The Use of a Small Computer in Agricultural Research

1977 ◽  
Vol 13 (3) ◽  
pp. 257-264 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Analysis Of Variance ◽  
Agricultural Research ◽  
Error Variance ◽  
Analysis Of Covariance ◽  
Research Worker ◽  
Small Computer ◽  
Disturbing Factor

SUMMARYIt is suggested that quite a small computer, in association with a minimal program for the analysis of variance, can be used to calculate quantities of use to the agronomic research worker beyond what he usually obtains. For example, without further programs it is possible to calculate an analysis of covariance, in which adjustment is made for some disturbing factor. It is also possible to find how much of the error variance arises from a particular plot, and to deal with situations in which data are incomplete or the yields from two plots have become mixed.

The Interpretation of Regression Analysis and the Covariance Adjustment, with Particular Reference to Agricultural Research

1973 ◽  
Vol 9 (4) ◽  
pp. 369-379
Author(s):  
N. A. Campbell ◽  
N. A. Goodchild
Keyword(s):  
Regression Analysis ◽  
Analysis Of Variance ◽  
Regression Coefficient ◽  
Agricultural Research ◽  
Covariance Analysis ◽  
Analysis Of Covariance ◽  
Covariance Adjustment

SUMMARYThe aim of this paper is to help biologists gain a clearer understanding of what is involved in covariance analysis. It is an exposition on the derivation of the regression coefficient in the analysis of covariance, and hence on the interpretation of the covariance adjustment. The covariance adjustment is shown to be equivalent to regressing the residuals from an analysis of variance of the original variate on the residuals from an analysis of variance of the covariate.

Religious Values and their Effect on the Perception of a Therapist in a Psychotherapy Analogue

1976 ◽  
Vol 4 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Carl D. Haugen ◽  
Keith J. Edwards
Keyword(s):  
Analysis Of Variance ◽  
Religious Orientation ◽  
Pearson Correlation ◽  
Value Orientation ◽  
Analysis Of Covariance ◽  
Control Group ◽  
Interpersonal Style ◽  
Positive Correlation ◽  
Dependent Variables ◽  
The Relationship

The purpose of the study was, first, to determine whether labeling a taped therapist in terms of the therapist's religious value orientation (Christian/non-Christian) and interpersonal style (warm/cold) would change religious subjects’ perceptions of the relationship. Second, it was to determine whether the strength of attraction of the religious value orientation was greater than the interpersonal style. Seventy-one Christian evangelical undergraduates were randomly assigned to five groups. Four groups were given different information with regard to a therapist's warmth and Christianity. The fifth group acted as a control. Following structuring, all subjects listened to the same tape of a simulated therapy session. At the conclusion, the subjects rated the tape using scales to measure the dependent variables of attraction, receptivity, persuasibility, and willingness to meet. A two by two and one by five analysis of covariance and analysis of variance were computed. The only significant results found were that the control group evidenced more persuasibility than the Christian/cold and non-Christian/cold groups. Examination of group means showed a trend in the direction hypothesized for attraction and receptivity. A Pearson correlation was computed to determine the relationship between perception of the therapist's religious orientation and the dependent variables and perception of the therapist's interpersonal style and the dependent variables. There was a positive correlation between rating the therapist as Christian and the dependent variables of attraction and receptivity, p<.05. There was also a positive correlation between rating the therapist as warm and attraction and receptivity, p<.05.

Empirical Formulae for Estimating Appropriate Sample Sizes for Analysis of Variance Designs

1968 ◽  
Vol 27 (2) ◽  
pp. 363-367 ◽  
Author(s):  
John E. Overall ◽  
Sudhir N. Dalal
Keyword(s):  
Sample Size ◽  
Analysis Of Variance ◽  
Error Variance ◽  
Sampling Unit ◽  
Sample Sizes ◽  
Constant Power ◽  
Treatment Difference ◽  
High Side ◽  
Power Curves ◽  
Size Estimates

Simple empirical formulae are presented for estimating appropriate sample size for simple randomized analysis of variance designs involving 2, 3, 4 or 5 treatments. In order to use these formulae one must specify the magnitude of a meaningful treatment difference and must have an estimate of the error variance. Sample size estimates derived from the simple formulae have been found to differ from values obtained using constant power curves by no more than one sampling unit on the low side and no more than two sampling units on the high side.

Influence of Ball Weight on Junior High School Girls' Volleyball Performance

1994 ◽  
Vol 78 (3_suppl) ◽  
pp. 1379-1384 ◽  
Author(s):  
Tracy L. Pellett ◽  
Heidi A. Henschel-Pellett ◽  
Joyce M. Harrison
Keyword(s):  
High School ◽  
Success Rate ◽  
Analysis Of Variance ◽  
Junior High School ◽  
Analysis Of Covariance ◽  
Seventh Grade ◽  
Game Play ◽  
High School Girls ◽  
School Girls ◽  
Practice Period

This study was designed to investigate the influence of a lighter ball (Tachikara Volley Lite) on 72 seventh-grade girls' tournament game play and pretest-to-posttest improvement for a 16-day volleyball practice period. Two intact classes were randomly assigned to groups, one of whom used lighter balls during skills progressions while a second used regulation balls. All students used regulation balls during tournament game play and skills tests. Both groups significantly improved the forearm pass from pretest to posttest. Analysis of covariance indicated no significant differences between groups on posttest means for any skill. A 2 × 6 (treatment x game day) analysis of variance indicated that the group practicing with lighter balls had significantly more correct sets and a higher average daily success rate for the set and underhand serve on game days than the group using a regulation ball.

