scholarly journals PENGARUH BANYAKNYA PESERTA TES, BUTIR, PILIHAN JAWABAN, SERTA INDEKS KESULITAN TERHADAP STATISTIK DAYA PEMBEDA DAN RELIABILITAS

At-Turats ◽  
2014 ◽  
Vol 8 (2) ◽  
Author(s):  
Ali Hasmy
Keyword(s):  
Factorial Design ◽  
Simulated Data ◽  
Test Reliability ◽  
Test Length ◽  
Analysis Program ◽  
Item Discrimination ◽  
Test Analysis ◽  
Index Of Difficulty ◽  
The Mean ◽  
Full Factorial

This research was conducted to see the effect of the number of testee (sample size), the number of items (test length), the number of options, and index of difficulty to various item discrimination statistics and test reliability. The data used are simulated data and analyzed using the Test Analysis Program (TAP) version 6.65 with a full factorial design. In general, the results show that the number of testee, the number of items, and index of difficulty (except number of options) significantly affect the various item discrimination statistics and test reliability. The statistics are robust to these three factors is only the Mean of Item Discrimination and Spearman Brown’s 1-2 Split-Half, while the most sensitive is Split-Half Odd-Even statistic.

Development and Optimization of Inhalable Levofloxacin Nanoparticles for The Treatment of Tuberculosis.

2020 ◽  
Vol 17 ◽  
Author(s):  
Sunny Shah ◽  
Rohit Ghetiya ◽  
Moinuddin Soniwala ◽  
Jayant Chavda
Keyword(s):  
Particle Size ◽  
Drug Release ◽  
Factorial Design ◽  
Full Factorial Design ◽  
Pareto Chart ◽  
Mean Particle Size ◽  
Screening Design ◽  
The Mean ◽  
Full Factorial ◽  
32 Full Factorial Design

Background:: Levofloxacin has been recommended by WHO for the treatment of pulmonary tuberculosis and inhalable delivery of levofloxacin can be advantageous over conventional delivery. Objective:: This study aimed to develop and optimize inhalable levofloxacin loaded chitosan nanoparticles (LCN). The objective was to achieve the mean particle size of LCN less than 300nm, sustain the drug release up to 24 h and achieve MMAD of LCN of less than 5μm. Methods:: LCN were prepared by ionic gelation of chitosan with sodium tripolyphosphate (STPP) and subsequent lyophilization. A Plackett Burman screening design, 32 full factorial design, and overlay plot were sequentially employed to optimize the formulation. The mean particle size, % entrapment efficiency, in vitro drug release, and Minimum inhibitory concentration were evaluated. Results:: The Pareto chart from Placket Burman screening design revealed that the concentration of chitosan and concentration of STPP were found to be significant (p < 0.05). Further analysis by 32 full factorial design revealed that F-ratio for each model generated was found to be greater than the theoretical value (p < 0.05), confirming the significance of each model. Conclusion:: The optimized formulation showed a mean particle size of 171.5 nm, sustained the drug release up to 24 h in simulated lung fluid, and revealed MMAD of 3.18 μm, which can confirm delivery of the drug to deep lung region. However, further in vivo studies are required to design suitable dosage regimen and establish the fate of nanoparticles for safe and efficacious delivery of the drug.

Application of 32 Full Factorial Design and Desirability Function for Optimizing The Manufacturing Process for Directly Compressible Multi-Functional Co-Processed Excipient

2020 ◽  
Vol 17 (6) ◽  
pp. 523-539
Author(s):  
Jalpa Patel ◽  
Dhaval Mori
Keyword(s):  
Factorial Design ◽  
Spray Drying ◽  
Desirability Function ◽  
Full Factorial Design ◽  
Angle Of Repose ◽  
P Value ◽  
Independent Variables ◽  
Full Factorial ◽  
32 Full Factorial Design ◽  
Response Variables

Background: Developing a new excipient and obtaining its market approval is an expensive, time-consuming and complex process. Compared to that, the co-processing of already approved excipients has emerged as a more attractive option for bringing better characteristic excipients to the market. The application of the Design of Experiments (DoE) approach for developing co-processed excipient can make the entire process cost-effective and rapid. Objective: The aim of the present investigation was to demonstrate the applicability of the DoE approach, especially 32 full factorial design, to develop a multi-functional co-processed excipient for the direct compression of model drug - cefixime trihydrate using spray drying technique. Methods: The preliminary studies proved the significant effect of atomization pressure (X1) and polymer ratio (microcrystalline cellulose: mannitol - X2) on critical product characteristics, so they were selected as independent variables. The angle of repose, Carr’s index, Hausner’s ratio, tensile strength and Kuno’s constant were selected as response variables. Result: The statistical analysis proved a significant effect of both independent variables on all response variables with a significant p-value < 0.05. The desirability function available in Design Expert 11® software was used to prepare and select the optimized batch. The prepared co-processed excipient had better compressibility than individual excipients and their physical mixture and was able to accommodate more than 40 percent drug without compromising the flow property and compressibility. Conclusion: The present investigation successfully proved the applicability of 32 full factorial design as an effective tool for optimizing the spray drying process to prepare a multi-functional co-processed excipient.

