ICAR AICE-JRF/SRF (PhD admission and fellowship) · India · National Testing Agency for ICAR
Original practice questions written to the published syllabus · not past papersHow to design, run and report a study: problems, hypotheses, measurement, questionnaires, sampling, analysis and writing up. Examined in agribusiness, agricultural economics and extension PhD entrance papers alike.
In the exams: Research methodology units of agribusiness management PhD entrance and fellowship examinations, agricultural economics PhD entrance and fellowship examinations and agricultural extension PhD entrance and fellowship examinations (heavy in all three)
ICAR AICE-JRF/SRF (PhD) Agri-Business Management, Unit 7 (Research Methods)
Research designs and process, scales and attitude measurement (Likert, Thurstone), reliability and validity, questionnaire design, sampling methods and sample-size formulas, and the multivariate tools: multiple and logistic regression, PCA, factor, discriminant and cluster analysis, and tree-based methods. Expect both definitions and one-step calculations.
ICAR AICE-JRF/SRF (PhD) Agricultural Economics, Unit 9 (Research Methodology and Econometrics)
Hypothesis testing logic, Type I and II errors, t, F and chi-square tests, OLS assumptions and the consequences of multicollinearity, heteroscedasticity and autocorrelation, dummy variables, logit and probit, and sampling and non-sampling errors in farm surveys.
ICAR AICE-JRF/SRF (PhD) Agricultural Extension, Unit 5 (Research Methodology)
Types of research including ex post facto, variables and hypotheses, levels of measurement, Thurstone and Likert scale construction, reliability (test-retest, split-half, alpha) and validity, sampling, and non-parametric tests used on ordinal extension data.
How our questions for ICAR AICE-JRF/SRF spread across the units of Research Methodology. Difficulty: 24 easy, 75 medium, 35 hard.
| Univariate analysis | 17 | |
| Multiple regression and logistic regression | 15 | |
| Measurement scales: nominal to ratio, Likert | 13 | |
| Sampling design | 9 | |
| Sample size determination | 9 | |
| Principal component and factor analysis | 9 | |
| Discriminant and cluster analysis | 9 | |
| Decision trees and random forests | 9 | |
| Report writing | 8 | |
| Hypotheses | 7 | |
| Questionnaire design | 7 | |
| Types of research and the research process | 6 | |
| Sampling and non-sampling errors | 6 | |
| Formulating the research problem | 5 | |
| Data editing, coding and tabulation | 5 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
In the hypothesis 'extension contact increases adoption of integrated pest management', adoption of IPM is the
B. dependent variable
Adoption is the outcome being explained, so it is dependent; extension contact is the independent variable.
Further reading: Kothari and Garg, Research Methodology: Methods and Techniques
Temperature in degrees Celsius is an example of
B. an interval scale
Equal units but an arbitrary zero: 20 degrees C is not twice as hot as 10 degrees C.
Further reading: Kothari and Garg, Research Methodology: Methods and Techniques
Farmers systematically under-report their income to enumerators. This is
A. response error that produces bias
Deliberate misreporting is a response error; because it is systematic it biases estimates and does not average out in larger samples.
Further reading: Cochran, Sampling Techniques
Six constraints are ranked by two methods, and the sum of squared rank differences is 14. Spearman's rank correlation is
C. 0.60
Working: rho = 1 - 6 x 14 / (6 x (36 - 1)) = 1 - 84/210 = 0.60. Stopping at 84/210 = 0.40 forgets to subtract from 1. Dropping the 6 gives 14/210 = 0.07, or 0.93 once subtracted from 1.
Further reading: Siegel and Castellan, Nonparametric Statistics for the Behavioral Sciences
Farmers' satisfaction ranks are compared across four independent districts. The appropriate test is
D. Kruskal-Wallis H, referred to chi-square with 3 df
Several independent groups with ordinal data call for Kruskal-Wallis; df = k - 1 = 3. Friedman is for related samples.
Further reading: Siegel and Castellan, Nonparametric Statistics for the Behavioral Sciences
Two independent groups (n1 = 10, mean 24, s1 = 4; n2 = 12, mean 20, s2 = 5) are compared with a pooled t test. Given the two-tailed 5 percent critical t with 20 df is 2.086, the result is
B. t = 2.04, not significant at 5 percent
Working: sp^2 = (9 x 16 + 11 x 25)/20 = 20.95; SE = sqrt(20.95 x (1/10 + 1/12)) = 1.96; t = 4/1.96 = 2.04 < 2.086, so H0 is not rejected. Comparing with 1.96 instead of the t critical value leads to the wrong verdict.
Further reading: Kothari and Garg, Research Methodology: Methods and Techniques
A Durbin-Watson statistic close to 2 indicates
B. no first-order autocorrelation
DW ranges from 0 to 4; about 2 means no first-order autocorrelation, near 0 positive and near 4 negative.
Further reading: Gujarati and Porter, Basic Econometrics
A farmer chooses among three unordered marketing channels: trader, cooperative, processor. The standard model is
C. multinomial logit
Unordered choices among more than two alternatives use multinomial logit; ordered models need a natural ranking.
Further reading: Gujarati and Porter, Basic Econometrics
The Kaiser criterion in principal component analysis retains components whose
D. eigenvalue is greater than 1
An eigenvalue above 1 means the component explains more variance than one standardised original variable.
Further reading: Hair et al., Multivariate Data Analysis
A researcher has a theory specifying which six items measure 'price risk perception' and which five measure 'production risk perception', and wants to test it. The appropriate method is
B. confirmatory factor analysis
Testing a pre-specified item-factor structure is confirmatory factor analysis, usually within structural equation modelling. EFA discovers structure without prior specification.
Further reading: Hair et al., Multivariate Data Analysis
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