of biomechanical properties is offered by Brillouin confocal microscopy (BCM), a novel, non-invasive and three-dimensional method based on the interaction of light with acoustic waves. BCM is a powerful tool to determine viscoelastic tissue properties and is, for the first time, applied to characterize novel biological graft materials, such as SULEEI-treated bovine pericardium. Therefore, the method has to be validated as a non-invasive alternative to conventional uniaxial tensile tests. Vibratome sections of SULEEI-treated bovine pericardium (decellularized, riboflavin/UV-cross-linked and low-energy electron irradiated) as well as native and GA-fixed controls (n = 3) were analyzed by BCM. In addition, uniaxial tensile tests were performed on equivalent tissue samples and Young's modulus as well as length of toe region were analyzed from stress-strain diagrams. The structure of the extracellular matrix (ECM), especially collagen and elastin, was investigated by multiphoton microscopy (MPM). SULEEI-treated pericardium exhibited a significantly higher Brillouin shift and hence higher tissue stiffness in comparison to native and GA-fixed controls (native: 5.6±0.2 GHz; GA: 5.5±0.1 GHz; SULEEI: 6.3±0.1 GHz; n = 3, p < 0.0001). Similarly, a significantly higher Young's modulus was detected in SULEEI-treated pericardia in comparison to native tissue (native: 30.0±10.4 MPa; GA: 31.8±10.7 MPa; SULEEI: 42.1±7.0 MPa; n = 3, p = 0.027). Native pericardia showed wavy and non-directional collagen fibers as well as thin, linear elastin fibers generating a loose matrix. The fibers of GA-fixed and SULEEI-treated pericardium were aligned in one direction, whereat the SULEEI-sample exhibited a much denser matrix. BCM is an innovative and non-invasive method to analyze elastic properties of novel pericardial graft materials with special mechanical requirements, like heart valve bioprostheses.

Measurement of Adverse Childhood Experiences: It Matters

Alternative measurement approaches for adverse childhood experiences (i.e., count score versus individual adverse childhood experiences measured dichotomously versus individual adverse childhood experiences measured ordinally) can alter the association between adverse childhood experiences and adverse outcomes. This could significantly impact the interpretation of adverse childhood experiences research. Data were collected in 2018 (analyzed in 2020) via Amazon's Mechanical Turk and from people incarcerated in 4 correctional facilities (N=1,451). Included adverse childhood experience questions measured the following: physical, emotional, and sexual abuse; physical and emotional neglect; household mental illness, substance use, domestic violence, and incarceration; and exposure to community violence before age 18 years. A total of 19 measured outcomes spanned 4 domains of functioning: general functioning, substance use, psychopathology, and criminal behavior. Regression models using the count score explained the least amount of variance in outcomes, whereas multivariable regression models assessing adverse childhood experiences on a continuum explained the most variance. In many instances, the explained variance increased by 2-5 times across the predictive models. When comparing regression coefficients for multivariable regression models that measured adverse childhood experiences as binary versus ordinal, there were notable differences in the effect sizes and in which adverse childhood experiences predicted outcomes. Disparities in results were most pronounced among high-risk populations that experience a disproportionate amount of adverse childhood experiences. Alternative methods of measuring adverse childhood experiences can influence understanding of their true impact. These findings suggest that the deleterious effects of imprecise measurement methods may be most pronounced in the populations most at risk of adverse childhood experiences. For the sake of prevention, the measurement of adverse childhood experiences must evolve.