Formulation of Extended-Release Beads of Lamotrigine Based on Alginate and Cassia fistula Seed Gum by QbD Approach

2020 ◽  
Vol 17 (5) ◽  
pp. 422-437
Author(s):  
Dixita Jain ◽  
Akshay Sodani ◽  
Swapnanil Ray ◽  
Pranab Ghosh ◽  
Gouranga Nandi
Keyword(s):  
Drug Release ◽  
Factorial Design ◽  
Extended Release ◽  
Polymer Concentration ◽  
Green Manufacturing ◽  
Full Factorial Design ◽  
Cassia Fistula ◽  
Negative Environmental Impact ◽  
Seed Gum ◽  
Full Factorial

Aim: This study was focused on the formulation of the multi-unit extended-release peroral delivery device of lamotrigine for better management of epilepsy. Background: The single-unit extended-release peroral preparations often suffer from all-or-none effect. A significant number of multi-unit delivery systems have been reported as a solution to this problem. But most of them are found to be composed of synthetic, semi-synthetic or their combination having physiological toxicity as well as negative environmental impact. Therefore, fabrication and formulation of multi-unit extended-release peroral preparations with natural, non-toxic, biodegradable polymers employing green manufacturing processes are being appreciated worldwide. Objective: Lamotrigine-loaded extended-release multi-unit beads have been fabricated with the incorporation of a natural polysaccharide Cassia fistula seed gum in calcium-cross-linked alginate matrix employing a simple green process and 23 full factorial design. Methods: The total polymer concentration, polymer ratio and [CaCl2] were considered as independent formulation variables with two different levels of each for the experiment-design. The extended-release beads were then prepared by the ionotropic gelation method using calcium chloride as the crosslinkerions provider. The beads were then evaluated for drug encapsulation efficiency and drug release. ANOVA of all the dependent variables such as DEE, cumulative % drug release at 2h, 5h, 12h, rate constant and dissolution similarity factor (f2) was done by 23 full factorial design using Design-Expert software along with numerical optimization of the independent variables in order to meet USP-reference release profile. Results: The optimized batch showed excellent outcomes with DEE of 84.7 ± 2.7 (%), CPR2h of 8.41± 2.96 (%), CPR5h of 36.8± 4.7 (%), CPR12h of 87.3 ± 3.64 (%) and f2 of 65.9. Conclusion: This approach of the development of multi-unit oral devices utilizing natural polysaccharides might be inspiring towards the world-wide effort for green manufacturing of sustained-release drug products by the QbD route.

scholarly journals Shear strength optimization for FSSW AA6060-T5 joints by Taguchi and full factorial design

2020 ◽  
Vol 9 (6) ◽  
pp. 16072-16079
Author(s):  
C.A.G. Aita ◽  
I.C. Goss ◽  
T.S. Rosendo ◽  
M.D. Tier ◽  
A. Wiedenhöft ◽  
...  
Keyword(s):  
Shear Strength ◽  
Factorial Design ◽  
Full Factorial Design ◽  
Full Factorial

scholarly journals Effects of Language Learning Strategies and Learners’ Motivation on Students’ Learning Achievement

2015 ◽  
Vol 3 (2) ◽  
pp. 132 ◽  
Author(s):  
Hao-Yuan Cheng ◽  
Nai-Ying Chang
Keyword(s):  
Language Learning ◽  
Learning Strategies ◽  
Language Learning Strategies ◽  
Course Performance ◽  
Test Analysis ◽  
New Information ◽  
Significant Difference ◽  
Group A ◽  
The Mean ◽  
The Relationship

<p>This study investigated whether learning strategies had made any impact on learners’ achievement and explored whether learning motivation was correlated with learning strategies. The participants of this study were the students from the EFL (English as a Foreign Language) intermediate level course at a college in Taiwan. The students were given a pretest and a posttest. The mean scores of these tests were compared with a SILL survey (Strategies Inventory for Language Learning) at the end of the investigation. The participants’ course performance was compared with their use of learning strategies. The assumption of the relationship between learning strategies and motivation is that motivated learners have a greater desire to seek out solutions or support from others and employ more strategies to process the new information. The results of this investigation revealed that only the memory strategies had a significant difference in the posttest of Group A on the independent sample t-test analysis.</p>

scholarly journals Electroflotation of emulsified oil in industrial wastes evaluated with a full factorial design

2008 ◽  
Vol 25 (3) ◽  
pp. 435-442 ◽  
Author(s):  
F. N. B. Nahui ◽  
M. R. Nascimento ◽  
E. B. Cavalcanti ◽  
E. O. Vilar
Keyword(s):  
Factorial Design ◽  
Full Factorial Design ◽  
Industrial Wastes ◽  
Emulsified Oil ◽  
Full Factorial
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