UGC NET Questions (Paper – 1)

Randomisation is the process of assigning participants to experimental and control groups using random mechanisms, such as random number tables or computer-generated sequences. This practice helps ensure that groups are equivalent on both known and unknown variables at the start of the experiment. By reducing systematic differences between groups, randomisation strengthens internal validity and makes it more likely that observed effects are due to the treatment. Hence, the procedure described in the stem is correctly called randomisation.

Option A:
Stratification involves dividing a population into homogeneous subgroups and can be used before sampling or randomisation, but it does not by itself eliminate selection bias in group assignment. It is more related to sampling than to within-experiment group allocation, so it is not the best answer.

Option B:
Matching pairs participants on certain characteristics such as age or ability before assigning them to groups, which can improve equivalence on those specific variables, but it is not purely random. It cannot control for all unmeasured variables, so it differs from the chance-based process emphasised in the stem.

Option C:
Option C, randomisation, uses chance to distribute both measured and unmeasured characteristics across groups, reducing the risk that pre-existing differences, rather than the treatment, explain outcomes. This aligns exactly with the purpose stated in the question.

Option D:
Standardisation refers to keeping procedures, instructions and conditions uniform across participants to reduce procedural variation. While important for internal validity, it does not address selection bias in the same way randomisation does.

Causal comparative research attempts to investigate possible cause–effect relationships by examining differences between natural groups. The researcher does not manipulate the independent variable but observes the effects of variables that have already occurred or that cannot ethically be controlled, such as gender or past experiences. Internal validity is weaker than in true experiments because rival explanations may remain.

Option A:
Option A describes essential features of true experiments, including manipulation and random assignment, which are absent in causal comparative studies.

Option B:
Option B correctly states that ex post facto studies use existing groups and nonmanipulated variables. The researcher works backward from observed differences to infer possible causes.

Option C:
Option C claims complete control over extraneous variables, which is rarely achievable even in experiments and certainly not in causal comparative designs.

Option D:
Option D restricts research to laboratory conditions, which is not a defining feature of either design; many studies of both types occur in natural settings.

Internal validity refers to the extent to which a study’s design and execution permit a clear causal interpretation of the relationship between independent and dependent variables. High internal validity means that alternative explanations, such as the influence of extraneous variables or selection biases, have been effectively controlled or ruled out. In experimental research, techniques like random assignment, control groups and standardised procedures are used to enhance internal validity. Since the stem focuses on confidently attributing differences in the dependent variable to the treatment, it is explicitly referring to internal validity.

Option A:
External validity is concerned with the generalisability of findings to other populations, settings and times, not with the internal logic of causal inference within the study itself. A study could have strong internal validity but limited external validity if the sample or conditions are highly specialized. Because the stem emphasises attribution of observed differences to the treatment within the experiment, external validity is not the correct answer.

Option B:
Reliability addresses the consistency or stability of scores obtained with a measurement tool, rather than whether those scores are due to the intended experimental manipulation. A measure can be reliable but still be influenced by confounding factors that threaten causal inference. Therefore, reliability does not capture the idea described in the question.

Option C:
Objectivity relates to the freedom of measurements and interpretations from subjective bias on the part of the researcher or assessor. While objectivity can support internal validity, it does not by itself guarantee that observed differences are due to manipulation of the independent variable. As such, objectivity is not the term that best fits the stem.

Option D:
Internal validity is strengthened when potential threats, such as maturation, history, instrumentation changes and selection effects, are addressed in the design. By minimizing these threats, the researcher can argue more convincingly that changes in the dependent variable are caused by the treatment. This reasoning matches the description in the stem, making internal validity the appropriate completion.

Random assignment uses chance procedures to place participants into different experimental conditions so that groups are equivalent on both known and unknown characteristics at the outset. It strengthens internal validity by making it more likely that observed differences in outcomes are due to the treatment rather than pre-existing differences. This process is distinct from random sampling, which concerns how participants are chosen from the population. Therefore, the term that correctly completes the stem is random assignment.

Option A:
Sampling refers to selecting individuals from the population for inclusion in the study, which occurs before participants are divided into groups. It addresses external validity rather than internal group equivalence.

Option B:
Allocation is a general word for distributing participants among conditions and may or may not be done randomly; the stem emphasises assignment “by chance,” which points specifically to random assignment.

Option C:
Random assignment ensures that every participant has an equal probability of being in any group, reducing systematic bias and supporting causal inference. Because the stem describes this process in experimental research, assignment is the appropriate completion and this option is correct.

Option D:
Randomisation is a broader term for using chance procedures in research (and may include random assignment), but the specific term for placing participants into experimental/control groups is random assignment, which is what the stem targets.

Control variables are factors that the researcher deliberately holds constant or accounts for so that they do not confound the relationship between the independent and dependent variables. By stabilising these variables, the researcher reduces alternative explanations for observed effects. This enhances the internal validity of the experiment. Because the stem refers to variables kept constant to prevent them from influencing outcomes, it is describing control variables.