RURAL HOUSEHOLD POVERTY AND ITS DETERMINING FACTORS: A POVERTY ANALYSIS USING ALTERNATIVE MEASUREMENT APPROACHES

Poverty analysis studies in Ethiopia are dominated by measures determined by the subjectivity of the researchers and not with the involvement of households in understanding and measuring what is meant to the people. Studies conducted to take into considerations the knowledge of the poor are very scant and limited to rural participatory projects. This study is motivated to bridge the literature gap of comparing the objective measuring of poverty with a measure that accounts the perception of households about poverty. Hence, this study aims at examining rural household poverty and its determining factors using alternative poverty measurement approaches inGozamin district of East Gojjam Zone, Amhara Region. It used both objective and subjective based poverty analysis approaches, where the poverty line of the study area is estimated as 19.16 Birr per day using cost of basic needs approach. The study indicates that 35.12 % of the population lives under poverty and it is closely estimated as 33.33% using Participatory Poverty Assessment (PPA). Poverty is rampant and a sever challenge in Dega(cold) agro-ecology of the District, where 57.37% of the population lives under poverty compared to Kolla(hot), where it is down to 8.4%. Among others, family size and being in Kolla agro-ecology have significant negative effect on consumption expenditure, but positively affect poverty incidence, gap and severity, while access to credit, cooperative, health extension services and off-farm activities have significant but exactly opposite results. PPA findings revealed that, perception of the community towards poverty is beyond the conventional, income/consumption based definition. Therefore, development policies and poverty reduction strategies should emphasie on local level poverty understanding and measures.

Phase-shifting interferometry from single frame in-line interferogram using deep learning phase-shifting technology

Phase-shifting interferometry is a highly accurate phase measurement technology. Its application in dynamic phase measurement is limited by the recording process of multiple phase-shifting interferograms. In this paper, a one-to-multiple deep learning (DL) framework is developed to generate an equivalent to multiple image phase shifting interferometry, using only a single in-line interferogram. The network is trained to map the single frame interferogram to three phase-shifting interferograms. And then, all these four phase-shifting interferograms are used for the phase-shifting phase recovery. Simulation and experimental results prove that the proposed method can realize phase measurement in in-line interference system by recording single interferogram. And it has similar precision with traditional phase-shifting holography by recording four frames phase-shifting interferograms. Further, compared with the strategy of generating the phase distribution from single frame interferogram by trained DL framework directly, the DL phase-shifting strategy can reduce the network error in the non-linear mapping partly owing to the introduction of phase-shifting algorithm and the network only needs to deal with the mapping relationship between the interferograms with different phase shifts, showing higher accuracy and better generalization ability. The proposed method can provide a new alternative approach for the high-accuracy dynamic phase measurement.

Computationally efficient processing of in situ underwater digital holograms

AbstractUnderwater digital in‐line holography can provide high‐resolution, in situ imagery of marine particles and offers many advantages over alternative measurement approaches. However, processing of holograms requires computationally expensive reconstruction and processing, and computational cost increases with the size of the imaging volume. In this work, a processing pipeline is developed to extract targets from holograms where target distribution is relatively sparse without reconstruction of the full hologram. This is motivated by the desire to efficiently extract quantitative estimates of plankton abundance from a data set (&gt;300,000 holograms) collected in the Northwest Atlantic using a large‐volume holographic camera. First, holograms with detectable targets are selected using a transfer learning approach. This was critical as a subset of the holograms were impacted by optical turbulence, which obscured target detection. Then, target diffraction patterns are detected in the hologram. Finally, targets are reconstructed and focused using only a small region of the hologram around the detected diffraction pattern. A search algorithm is employed to select distances for reconstruction, reducing the number of reconstructions required for 1 mm focus precision from 1000 to 31. When compared with full reconstruction techniques, this method detects 99% of particles larger than 0.1 mm2, a size class which includes most copepods and larger particles of marine snow, and 85% of those targets are sufficiently focused for classification. This approach requires 1% of the processing time required to compute full reconstructions, making processing of long time‐series, large imaging volume holographic data sets feasible.

Intensive measurement of cognition to support early detection of cognitive change in individuals at risk of dementia

