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The document discusses experimental design and analysis for measuring reliability between two therapists. It describes how interclass correlation coefficient (ICC) and Bland & Altman plots are used to assess reliability of ultrasound measurements of muscle tissue taken by two therapists on 10 participants. The ICC between the therapists was found to be 0.92, indicating high reliability, though the confidence interval of 0.72 to 0.98 was considered wide.
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Topic: What is Reliability and its Types?
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Lab 7 Template1. Using the data you collected for the Week 5 .docx
Lab 7 Template
1. Using the data you collected for the Week 5 Lab (heights of 10 different people that you work with plus the 10 heights provided by your instructor), discuss your method of collection for the values that you are using in your study (systematic, convenience, cluster, stratified, simple random). What are some faults with this type of data collection? What other types of data collection could you have used, and how might this have affected your study?
Use the Week 6 Spreadsheet to help you with calculations for the following questions/statements.
2. Give a point estimate (mean) for the average height of all people at the place where you work. Start by putting the 20 heights you are working with into the blue Data column of the spreadsheet. What is your point estimate, and what does this mean?
3. Find a 95% confidence interval for the true mean height of all the people at your place of work. What is the interval?
4. Give a practical interpretation of the interval and explain carefully what the output means. (For example, you might say, "I am 95% confident that the true mean height of all of the people in my company is between 64 inches and 68 inches").
5. Post a screenshot of your work from the t value Confidence Interval for µ from the Confidence Interval tab on the Week 6 Excel spreadsheet.
6. Now, change your confidence level to 99% for the same data, and post a screenshot of this table/interval, as well.
7. Compare the margins of error from the two screenshots. Would the margin of error be larger or smaller for the 99% CI? Explain your reasoning.
8. Save this template with your answers and include your name in the title.
9. Submit the document.
J
LU
TERMINOLOGY 101
Confidence intervals: Part 2
MAHER M. EL-MASRI, RN, PhD, IS AN ASSOCIATE PROFESSOR AND RESEARCH LEADERSHIP CHAIR
IN THE FACULTY OF NURSING, UNIVERSITY OF WINDSOR, IN WINDSOR, ONT.
Confidence interval: The range of values, consistent with the data, that is believed to encompass the actual or
"true" population value
Source: Lang, T.A., & Secic, M. (2006). How to Report Statistics in Medicine. (2nd ed.). Philadelphia: American College of Physicians
Part 1, which appeared in the February 2012
issue, introduced the concept of confidence
intervals (CIs) for mean values. This article
explains how to compare the CIs of two mean
scores to draw a conclusion about whether or
not they are statistically different. Two mean
scores are said to be statistically different if their
respective CIs do not overlap. Overlap of the CIs
suggests that the scores may represent the same
"true" population value; in other words, the true
difference in the mean scores may be equivalent
NurseONE resources
ON THIS TOPIC
EBSCO-MEDLINE FULL-TEXT ARTICLES
• Hildebrandt, M., Vervölgyi, E., & Bender, R. (2009).
Calculation of NNTs in RCTs with time-to-event
outcomes: A literature review. BMC Medical
Research Methodology, 9,21.
• Hildebrandt, M., Bender, .
Representing and generating uncertainty effectively presentatıon
Prof. Frank H Knight (1921) proposed that "risk" is randomness with knowable probabilities, and "uncertainty" is randomness with unknowable probabilities. However, risk and uncertainty both share features with randomness. The illustration here explains the relationship of the concepts better than words...
cannonicalpresentation-110505114327-phpapp01.pdf
This document summarizes a study that used canonical correlation analysis to detect potential bias in faculty promotion scores at American University of Nigeria. The study aimed to test if canonical correlation could identify bias scoring, determine the influence of individual assessors' scores, and discriminate between promotable and non-promotable candidates. The results showed that canonical correlation could detect bias and influence with over 90% confidence and correctly classified candidates into promotable and non-promotable groups, rejecting the null hypotheses. Thus, canonical correlation was found to be an effective statistical tool for unbiased promotion scoring and decision making at the university.