Option A:
Independent variables are intentionally manipulated to assess their impact on outcomes, not held steady. Their variation is central to testing hypotheses about cause and effect. Thus, independent variables are the opposite of what the stem indicates and cannot be the correct answer.

Option B:
Dependent variables are the outcomes measured to see whether they change as a result of the independent variable. They are expected to vary and are not kept constant. Since the question is about variables that are purposely held constant, dependent is not appropriate.

Option C:
A moderator variable influences the strength or direction of the relationship between independent and dependent variables, helping to specify “when” or “for whom” an effect is observed. Although researchers may sometimes create conditions based on moderators, the classic notion of control variables is more directly about holding factors constant. Therefore, moderator does not fit the stem’s description.

Option D:
Control variables are chosen and managed to remove their potential influence, such as keeping room temperature constant in a cognitive experiment. This deliberate stabilisation ensures that observed differences in the dependent variable are more likely due to the independent variable. These functions match the description in the stem, so control is the correct completion.

Random assignment is the process of allocating participants to experimental and control groups purely by chance, such as by using random numbers or drawing lots. It ensures that, on average, the groups are equivalent with respect to both known and unknown variables before the treatment is applied. This greatly strengthens internal validity by reducing selection bias. Because the stem highlights equal and independent chances of being placed in any group, random assignment is the correct term.

Option A:
Random assignment ensures that individual differences are distributed approximately evenly across groups, so observed post-treatment differences are more likely due to the intervention. It is a defining feature of true experimental designs. This role matches the stem’s description, confirming random assignment as the appropriate completion.

Option B:
Matching involves pairing participants who are similar on certain variables and then assigning them to different groups, which partially controls for those variables but does not guarantee equal independent chance of group placement for all participants. Thus, matching is not the correct answer.

Option C:
Blocking is used to control for the effects of one or more nuisance variables by forming homogeneous blocks and then randomly assigning treatments within each block, especially in agricultural or industrial experiments. While it involves randomisation, the stem’s focus on general equal and independent chances for all participants is more directly captured by random assignment.

Option D:
Clustering usually refers to grouping participants based on natural units such as classes or schools in sampling or design and does not necessarily imply random allocation of individuals to treatment conditions. As such, clustering is not the proper term for the procedure described.

Internal validity refers to the degree to which a study is free from confounding influences and alternative explanations, so that changes in the dependent variable can be ascribed to the independent variable. High internal validity is achieved through control of extraneous variables, random assignment and appropriate design. It is crucial in experimental research where causal claims are being made. Because the stem describes attributing observed changes to the manipulation of the independent variable, internal validity is the correct term.

Option A:
External validity concerns the generalisability of findings to other populations, settings or times, rather than the correctness of causal inference within the study. A study can be internally valid but externally limited, so external validity does not match the stem.

Option B:
Ecological validity focuses on how well research findings reflect real-world behaviours and contexts, often associated with naturalistic settings. While related to generalisability, it is not about directly attributing effects to independent variables in a controlled design. Thus, ecological validity is not the appropriate completion here.

Option C:
Internal validity is enhanced by using control groups, randomisation and standardised procedures to isolate the effect of the independent variable. When these strategies are successful, researchers can more confidently claim that the treatment caused the observed outcomes. This is precisely what the stem is describing.

Option D:
Face validity simply indicates whether a test or study appears, on the surface, to measure what it claims to measure, as judged by laypersons. It does not address deeper issues of causal attribution and control over confounds, so it is not the correct answer.

A counterbalanced design systematically varies the order in which treatments are administered so that each treatment appears in each position equally often across participants. This procedure helps control for order and carryover effects that could confound the results of repeated-measures experiments. By distributing potential order effects evenly, researchers can attribute observed differences more confidently to the treatments themselves. Because the stem describes using every possible order of treatments equally often, counterbalanced design is the correct answer.

Option A:
Matched-groups designs involve pairing participants on certain characteristics and then assigning members of each pair to different groups, reducing variability between groups but not directly addressing order effects in repeated measures. Thus, matched groups is not the appropriate completion.

Option B:
Counterbalancing ensures that practice, fatigue or other sequence-related influences are not confounded with specific treatments. It is especially valuable when the same participants receive multiple conditions. The stem’s emphasis on controlling order effects by using all possible orders aligns exactly with this strategy, confirming counterbalanced as the right term.

Option C:
Factorial designs study the effects of two or more independent variables simultaneously, examining both main effects and interactions. They concern the structure of independent variables rather than the order of administration in repeated measures. Hence, factorial design is not suitable here.

Option D:
Single-subject designs focus on repeated observations of a small number of participants, often using AB or ABA patterns, and may or may not involve counterbalancing. The term single-subject alone does not capture the systematic variation in treatment order described in the stem. Therefore, single-subject is not the correct answer.