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A Corpus Based Quantitative Analysis of Gurmukhi Script
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
Gurjot Singh Mahi, Amandeep Verma
Median value separates the dataset into two halves, i.e., lower half and higher half, whereas mode symbolizes the value with the highest frequency in the dataset, where represents the calculated value for the words and sentences for the respective median and mode.
Quantitative Methods for Analyzing Experimental Studies in Patient Ergonomics Research
Published in Richard J. Holden, Rupa S. Valdez, The Patient Factor, 2021
Kapil Chalil Madathil, Joel S. Greenstein
Descriptive statistics include quantitative measures that enable a researcher to summarize the data collected from experimental studies. Some provide information regarding central tendency, including mean, median, and mode; others provide information regarding the dispersion of the data. The mean is the average value of the data set, the median is the value that divides the data set into equal halves, and the mode is the value that occurs most frequently. Though the arithmetic mean, median, and mode are used to summarize the central tendency of the majority of dependent variables, for certain dependent measures, such as the time taken to complete a task, small sample sizes will often have a positively skewed distribution. In this case, the geometric mean is used because it provides a better estimate of the middle value of the population when the distribution is skewed. To quantify the dispersion of data around a measure of central tendency, measures such as the variance, standard deviation, range (difference between the largest and smallest observations), and percentiles are used.
Experimental Design, Evaluation Methods, Data Analysis, Publication, and Research Ethics
Published in Yuehuei H. An, Richard J. Friedman, Animal Models in Orthopaedic Research, 2020
The median is the central value in a set of ordered numbers. Unlike the mean, every number in a set does not enter into the computation of the median. The strength of the median is that it is not sensitive to the extreme scores in a set of data. It is appropriately used for analysis of data with a skewed distribution, such as the “time to recurrence.” The mode is simply the most frequently seen value in a set of data. It is most useful for demonstrating the clustering of values in a set of data.12 The minimum value is the smallest value in a set of data and the maximum value is the largest one. The range is the absolute value of the difference between the minimum and the maximum values. These are simplified expressions of the variability of a set of numbers. Often they do not accurately represent the rest of the dataset, but they are easy to document and interpret.
Refinements of the ICF Linking Rules to strengthen their potential for establishing comparability of health information
Published in Disability and Rehabilitation, 2019
Alarcos Cieza, Nora Fayed, Jerome Bickenbach, Birgit Prodinger
Health information is also collected by different modes. Information may be collected by means of a technical or clinical test, by a self-report instrument, in terms of expert, or proxy judgment, or by means of a population survey. Thus, joint inflammation can be assessed with laboratory tests, by means of a joint ultrasound, self-report, or by means of a professional judgment by a health professional. Furthermore, because of differences in measurement units and the meaning of the terms in a response scale, it might be a challenge to compare the health information, that is, collected.[5] For instance, the Brief Pain Inventory [6] asks to rate the pain in the last 24 h on a scale from 0 “No pain” to 10 “Pain” as bad as you can imagine. The West Haven-Yale Multidimensional Pain Inventory [7] asks to rate the pain at the present moment from 0 “No pain” to 6 “Very intense pain”. While both items aim to gather information about the level of pain of a person, a rating of 5 has a different meaning on these two scales.
Comprehensive characterisation of the heterogeneity of adalimumab via charge variant analysis hyphenated on-line to native high resolution Orbitrap mass spectrometry
Published in mAbs, 2019
Florian Füssl, Anne Trappe, Ken Cook, Kai Scheffler, Oliver Fitzgerald, Jonathan Bones
The surface charge of a mAb can be subject to change by several of the modifications mentioned above, either by providing or depleting acidic or basic residues or by changing protein higher order structure.7 This surface charge diversity has been utilised in the past to analyse mAb heterogeneity with charge sensitive methods, most notably cation exchange chromatography (CEX). CEX can be used in either salt gradient or pH-gradient elution mode, which is the chromatographic equivalent to isoelectric focusing.8–10 Both modes have been applied successfully and were subject to a series of comparison studies with diverse outcomes.11–13 In industry, charge variant analysis (CVA) is commonly used for direct comparison of different product batches to ensure consistent product quality. Recently CVA, applying both weak cation exchange (WCX) and strong cation exchange (SCX) chromatography, was used in a process analytical technology (PAT) framework as a powerful tool to control and optimise upstream and downstream processing.14 While well suited for comparability studies, one limitation is the necessity to perform elaborate sample preparation procedures and follow-up experiments for initial identification of the peaks and in case of any out-of-specification events. These experimental procedures usually involve peak collection followed by buffer exchange to allow further analysis, which can be time consuming and costly.15 More importantly, these procedures are a possible source for sample preparation-induced modifications.
Validated repeatability of patient-reported outcome measures following primary total hip replacement: a mode of delivery comparison study with randomized sequencing
Published in Acta Orthopaedica, 2018
Charlotte V E Carpenter, Julia Blackburn, John Jackson, Ashley W Blom, Adrian Sayers, Michael R Whitehouse
There are 2 possible methods to analyze mode comparison studies. The first is to assume independence on each occasion a questionnaire is completed. This analytical standpoint is typically described as investigation of between-population differences. In this case, linear regression was used to estimate the between-population mean difference of each questionnaire by delivery mode of delivery. The second method of analysis investigates within-individual (paired) differences of each questionnaire by mode of delivery. In this case, the paired difference between each mode was calculated and linear regression was used to estimate any within individual differences between modes of delivery. Within-patient analyses were further adjusted for the time between completion of questionnaires (time delay) and age of the participant, i.e., < 70 or ≥70 years and old. P-values are reported without adjustment for multiple testing (Perneger 1998). Data were analyzed using STATA (version 14, StataCorp, College Station, TX, USA).