Lecture note 2
This document provides an overview of standards of measurement and discusses key concepts:
- Standards are classified as primary or secondary, with primary standards defining fundamental units and secondary standards calibrated against primary standards.
- Standard units discussed include the meter (length), kilogram (mass), second (time), Kelvin (temperature), candela (light intensity), mole (amount of substance), and ampere (electric current).
- Random and systematic errors are defined, with random errors averaging out over repeated measurements but systematic errors requiring correction. Significant figures and calculating relative/absolute errors are also covered.
Agreement analysis
This document discusses various statistical tests used to analyze agreement between raters or tests, including intraclass correlation, Cohen's kappa, and Bland-Altman plots. It explains how to perform intraclass correlation, Cohen's kappa, receiver operating characteristic curves, and other tests on SPSS. These statistical analyses are used to evaluate rater agreement, compare tests to a gold standard, and determine if tests provide predictions better than chance. The document provides guidance on interpreting the results of these analyses and choosing appropriate cut-off values.
Accuracy precision and significant figures
This document defines accuracy, precision, and significant figures in analytical chemistry.
Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of repeated measurements. A measurement can be precise without being accurate.
There are two methods to determine accuracy - absolute and comparative. The absolute method uses samples of known composition, while the comparative method uses secondary standards. Precision is measured by tests of repeatability and reproducibility.
The number of significant figures indicates the certainty of a measurement and must be considered when performing calculations to avoid losing accuracy. Rules are provided for determining significant figures in addition, subtraction, multiplication, division and other operations.
Statistical inference: Estimation
The ppt gives an idea about basic concept of Estimation. point and interval. Properties of good estimate is also covered. Confidence interval for single means, difference between two means, proportion and difference of two proportion for different sample sizes are included along with case studies.
Confidence Intervals in the Life Sciences PresentationNamesS.docx
Confidence Intervals in the Life Sciences Presentation
Names
Statistics for the Life Sciences STAT/167
Date
Fahad M. Gohar M.S.A.S
1
Conservation Biology of Bears
Normal Distribution
Standard normal distribution
Confidence Interval
Population Mean
Population Variance
Confidence Level
Point Estimate
Critical Value
Margin of Error
Welcome to the presentation on Confidence Intervals of Conservation Biology on Bears.
The team will define normal distribution and use an example of variables why this is important. A standard and normal distribution is discussed as well as the difference between standard and other normal distributions. Confidence interval will be defined and how it is used in Conservation Biology and Bears. We will learn how a confidence interval helps researchers estimate of population mean and population variance. The presenters defined a point estimate and try to explain how a point estimate found from a confidence interval. Confidence level is defined and a short explanation of confidence level is related to the confidence interval. Lastly, a critical value and margin of error are explained with examples from the Statdisk.
2
Normal Distribution
A normal distribution is one which has the mean, median, and mode are the same and the standard deviations are apart from the mean in the probabilities that go with the empirical rule. Not all data has the measures of central tendency, since some data sets may not have one unique value which occurs more than once. But every data set has a mean and median. The mean is only good with interval and ratio data, while the median can be used with interval, ratio and ordinal data. Mean is used when they're a lot of outliers, and median is used when there are few.
The normal distribution is continuous, and has only two parameters - mean and variance. The mean can be any positive number and variance can be any positive number (can't be negative - the mean and variance), so there are an infinite number of normal distributions. You want your data to represent the population distribution because when you make claims from the distribution of the sample you took, you want it to represent the whole entire population.
Some examples in the business world: Some industries which use normal distributions are pharmaceutical companies. They model the average blood pressure through normal distributions, and can make medicine which will help majority of the people with high blood pressure. A company can also model its average time to create something using the normal distribution. Several statistics can be calculated with the normal distribution, and hypothesis tests can be done with the normal distribution which models the average time.
Our chosen life science is BEARS. The age of the bears can be modeled by normal distributions and it is important to monitor since that tells us the average age of the bear, and can tell us a lot about the population. If the mean is high and the standard deviatio.
