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ICAR AICE-JRF/SRF Factorial Experiments, Confounding, Split- & Strip-Plot Designs: practice questions

ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
10worked here, free
88written to this exam in this subject
11syllabus units

What this subject covers

Studying several factors at once: 2^n, 3^n and asymmetrical factorials, main effects and interactions by Yates' method, total and partial confounding, fractional factorials, and the split-plot, split-split-plot and strip-plot layouts that field operations such as irrigation and tillage force on agronomists.

In the exams: AICE-JRF Ag Stat Unit 4 (heavy: 2^n, 3^n, asymmetrical, confounding, split, strip and crossover named); AICE-JRF Agronomy Unit 6 (heavy); ASRB NET Ag Stat (heavy)

What ICAR AICE-JRF/SRF asks from it

ICAR AICE-JRF/SRF Agricultural Statistics (Unit 4)

2^n and 3^n factorials, asymmetrical factorials, total and partial confounding, split- and strip-plot designs, crossover designs: effects, sums of squares, confounding schemes, degrees of freedom and error terms.

ICAR AICE-JRF/SRF Agronomy (Unit 6) and other crop subjects

Split-plot, strip-plot, factorial and simple confounding designs as used in agronomic research; which design for which practical constraint; reading the ANOVA.

Where the 88 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Factorial Experiments, Confounding, Split- & Strip-Plot Designs. Difficulty: 21 easy, 50 medium, 17 hard.

Factorial experiments: main effects and interactions11
2^n factorials and Yates' algorithm10
Total and partial confounding10
Split-plot designs and their error terms10
Fractional factorials and aliasing9
Crossover (changeover) designs8
3^n factorials7
Asymmetrical factorials6
Strip-plot (split-block) designs6
Factorials with extra (control) treatments6
Split-split-plot designs5

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Factorial experiments: main effects and interactions · medium

In a 3 x 4 factorial (A at 3 levels, B at 4) in an RCBD with 3 replications and error mean square MSE, what is the standard error of the difference between two A means?

  1. The square root of (2 MSE / 9)
  2. The square root of (2 MSE / 4)
  3. The square root of (2 MSE / 12)
  4. The square root of (2 MSE / 3)
Answer and working

C. The square root of (2 MSE / 12)

Each A mean is averaged over r x b = 3 x 4 = 12 plots, so SEd = sqrt(2 MSE / 12). The square root of 2 MSE / 3 is for two treatment-combination means; 2 MSE / 9 would be for B means (r x a = 9).

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

2 · Total and partial confounding · hard

Confounding AB and ABC in a 2^3 in four blocks of two would be a bad choice because:

  1. AB and ABC cannot both be confounded in a 2^3 for any block size
  2. Four blocks of two leave no degrees of freedom for any main effect
  3. Their generalised interaction ABC^2 is confounded with replicates
  4. Their generalised interaction C, a main effect, is also confounded
Answer and working

D. Their generalised interaction C, a main effect, is also confounded

(AB)(ABC) = A^2B^2C = C, so the main effect of C would be lost. Confounded effects should be chosen so that their generalised interactions are high-order.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

3 · Fractional factorials and aliasing · medium

What is the resolution of a 2^(4-1) design with defining relation I = ABCD?

  1. V
  2. III
  3. IV
  4. II
Answer and working

C. IV

Resolution is the length of the shortest word in the defining relation; ABCD has four letters, so resolution IV: main effects are clear of two-factor interactions, but two-factor interactions are aliased with each other.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

4 · Fractional factorials and aliasing · medium

A significant contrast in a 2^(4-1) (I = ABCD) estimates AC + BD. How can the two effects be separated?

  1. Assume both effects are equal and halve the estimate
  2. Test the contrast against a larger error mean square
  3. Repeat the same half fraction with more replicates
  4. Run the complementary or fold-over fraction
Answer and working

D. Run the complementary or fold-over fraction

Replicating the same fraction keeps AC and BD aliased; running the other half (or a fold-over) breaks the alias. Subject knowledge may also suggest which is plausible.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

5 · Strip-plot (split-block) designs · medium

How are the two factors randomised in a strip-plot design?

  1. Each factor is randomised to its strips independently in every block
  2. Both factors are randomised together over all intersection plots
  3. Each factor occurs once in every row and column of the block
  4. The second factor is randomised within each plot of the first factor
Answer and working

A. Each factor is randomised to its strips independently in every block

Horizontal strips get factor A at random and vertical strips get factor B at random, separately in each block; randomising B within A plots would be a split plot.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

6 · Factorials with extra (control) treatments · medium

A 3 x 3 factorial plus one control is in an RCBD. How are the 9 treatment degrees of freedom split?

  1. Control vs rest 2, A 2, B 2, A x B 3
  2. A 3, B 3, A x B 3, with the control in A
  3. Control vs rest 1, A 3, B 3, A x B 2
  4. Control vs rest 1, A 2, B 2, A x B 4
Answer and working

D. Control vs rest 1, A 2, B 2, A x B 4

Ten treatments give 9 df: one for control versus the nine factorial combinations and 8 for the 3 x 3 factorial (2 + 2 + 4).

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

7 · Factorials with extra (control) treatments · hard

A 2 x 2 factorial plus one control in an RCBD with 4 replications: how many error degrees of freedom are there?

  1. 15
  2. 16
  3. 12
  4. 9
Answer and working

C. 12

Five treatments and 4 blocks: error df = (4 - 1)(5 - 1) = 12. 9 would be the 2 x 2 alone.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

8 · Crossover (changeover) designs · easy

In a crossover design, each experimental unit:

  1. Receives all treatments at once on different parts of its body
  2. Receives a sequence of different treatments in successive periods
  3. Receives one treatment for the whole trial, chosen at random
  4. Receives the same treatment repeatedly in every period of the trial
Answer and working

B. Receives a sequence of different treatments in successive periods

Treatments are compared within units over periods, which removes between-animal variation from comparisons.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

9 · Crossover (changeover) designs · medium

Why are crossover designs efficient in animal experiments?

  1. They avoid the need for any randomisation of treatment order
  2. Between-animal variation is removed from treatment comparisons
  3. Each animal needs to be measured only once during the trial
  4. They allow carry-over effects to be ignored in the analysis
Answer and working

B. Between-animal variation is removed from treatment comparisons

Each animal serves as its own control, so large differences between animals do not enter the error; this is why fewer animals are needed.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

10 · Factorial experiments: main effects and interactions · easy

A factorial set of treatments can be laid out in which designs?

  1. Only a Latin square design
  2. Any basic design: CRD, RCBD or Latin square
  3. Only a split-plot design
  4. Only a completely randomised design
Answer and working

B. Any basic design: CRD, RCBD or Latin square

A factorial is a treatment structure; the combinations are arranged in whatever design suits the experimental material.

Further reading: Cochran and Cox, Experimental Designs; Gomez and Gomez, Statistical Procedures for Agricultural Research

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Read the subject

Factorial Experiments, Confounding, Split- & Strip-Plot DesignsThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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