Was sind einige häufige Quellen für Messverzerrungen in Six Sigma-Projekten?

It is important to clearly define bias and measurement error. A random error is not a bias because bias means that the outcome of a measurement is always deviated to a higher or lower value when compared to the true value. A random error is randomly distributed around the true value. However random errors and biases are both bad for any process optimization and impair the day to day decisions in a process

A validação do sistema de medição é uma das precauções iniciais à ser adotada na elaboração dos projetos Six Sigma. Validar a medição consiste em dispor de instrumentos de medição com boa resolução e devidamente calibrados, uma definição operacional padrão clara e objetiva sobre como praticar as medições de forma correta e bons avaliadores treinados para medir, avaliar os resultados e decidir se há um viés de medição ou não para seguir ou não com o projeto.

As far as i know common mb includes operator variability, equipment inaccuracy, environmental factors & part-to-part variation & to minimize bias, we can use standardized procedures, calibrate equipment regularly, train operators & control environmental conditions. We can also implement a robust measurement system analysis to identify & quantify bias.

Don't rush. ... Plan your assessments carefully. ... Aim for assignments and questions that are crystal clear. ... Guard against unintended bias. ... Ask a variety of people with diverse perspectives to review assessment tools. ... Try out large-scale assessment tools.

The first thing we need to acknowledge is that there is more than one type of measurement. It's easier to identify and fix bias when we identify it for continuous variable measurements (through MSA, MSE, or GR&R studies). However, the bias can occur in surveys or qualitative measurements. Examples: a) 5-star metrics, used to measure service or product quality on a website, can be very biased, depending on when people are voting (e.g. right after buying or after a few weeks); b) in a survey, people can change their opinion just by changing the poll multiple answers order, or by having open voting instead of a closed voting session (check Scott Plous's The Psychology of Judgment and Decision Making book).

Human factors almost always play a role in measurement bias. If a process isn't automated, it can be tricky to duplicate a process exactly without certain human factors/conditions causing a deviation.

An example of measurement bias that I’ve witnessed is created by an expectation of the mean value, rather than determining the mean through objective data collection. For instance, you may expect a mean measurement of 5, but your measurements are showing a mean of 5.5; because of your expectations, you chalk this variance up to poor measurement and carry on assuming a mean of 5. This is prone to happen when the people who designed a process are the ones measuring the results, as they place too much confidence in their own process.

Measurement bias refers to a systematic error or distortion in the way data is collected, recorded, or analyzed, leading to inaccurate or misleading results. It can occur due to various factors, such as the design of measurement instruments, the methods used to collect data, or the interpretation of the data. Measurement bias can lead to skewed findings and affect the validity and reliability of research or assessments. For instance, if a measurement instrument consistently underestimates or overestimates certain values, it introduces bias into the data, potentially leading to incorrect conclusions. Identifying and addressing measurement bias is crucial for ensuring the accuracy and credibility of research findings.

Es evidente que lo primero que debemos fijarnos a la hora de realizar cualquier análisis dimensional, es verificar que la herramienta que utilizamos esta debidamente calibrada, luego hemos de evitar el sesgos personales como el sesgo de confirmación haciendo que nuestros datos se transformen en aquello que queremos y no lo que nuestros datos nos dicen realmente, pensar con objetividad y dejar que los datos hablen es clave para una buen análisis.

Senior managers should be accountable for allowing team members to use biased data. Fixing data acquisition error is of paramount importance to make company wide decisions.

Measurement bias is important because it can significantly impact the validity, reliability, & generalizability of research findings. Here are several reasons why measurement bias is significant: Accuracy of Results Measurement bias can lead to inaccurate measurements, which in turn affect the accuracy of research findings. If measurements consistently overestimate/underestimate certain variables, the conclusions drawn from the data may be flawed. Validity of Conclusions Biased measurements can distort the relationships between variables, leading to incorrect conclusions about the phenomena under study. This can undermine the validity of research findings & compromise the ability to make accurate prediction/ draw meaningful conclusions.

