Split-Plot Factorial Design

2012 ◽  
Keyword(s):  
Factorial Design ◽  
Split Plot

scholarly journals CHANGES IN THE CHARACTERISTICS OF ‘PRATA’ BANANA TREATED WITH CYTOKININ AND GIBBERELLIN

2016 ◽  
Vol 38 (3) ◽  
Author(s):  
JULIANA DOMINGUES LIMA ◽  
JÉSSICA SANTA ROSA ◽  
DANILO EDUARDO ROZANE ◽  
EDUARDO NARDINI GOMES ◽  
SILVIA HELENA MODENESE GORLA DA SILVA
Keyword(s):  
Plant Growth ◽  
Gibberellic Acid ◽  
Factorial Design ◽  
Plant Growth Regulators ◽  
Growth Regulators ◽  
Physicochemical Characteristics ◽  
Fruit Yield ◽  
Musa Spp ◽  
Yield And Quality ◽  
Split Plot

ABSTRACT Plant growth regulators can influence fruit yield and quality. This study aimed to evaluate the effect of cytokinin and gibberelin on the agronomic and physicochemical characteristics of banana fruits cv. ‘Prata’ (Musa spp. AAB), according to the formation period and position in the bunch. The experiment was conducted in a completely randomized 2 x 5 factorial design, two periods of bunch development (summer and winter), five treatments and ten replicates. To study the effect of position in the bunch, split plot was adopted, considering in the plot, 2 x 5 factorial and in subplots, hand 1, hand 4 and last hand. Treatments consisted of 2 pulverizations with water, 150 mg L-1 cytokinin, 200 mg L-1 of gibberellic acid, 100 mg L-1 of cytokinin plus 200 mg L-1 of gibberellic acid and 200 mg L-1 of cytokinin plus 200 mg L-1 of gibberellic acid, applied from the fourth to the last hand of the bunch. Cytokinin and gibberellin, alone or associated, regardless of formation period and position, did not affect the size and physicochemical characteristics of fruits, only delayed the bunch harvest.

Ten-year Growth Response of Black Spruce to Thinning and Fertilization Treatments

1975 ◽  
Vol 5 (2) ◽  
pp. 302-309 ◽  
Author(s):  
G. F. Weetman
Keyword(s):  
Synergistic Effect ◽  
Factorial Design ◽  
Growth Response ◽  
Black Spruce ◽  
Stand Density ◽  
Nitrogen Addition ◽  
Size Classes ◽  
Split Plot ◽  
Nitrogen Additions

A 65-year-old upland black spruce (Piceamariana Mill. B.S.P.) stand near Baie Comeau, Quebec, was thinned and fertilized with urea; each treatment was at 2 levels in a split plot factorial design. The trees responded to the nitrogen addition after 1 year and to thinning after 7 years. The trees were still growing faster in response to most treatments after 10 years. Trees of all size classes responded to the treatments. The periodic increment was not increased by 25% thinning, but was increased by 50% thinning and was always increased by nitrogen additions. Absolute increases over control values ranged from 130 to 290 ft3 (11 to 24.6 m3) for an application of 100 lb N per acre (112 kg N per hectare) and 238 to 297 ft3 (20 to 25.2 m3) for 400 lb N per acre (444 kg N per hectare) (1 ha = 104 m2). Some synergistic effect of combined thinning and fertilizer treatments is indicated.Mortality losses in the 10-year period were appreciable because of excessive stand density. The roles of thinning and fertilizer treatments in black spruce management are discussed.

scholarly journals Optimization of Split-Plot Design in the Context of Mixture Process Variable Settings

2021 ◽  
pp. 1-36
Author(s):  
Samson W. Wanyonyi ◽  
Ayubu A. Okango ◽  
Julius K. Koech ◽  
Betty C. Korir
Keyword(s):  
Factorial Design ◽  
Process Variable ◽  
Optimality Criteria ◽  
Mixture Design ◽  
Parameter Estimates ◽  
Process Variables ◽  
Split Plot ◽  
Set Up ◽  
Plot Structure ◽  
Plot Design