Anova of Residuals and Ancova: Correcting an Illusion by Using Model Comparisons and Graphs

1985 ◽  
Vol 10 (3) ◽  
pp. 197-209 ◽  
Author(s):  
Scott E. Maxwell ◽  
Harold D. Delaney ◽  
Jerry M. Manheimer
Keyword(s):  
Analysis Of Variance ◽  
Regression Coefficient ◽  
Total Sample ◽  
Significance Test ◽  
Statistical Test ◽  
Analysis Of Covariance ◽  
Model Comparisons ◽  
Group B ◽  
Α Level ◽  
Single Set

Analysis of covariance is often conceptualized as an analysis of variance of a single set of residual scores that are obtained by regressing the dependent variable on the covariate. Although this conceptualization of an equivalence between the two procedures may be intuitively appealing, it is mathematically incorrect. If residuals are obtained from the pooled within-groups regression coefficient ( bw), an analysis of variance on the residuals results in an inflated α-level. If the regression coefficient for the total sample combined into one group ( bT) is used, ANOVA on the residuals yields an inappropriately conservative test. In either case, analysis of variance of residuals fails to provide a correct test, because the significance test in analysis of covariance requires consideration of both bw and bT, unlike analysis of residuals. It is recommended that the significance test of treatment effects in analysis of covariance be conceptualized, not as an analysis of residuals, but as a comparison of models whose parameters are estimated by the principle of least squares. Focusing on model comparisons and their associated graphs can be used effectively here as in other cases to teach simply and correctly the logic of the statistical test.

scholarly journals Evaluation of Newly Developed Rice Lines in Lowland Ecology in Ghana

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Esther Fobi Donkor ◽  
Remember Roger Adjei ◽  
Sober Ernest Boadu
Keyword(s):  
Moisture Content ◽  
Analysis Of Variance ◽  
Agronomic Traits ◽  
Agricultural Research ◽  
Principal Component ◽  
Research Field ◽  
Rice Varieties ◽  
Statistical Tool ◽  
Cophenetic Correlation ◽  
Cophenetic Correlation Coefficient

Rice is one of the major staple foods consumed in all part of the world including Ghana. The study was conducted at the research field of CSIR in Sokwai to evaluate the performance of the newly released rice varieties in lowland ecology in Ghana. The data collected were subjected to analysis of variance using Statistical Tool for Agricultural Research Version 2.0.1. The analysis of variance revealed a highly significant variation among most of the agronomic traits studied except for the panicle length, moisture content, total weight of 5 hills, the percent moisture content and the lodging index. The first five principal component (PC) accounted for 71% of the total variation with PC1, PC2 and PC3 contributing 20%, 17% and 13% respectively. The cluster analysis placed the accessions into five main clusters. The cophenetic correlation coefficient (CCC) was 0.42.

Analyzing pre-post designs using the analysis of covariance models with and without the interaction term in a heterogeneous study population

2019 ◽  
Vol 29 (1) ◽  
pp. 189-204 ◽  
Author(s):  
Fei Wan
Keyword(s):  
Treatment Effect ◽  
Treatment Strategies ◽  
Interaction Model ◽  
Error Variance ◽  
Ordinary Least Squares ◽  
Conditional Variance ◽  
Analysis Of Covariance ◽  
Parallel Group ◽  
Interaction Term ◽  
Variable Bias

Pre-post parallel group randomized designs have been frequently used to compare the effectiveness of competing treatment strategies and the ordinary least squares (OLS)-based analysis of covariance model (ANCOVA) is a routine analytic approach. In many scenarios, the associations between the baseline and the post-randomization scores could differ between the treatment and control arms, which justifies the inclusion of the treatment by baseline score interaction in ANCOVA. This heterogeneity may also cause heteroscedastic errors in ANCOVA. In this study, we compared the performances of the ANCOVA models with and without the interaction term in estimating the marginal treatment effect in a heterogeneous two-arm pre-post design. We explored the relationship between the two nested ANCOVA models from the perspective of an omitted variable bias problem and further revealed the reasons why the usual ANCOVA may fail in heterogeneous scenario through the discussion of the three types of variances associated with the ANCOVA estimators of the marginal treatment effect: the target unconditional variance, the conditional variance allowing unequal error variances, and the OLS conditional variance derived under the assumption of constant error variance. We demonstrated analytically and with simulations that the proposed heteroscadastic-consistent variance estimators provide valid unconditional inference for ANCOVA, and the ANCOVA interaction model is more powerful than the ANCOVA main effect model when a design is unbalanced.

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