STRUCTURAL EQUATION MODEL (SEM)
ABSTRACT : This paper critically examined a broad view of Structural Equation Model (SEM) with a view
of pointing out direction on how researchers can employ this model to future researches, with specific focus on
several traditional multivariate procedures like factor analysis, discriminant analysis, path analysis. This study
employed a descriptive survey and historical research design. Data was computed viaDescriptive Statistics,
Correlation Coefficient, Reliability. The study concluded that Novice researchers must take care of assumptions
and concepts of Structure Equation Modeling, while building a model to check the proposed hypothesis. SEM is
more or less an evolving technique in the research, which is expanding to new fields. Moreover, it is providing
new insights to researchers for conducting longitudinal investigations.
.
Practical Methods To Overcome Sample Size Challenges
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
IRJET- An Empirical Study on the Relationship Between Meditation, Emotion...
The document presents a study that examines the relationship between meditation, emotional intelligence, and subjective well-being through structural equation modeling. The study assessed 727 meditators in Erode District, India and found that meditation positively impacts emotional intelligence and the five dimensions of subjective well-being, including achievement, charisma, diplomacy, progressiveness, and optimism. The study concluded that increasing emotional intelligence through meditation enhances subjective well-being.
Evidential reasoning based decision system to select health care location
The general public’s demand of Bangladesh for safe health is rising promptly with the improvement of the living standard. However, the allocation of limited and unbalanced medical resources is deteriorating the assurance of safe health of the people. Therefore, the new hospital construction with rational allocation of resources is imminent and significant. The site selection for establishing a hospital is one of the crucial policy-related decisions taken by planners and policy makers. The process of hospital site selection is inherently complicated because of this involves many factors to be measured and evaluated. These factors are expressed both in objective and subjective ways where as a hierarchical relationship exists among the factors. In addition, it is difficult to measure qualitative factors in a quantitative way, resulting incompleteness in data and hence, uncertainty. Besides it is essential to address the subject of uncertainty by using apt methodology; otherwise, the decision to choose a suitable site will become inapt. Therefore, this paper demonstrates the application of a novel method named belief rulebased inference methodology-RIMER base intelligent decision system(IDS), which is capable of addressing suitable site for hospital by taking account of large number of criteria, where there exist factors of both subjective and objective nature.
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my reliability
- 1. Experimental Design and Analysis CE: 540 Y.Doc.Dr. M. TOLGA GOGUS Present By : Mustafa Mohammed Rashid
- 19. EXAMPLE A comparison of the reliability of measurements from two therapists was performed. Data from real time ultrasound imaging of a muscle in 10 participants, one reading per therapist, are recorded in columns 2 and 3 in Table. Do the two therapists produce 'reliable' readings?
- 20. Two techniques exploring the variability of the data to gauge reliability are demonstrated; interclass correlation coefficient (ICC) and Bland & Altman plot. There are various forms of ICC and they are discussed in the paper, along with their associated labels and formulae for calculation, although the worksheet uses SPSS for their calculations. The Bland & Altman plot is illustrated in MS Excel. An ICC is measured on a scale of 0 to 1; 1 represents perfect reliability with no measurement error, whereas 0 indicates no reliability.
- 22. Our estimated reliability between therapists is 0.92, with 95% CI (0.72, 0.98), which is quite 'wide'.
- 23. Comments The Rankin & Stokes (1998) paper gives much more detailed discussion around measures of reliability. In particular they give references for the following comments: • Pearson’s correlation coefficient is an inappropriate measure of reliability because the strength of linear association, and not agreement, is measured (it is possible to have a high degree of correlation when agreement is poor. • A paired t-test assesses whether there is any evidence that two sets of measurements agree on average. However, it is the difference between within-subjects scores that is of interest (taking the mean score of all subjects has potential to provide misleading estimates). • A high scatter of individual differences can result in the difference between the means being non-significant. • It is no longer considered to be appropriate (in most cases) to use the coefficient of variation (CV) to calculate reliability.