In the presence of process variables, a mixture design has become well-known in statistical modeling due to its utility in modeling the blending surface, which empirically predicts any mixture's response and serves as the foundation for optimizing the expected response blends of different components.  In the most common practical situation involving a mixture-process variable, restricted randomization occurs frequently. This problem is solved when the split-plot layout arrangement is used within the constraints. This study's primary goal was to find the best split-plot design (SPD) for the settings mixture-process variables. The SPD was made up of a simplex centroid design (SCD) of four mixture blends and a factorial design with a central composite design (CCD) of the process variable and compared six different context split-plot structure arrangement.  We used JMP software version 15 to create D-optimal split-plot designs. The study compared the constructed designs' relative efficiency using A-, D-, I-, and G- optimality criteria. Furthermore, a graphical technique (fraction of design space plot) was used to display, explain, and evaluate experimental designs' performance in terms of precision of the six designs' variance prediction properties. We discovered that arranging subplots with more SCD points than pure mixture design points within SPD with two high process variables is more helpful and provides more precise parameter estimates. We recommend using SPDs in experiments involving mixture process settings developments to measure the mixture components' interaction effects and the processing conditions. Also, the investigation should be set up at each of the points of a factorial design.

Effects of a Brief Video Intervention on Attitudes Toward Counseling Services

2021 ◽  
pp. 003329412110519
Author(s):  
Eun Hye Park ◽  
Sang Min Lee
Keyword(s):  
Factorial Design ◽  
Counseling Services ◽  
Intervention Effects ◽  
Video Intervention ◽  
Attitudes Toward Counseling ◽  
Split Plot ◽  
And Control ◽  
Implementing Strategies

This study investigated the effects of a brief video intervention on attitudes toward counseling services. Two hundred and seventy-seven participants were divided into four groups (anxious-preoccupied, dismissive-avoidant, fearful-avoidant, and secure) by their attachment scores. Then, the participants of each group were randomly assigned to three conditions (stigma-reducing, utility-enhancing, and control). A split-plot factorial design was performed to examine the intervention effects. The results indicate that the stigma-reducing video intervention was more effective for the anxious-preoccupied group, whereas the utility-enhancing video intervention was more effective for the dismissive-avoidant group than other groups. These results suggest the importance of implementing strategies tailored to each attachment group.

L2 fluency as influenced by content familiarity and planning: Performance, measurement, and pedagogy

2016 ◽  
Vol 22 (1) ◽  
pp. 94-114 ◽  
Author(s):  
Gavin Bui ◽  
Zeping Huang
Keyword(s):  
Second Language ◽  
Performance Measurement ◽  
Factorial Design ◽  
Task Planning ◽  
Planning Time ◽  
Positive Effects ◽  
Content Familiarity ◽  
Split Plot ◽  
Two Factors

This study investigates how second language (L2) fluency is influenced by two factors: Pre-task planning and content familiarity. Planning was adopted as a between-participant variable, combined with content familiarity as a within-participant variable, in a 2 × 2 split-plot factorial design. Nineteen measures of fluency phenomena, constituting eight categories, were used. Both planning and content familiarity were found to enhance fluency, but the positive effects of planning were stronger and noticeable on a wider range of measures. The availability of planning time also helped to compensate for lack of content familiarity. Implications for pedagogy and L2 fluency measurement are discussed.

scholarly journals Lab-Scale Fiber Spinning Experimental Design Cost Comparison

2010 ◽  
Vol 5 (1) ◽  
pp. 155892501000500 ◽  
Author(s):  
Jeffrey C. Moreland ◽  
Julia L. Sharp ◽  
Philip J. Brown
Keyword(s):  
Factorial Design ◽  
Response Surface ◽  
Second Order ◽  
Fiber Spinning ◽  
Raw Material ◽  
Time Cost ◽  
Response Surface Design ◽  
Surface Design ◽  
Split Plot ◽  
Plot Design

Many statistical experimental designs are too costly or require too much raw material to be feasible for lab-scale fiber spinning experiments. In this study a four-factor response surface design is presented to study the fiber spinning process in detail at the lab scale. The time, cost, and amount of raw material required to execute the proposed design are compared to the typical completely randomized 24 factorial design used in fiber spinning experiments and also to a standard four-factor response surface design. Sample fiber data as well as analysis from a typical statistical software package is provided to further demonstrate the differences between each design. By designating some treatment factors in the design as hard-to-change, split-plotting is used to reduce the time, cost, and amount of raw material required to complete the experiment. The proposed split-plot design is faster and less expensive than a typical factorial design and has the advantage of fitting a more complex second-order model to the system. When compared to a standard response surface design, the proposed split-plot design provides the same second-order modeling capabilities but reduces the cost of the experiment by 53%, the total time by 36%, and the amount of polymer required by 24%. Thus, a split-plot response surface design based on hard-to-change factors is recommended in lab-scale spinning.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

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