Identificar o viés de medição e corrigí-lo é ter a certeza dos dados do processo. Imagine um processo relativamente estável com um instrumento de medição descalibrado que passa despercebido? Iremos ajustar o controle do processo estável para um resultado com viés de medição e passaremos a entregar defeitos em função dos erros de medição. Por outro lado, um processo com alta variação, porém, com um sistema de medição confiável nos dará a convicção do que necessitamos melhorar facilitando a identificação da causa raiz da alta variação.

O viés de medição é fruto da discordância da leitura dos dados. Se dois avaliadores medem o cumprimento de uma peça com os mesmos instrumentos e técnicas e encontram resultados diferentes haverá um viés de medição, pois, as peças não aumentaram ou diminuíram de tamanho entre uma leitura e a outra (salvo características de deterioração acelerada no produto, como perecíveis, exalantes, etc..que passam por testes destrutivos). A falta de resolução, estabilidade, linearidade, repetibilidade e reprodutividade indicam a possível presença de viés de medição na avaliação de um MSA.

Use of a chart with control bands can create a quick, visual way for anyone to spot measurement bias. Trending the measurements over time makes it easy to detect most issues before they cause a problem.

Compare Multiple Measures Use multiple measures or methods to assess the same variable or construct. If different measures yield consistently different results, it may indicate measurement bias in one or more of the measures. Examine Consistency Assess the consistency of measurements over time or across different settings or populations. Inconsistent patterns or discrepancies may suggest measurement bias. Conduct Pilot Testing Before conducting a full-scale study, conduct pilot testing to identify and address potential sources of bias in the measurement instruments or procedures.

Things to consider during the measurement bias study: - define the sampling strategy that supports you to segregate measurement bias from measurement error: they will require different mitigation actions; - a clear measurement process map will help you to identify the possible sources of those errors; - if your measurement equipment has different modes to run, ensure you are spanning to all (e.g. all voltage ranges in a voltmeter); - ensure you are covering the full range of your measurement system, as not always the measurement bias is constant; - in case you take actions to fix the measurement bias, keep track and record new measurements vs. your standard to check if your measurement system remains stable through time.

Bifactor Models Bifactor models can be used to estimate the degree of bias in measurement instruments by decomposing observed scores into a general factor and specific factors related to bias. This approach allows researchers to quantify the amount of variance attributable to bias in the measurements. Differential Item Functioning (DIF) Analysis DIF analysis examines whether different groups within a sample respond differently to individual items on a measurement instrument, after controlling for the underlying construct being measured. Significant DIF indicates potential bias in the measurement instrument.

This is where the fourth “S” comes into play: standardize. Creating a standard operating procedure or standard protocols for measurement and data collection is a practical way to eliminate measurement bias.

Use Valid and Reliable Measurement Instruments Select measurement instruments that have been validated and demonstrated to reliably measure the constructs of interest. Ensure that the measurement instruments have adequate psychometric properties, such as validity, reliability, and responsiveness. Standardize Measurement Procedures Standardize the procedures for data collection, administration of measurement instruments, and scoring to minimize variability and ensure consistency across different settings and researchers.

1-Obtendo e calibrando instrumentos de medição que utilizem a regra dos "10 x mais". Não queira ler em milímetro utilizando escala de centímetro no instrumento de medição. 2- Crie uma definição operacional para a medição utilizando a simples técnica do 5W2H perguntando? O que será medido? Quem irá medir? Onde será medido? Quando será medido? Como será medido? Quanto custa medir? Por que iremos medir? 3- Treine os avaliadores com o padrão de medição estabelecido. 4- Verifique se a variação climática ou da temperatura (meio ambiente em geral) poderá influenciar na medição. 5- Compare as medições obtidas com um padrão de referência. 6- Faça benchmarking das medições