Examius
🇮🇳 UGC NET Psycology · flashcards
UGC NET Psycology Research Methodology and Statistics Flashcards
63 question-and-answer cards covering Research Methodology and Statistics as it is examined in UGC NET Psycology. 24 of them are printed below, taken from across the deck — no signup, no paywall on the preview.
24 sample cards from the Research Methodology and Statistics deck
Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.
State the empirical (68-95-99.7) rule for the normal curve.
About 68% of scores fall within ±1 SD of the mean, ~95% within ±2 SD, and ~99.7% within ±3 SD.
Distinguish positive skew, negative skew, and the kurtosis terms leptokurtic and platykurtic.
Positive skew: tail to the right (mean > median); negative skew: tail to the left (mean < median). Leptokurtic: peaked, heavy tails; platykurtic: flat, light tails (vs mesokurtic = normal).
What assumptions distinguish parametric tests from non-parametric tests?
Parametric tests assume normality, homogeneity of variance, and interval/ratio data; non-parametric (distribution-free) tests make no such distributional assumptions and suit ordinal/nominal data or small/skewed samples.
Give the non-parametric equivalents of the independent t-test, paired t-test, and one-way ANOVA.
Independent t-test → Mann-Whitney U; paired t-test → Wilcoxon signed-rank; one-way ANOVA → Kruskal-Wallis H.
When is a chi-square test used?
With categorical (nominal/frequency) data to test goodness-of-fit (one variable) or independence/association between two categorical variables.
Define statistical power and state its conventional desired level.
Power is the probability of correctly rejecting a false null hypothesis (1 − β), i.e., detecting a true effect; the conventional minimum desired level is 0.80.
List the four factors that influence statistical power.
Sample size (n), effect size, significance level (alpha), and the variability/error in the data (and whether the test is one- or two-tailed).
Define Type I and Type II errors.
Type I error (alpha): rejecting a true null hypothesis (false positive); Type II error (beta): failing to reject a false null hypothesis (false negative).
What is effect size and name one common measure for mean differences and one for correlation.
Effect size quantifies the magnitude/strength of a relationship or difference independent of sample size. For mean differences: Cohen's d; for association: r (or eta-squared/r² for variance explained).
State Cohen's conventions for small, medium, and large values of d.
d ≈ 0.2 (small), 0.5 (medium), 0.8 (large).
What does the Pearson product-moment correlation coefficient (r) measure, and what is its range?
The strength and direction of a linear relationship between two continuous variables; it ranges from −1.00 (perfect negative) through 0 (no linear relation) to +1.00 (perfect positive).
Match these special correlation methods to their data: Spearman's rho, point-biserial, phi, biserial.
Spearman's rho: two ranked/ordinal variables; point-biserial: one continuous and one true dichotomy; phi (φ): two true dichotomies; biserial: one continuous and one artificial dichotomy.
What is the coefficient of determination (r²) and what does it represent?
r² is the square of the correlation coefficient; it represents the proportion of variance in one variable that is accounted for/explained by the other.
What is the purpose of regression analysis, and what does the regression coefficient (b) indicate?
Regression predicts a dependent variable from one or more predictors. The slope coefficient b indicates the expected change in Y for a one-unit change in X.
In the regression equation Y = a + bX, what do 'a' and 'b' represent?
'a' is the intercept (predicted value of Y when X = 0); 'b' is the slope/regression coefficient (rate of change in Y per unit X).
What is multicollinearity in multiple regression and why is it problematic?
High intercorrelation among predictor variables; it inflates standard errors, makes coefficient estimates unstable, and obscures the unique contribution of individual predictors.
What is the goal of factor analysis?
A data-reduction technique that identifies underlying latent dimensions (factors) explaining the pattern of intercorrelations among a large set of observed variables.
Differentiate exploratory factor analysis (EFA) from confirmatory factor analysis (CFA).
EFA explores data to discover the number and nature of factors without a prior hypothesis; CFA tests whether data fit a hypothesized, pre-specified factor structure.
What is an eigenvalue in factor analysis, and what is the Kaiser criterion for retaining factors?
An eigenvalue is the amount of total variance explained by a factor; the Kaiser criterion retains factors with eigenvalues greater than 1.0.
Distinguish orthogonal (e.g., Varimax) from oblique (e.g., Promax) rotation in factor analysis.
Orthogonal rotation assumes factors are uncorrelated (90°); oblique rotation allows factors to correlate, which is more realistic when underlying constructs are related.
What is a factorial experimental design and what unique information does it provide?
A design with two or more independent variables each at multiple levels, studied simultaneously; it reveals both main effects of each IV and interaction effects between them.
Differentiate between-subjects and within-subjects (repeated measures) designs.
Between-subjects: different participants in each condition (no carryover, needs more participants); within-subjects: same participants in all conditions (controls individual differences, but risks order/carryover effects).
What is counterbalancing and which design problem does it control?
Systematically varying the order of conditions across participants to control order effects (practice and fatigue) in within-subjects/repeated-measures designs.
In a pretest-posttest control group design, why is the control group essential?
It provides a baseline to rule out threats to internal validity (maturation, history, testing) so that observed change can be attributed to the treatment rather than extraneous factors.
What this deck covers
The Research Methodology and Statistics deck follows the UGC NET Psycology Research Methodology and Statistics syllabus — 3 chapters and 27 topics — so questions land on material that is genuinely examinable rather than trivia around it. That works out to roughly 21.0 cards per chapter.
Answers are written to be recallable, not just readable — averaging about 159 characters, which is long enough to carry the reasoning and short enough to say out loud.
A deck like this earns its keep on the second and third pass. Read the syllabus first so you know the shape of the subject, then use the cards to find the specific facts that have not stuck.
Research Methodology and Statistics flashcards FAQ
How many Research Methodology and Statistics flashcards are in this UGC NET Psycology deck?
63 cards. This page previews 24 of them, sampled evenly across the deck so you can judge the difficulty before installing anything.
Are these UGC NET Psycology flashcards free?
Yes. The preview here is free to read with no signup, and the full 63-card deck is free inside the Examius app.
What do the Research Methodology and Statistics cards cover?
They follow the UGC NET Psycology Research Methodology and Statistics syllabus — 3 chapters and 27 topics — so the questions track what is actually examinable.
How should I use these flashcards?
Read the syllabus first so you know the shape of the subject, then drill the deck. Examius schedules each card with spaced repetition, so cards you keep missing come back sooner and ones you know drift further apart.