Survey Research Methods in Functional GI Disorders
Kevin W. Olden in Handbook of Functional Gastrointestinal Disorders, 2020
Analysis of variance (ANOVA) is a technique used to assess the reliability of a measure or value. The variability in the answers given by different respondents and in evaluations given by different observers, as well as the variability that exists randomly, are all assessed by ANOVA. An analysis of variance can quantify how each source of variability contributes to the overall variability of a . measurement. In the functional GI disorders, ANOVA has been used extensively to test for reliability of symptom measures. In one study to develop a numerical index of illness severity, ANOVA was used to compare physicians’ ratings of illness severity across several study sites to assess variability in observers (28). Another application of ANOVA techniques in the functional GI disorders is the comparison of the composition of a sample by demographic or other characteristics in order to assess the comparability of results from different study sites (28).
Inferential statistics
Louis Cohen, Lawrence Manion, Keith Morrison in Research Methods in Education, 2017
Analysis of Variance (ANOVA) requires: continuous parametric data;random sampling;normal distribution of the data (though large samples often overcome this);homogeneity (equality) of variances (though the Levene test can identify problems here, and SPSS can offer the Brown-Forsythe and Welch tests to overcome the problem here, discussed below). There are several kinds of Analysis of Variance; here we introduce only the three most widely used versions: the one-way Analysis of Variance, the two-way Analysis of Variance and Multiple Analysis of Variance (MANOVA). Analysis of Variance, like the t-test, assumes that the independent variable(s) is/are categorical (e.g. teachers, students, parents, governors) and one is a continuous variable (e.g. marks on a test). It calculates the F ratio, given as: ANOVA calculates the means for all the groups and then it calculates the average of these means. For each group separately it calculates the total deviation of each individual’s score from the mean of the group (within-groups variation). Finally it calculates the deviation of each group mean from the grand mean (between-groups variation).
Comparing Group Means When the Standard Assumptions Are Violated
Mohamed M. Shoukri in Analysis of Correlated Data with SAS and R, 2018
The majority of statistical experiments are conducted so as to compare two or more groups. It is understood that the word group is a generic term used by statisticians to label and distinguish the individuals who share a set of experimental conditions. For example, diets, breeds, age intervals, and methods of evaluations are groups. In this chapter we will be concerned with comparing group means. When we have two groups, that is, when we are concerned with comparing two means μ1 and μ2, the familiar Student’s t statistic is the tool that is commonly used by most data analysts. If we are interested in comparing several group means, that is, if the null hypothesis is H0∶μ1 = μ2 = ⋯ = μk, a problem known as the analysis of variance (ANOVA), we use the F-ratio to seek the evidence in the data and see whether it is sufficient to justify the hypothesis.
Ovarian reserve analysis in subfertile women based on physical, ultrasound and hormonal parameters
Published in Gynecological Endocrinology, 2023
The descriptive, inferential statistics have been utilized to determine the correlation coefficient, and consequently, the detection of association with the marker’s correlation coefficient was also performed. Various ultrasound and hormonal parameters were noted in the excel sheets, and a comparative analysis was performed with Statistical analysis. Statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 20.0. Mean standard deviation and range of values were estimated, and the correlation coefficients were determined with every set of values and Microsoft Excel 2010 was used for all statistical analyses. The frequency analysis presented the demographic, physical parameters, age and BMI as the count and percentage. ANOVA (analysis of variance) is a test used to test the significant difference among the groups. If the p value is smaller than .05, then it is shown that respondents differ significantly in this study. The two-way ANOVA test was used to assess the differences between the mean values of ultrasound and hormonal parameters in the different physical parameter groups. Bonferroni’s method revealed the significant difference between the different types of treatment. In this study, Bonferroni t test, multiple comparisons test was used to compare the physical with hormonal and ultrasound parameters and the relationship between these parameters is assessed. The study represents a multivariate analysis and is presented in a table to depict the interrelation of all the parameters to determine their corresponding significant value.
Analysis of means approach for random factor analysis
Published in Journal of Applied Statistics, 2018
Kalanka P. Jayalath, Hon Keung Tony Ng
The analysis of variance (ANOVA) is one of the commonly used statistical techniques for testing differences between two or more means. The analysis of means (ANOM) is an alternative graphical procedure for multiple group comparisons with an overall mean. The ANOM was first proposed by Ott [13] and it was further developed by many authors (see, for example, [1,8,9,15,16,18]). The ANOM is essentially a graphical testing procedure similar to the Shewhart control chart which allows comparisons of all the treatment means with respect to the overall mean simultaneously. The major advantages of the ANOM procedure over the ANOVA are that the ANOM is visually appealing and it provides a quick way for readers, especially non-statisticians, to assess practical and statistical significant differences between the treatment means and the overall mean. The ANOM procedure has become increasingly popular among practitioners in recent years and it has been implemented in many statistical software packages such as SAS (PROC ANOM), MINITAB, JMP, and R (ANOM package developed by Pallmann and Hothorn [16]). For a comprehensive comparative study of the performance of the ANOVA and ANOM approaches for one-way ANOVA, one may refer to the paper by Mendeş and Yiğit [6].
Acquisition of Tok Pisin phonology in the multilingual highlands of Papua New Guinea
Published in International Journal of Speech-Language Pathology, 2022
Jennifer Boer, Mary Claessen, Cori Williams
Each child’s phoneme and PCC scores were incorporated into a single line of data per participant for entry into SPSS (Version 25. 2017), in order to generate further descriptive and inferential statistics. Descriptive measures of PCC were obtained for the group as a whole and for each 12-month age group. The assumptions of independence, normal distribution, and homogeneity for one-way between groups analysis of variance (ANOVA) were tested. Participant data were independent. The normal distribution of the PCC was examined with the Shapiro-Wilk test, descriptive statistics, histograms, and boxplots. Homogeneity of variance was examined with Levene’s test. An ANOVA was used to investigate the impact the age group had on mean PCC. Post hoc Tukey's HSD tested the significance of individual 12-month age group comparisons. A univariate analysis of ANOVA was used to calculate an omnibus measure of effect size (η2 =.27).
Related Knowledge Centers
- Null Hypothesis
- Observational Study
- Permutation Test
- Randomized Controlled Trial
- Statistical Hypothesis Testing
- Law of Total Variance
- Personal Equation
- Dependent & Independent Variables
- F-Test
- P-Value