AbstractBackgroundCognitive changes in nondemented older adults are best measured longitudinally and relative to individual baselines. Effect sizes of early changes in cognition are small with most traditional measures, and so there is a need for alternative measurement approaches that can improve statistical power to detect change. Remotely administered mobile assessments can address this need by permitting much more frequent repeated measurements, which then permits statistical delineation of early slopes of decline from performance variation associated with extraneous factors (e.g., mood, practice). MyCogHealth is an iOS/Android app for self‐administered cognitive assessments and customizable surveys. Inclusion of MyCogHealth in the Canadian Thumbs Up program (CTU) will permit evaluation of feasibility and utility of intensive measurement designs for capturing early cognitive changes in older adults identified to be at increased risk of dementia.MethodThe CTU Platform Trial Cohort will complete five intensive measurement “bursts” in three‐month intervals using MyCogHealth. Each burst involves brief self‐administered assessments completed twice daily across seven consecutive days. Each assessment includes the Symbol Match test (processing speed), Dot Memory (visual short‐term memory), a digital Trailmaking Test analogue, and surveys assessing state factors that can affect cognition. A pilot cohort (N=17; Mean age=74; Mean MoCA score=28) was followed for 12 months using this protocol to evaluate feasibility and psychometric integrity of the Symbol Match and Dot Memory tests in this design.ResultIn the pilot cohort Symbol Match correlated with Digit Span Forward and the Trailmaking Test (r=.51‐.66). Dot Memory correlated with Backward Digit Span (r=.49), Trailmaking Test (r=‐.59), object naming (r=.64), and visual episodic memory (r=.58). Retest reliability was .80 for Dot Memory and .87 for Symbol Search. Intraclass correlation was .77 for Symbol Search and .42 for Dot Memory. Participant retention was 100% and compliance with burst testing was 94%.ConclusionPreliminary results support the feasibility, validity, and reliability of self‐administered burst testing deployed longitudinally. Inclusion of MyCogHealth in CTU should be informative in capturing early slopes of decline in the study cohort. Such early detection of decline will inform more powerful, personalized trials where those at the highest predicted risk would be included and those at lower risk excluded.

RainbowPIV with improved depth resolution—design and comparative study with TomoPIV

RainbowPIV is a recent imaging technology, proposed for time-resolved 3D-3C fluid velocity measurement using a single RGB camera. It dramatically simplifies hardware setup and calibration procedures as compared to alternative 3D-3C measurement approaches. RainbowPIV combines optical design and tailored reconstruction algorithms, and earlier preliminary studies have demonstrated its ability to extract physically constrained fluid vector fields. This article addresses the issue of limited axial resolution, the major drawback of the original RainbowPIV system. We validate the new system with a direct, quantitative comparison with four-camera Tomo-PIV on experimental data. The reconstructed flow vectors of the two approaches exhibit a high degree of consistency, with the RainbowPIV results explicitly guaranteeing physical properties, such as divergence free velocity fields for incompressible fluid flows.

A review on recent near infrared spectroscopic measurement setups and their challenges

Despite near infrared spectroscopy (NIRS) has been studied as an alternative measurement approach in various applications, NIRS measurement setup that used in most of studies is ambiguous. Thus, this study aims to review the measurement setup of different near infrared spectroscopic (NIRS) acquisition modes with respect to different types of instrumentation and samples, and to classify them systematically. The relationship among different reported acquisition modes was analysed. Different reported acquisition modes were classified based on the position of instruments in acquiring near infrared signals. A detailed analysis is provided to highlight the distinction of different NIRS acquisition modes. The challenges of existing measurement setups have been discussed. Interaction and diffuse reflection are the same because both measure diffuse signals that reflected from the inner parts of a sample. Finding shows that future NIRS investigation should address the concerns that related to the acquisition modes, unwanted signals, and measurement parameters.

ÖĞRETMENLERİN ALTERNATİF ÖLÇME DEĞERLENDİRME TEKNİKLERİNİ KULLANIMINA YÖNELİK YETERLİK ALGILARININ İNCELENMESİ

The purpose of this research is to reveal teachers' perceptions of competence towards using alternative measurement and evaluation techniques. Participants of the study are teachers working in primary, secondary and high schools in Sivas city center. The data were collected between September and November 2019. In the study, “self-efficacy scale for alternative measurement and evaluation approaches” was used to collect data. Cronbach's Alpha reliability coefficient of the scale was examined and it was concluded that the scale was reliable. The data obtained were examined in terms of normal distribution and the Kolmogorov-Smirnov test showed that the data were distributed normally. Independent groups t-test was used for the participants' gender, educational status and in-service training, and ANOVA test was used for the education level and seniority variables. As a result of the research, it was found that teachers' perceptions of competence towards using alternative measurement and evaluation techniques did not make a significant difference according to gender. It was concluded that there was a significant difference in terms of educational status, in-service training, education level and seniority variables. As a result of the research, it is suggested that teachers should be encouraged to do postgraduate education in order to ensure their personal development and that teachers should be supported by establishing a measurement and evaluation unit in the school.