UGC NET Questions (Paper – 1)

The independent variable is the factor that the researcher systematically varies or manipulates to observe its impact on the dependent variable. It is considered the presumed cause in a cause–effect relationship tested in an experiment. By changing levels of the independent variable while controlling other influences, the researcher can examine how outcomes respond. Therefore, the variable that is deliberately manipulated is rightly termed the independent variable.

Option A:
The independent variable is chosen based on theoretical expectations that it will influence the outcome of interest, such as teaching method or training programme. It is under direct control of the researcher within the experimental setting. This makes independent variable the correct completion of the stem.

Option B:
The dependent variable is the outcome or response that is measured to assess the effect of the independent variable. It is not directly manipulated but observed for changes as conditions vary. Hence, dependent variable does not match the description of the variable that is deliberately manipulated.

Option C:
A control variable is an extraneous factor that the researcher holds constant to prevent it from confounding the relationship between independent and dependent variables. Although it is regulated, it is not the variable whose different levels are being intentionally varied to test effects. Thus, control variable is not the correct answer.

Option D:
Extraneous variables are unwanted factors that may influence the dependent variable and threaten internal validity if not controlled. They are usually not intentionally manipulated but need to be minimised or accounted for. Therefore, extraneous variable cannot complete the stem accurately.

Statements A, B, C and D correctly distinguish internal and external validity and the roles of random assignment and random sampling. Internal validity is about attributing effects to the treatment rather than confounds, while external validity is about generalising beyond the study context. Random assignment strengthens internal validity by equalising extraneous variables across groups, and random sampling supports external validity by making the sample more representative. Statement E is false because a study can have strong internal validity yet limited generalisability, so high internal validity does not guarantee high external validity.

Option A:
Option A is incomplete because it omits D, which correctly highlights the relation between random sampling and external validity. Without D, the link between sampling and generalisability is not fully represented.

Option B:
Option B leaves out statement A, overlooking the fundamental definition of internal validity as ruling out alternative explanations. Even though B and D address external validity and sampling, omitting A weakens the conceptual pair.

Option C:
Option C is correct because it includes all four accurate statements about internal and external validity and their methodological supports. It deliberately excludes E, which wrongly assumes that internal and external validity always go together. Thus this combination precisely reflects textbook distinctions.

Option D:
Option D is incorrect because it includes E, which overstates the relationship between internal and external validity, and it omits B, which defines external validity itself. This mixture of omission and error makes the option unacceptable.

Option E is also wrong because it incorporates E, the false automatic link between internal and external validity. A correct option must not contain this overgeneralisation.

The control group is included in an experiment to provide a baseline against which the effects of the experimental treatment can be judged. It either receives no treatment or a standard condition, while the experimental group receives the new or varied treatment. Any differences in outcomes between the groups can then be attributed more confidently to the manipulation of the independent variable. Thus, the group described in the stem is correctly called the control group.

Option A:
Control groups help isolate the effect of the independent variable by holding all other conditions as constant as possible. They strengthen internal validity by allowing researchers to rule out alternative explanations such as maturation or history. This critical comparison role matches the definition given in the question.

Option B:
The experimental group is the set of participants that receives the treatment or intervention under investigation. It does not serve as the baseline but rather as the group whose outcomes are compared with those of the control group. Hence, experimental group is not the correct completion.

Option C:
Random group is not a standard term in experimental design; randomness refers instead to the method of assigning participants to groups. While random assignment is important, it does not name the specific group that receives no or standard treatment. Therefore, random group is inappropriate here.

Option D:
A matched group refers to participants paired or grouped based on similar characteristics to control for confounding variables, but such groups may still be either experimental or control groups. The term does not by itself identify the no-treatment comparison group described in the stem.

The pretest–posttest control group design involves randomly assigning participants to at least two groups, administering a pretest to both, applying the treatment only to the experimental group and then giving a post-test to both groups. This structure allows the researcher to compare changes over time while controlling for initial differences between groups. Random assignment enhances internal validity by balancing extraneous variables across groups. Thus, the design described in the stem is known as the pretest–posttest control group design.

Option A:
One-shot case study design exposes a single group to a treatment and then administers a post-test without any pretest or control group. Because it lacks both random assignment and a comparison group, it provides very weak evidence about treatment effects. This design is quite different from the one in the stem, which explicitly mentions both pretests and control groups.

Option B:
Static group comparison design includes an experimental group that receives treatment and a non-equivalent control group that does not, with only post-test observations and no random assignment. Without pretests and randomisation, this design cannot control for initial group differences. Therefore, static group comparison does not match the detailed description in the question.

Option C:
Pretest–posttest control group design combines random assignment, pretesting and post-testing for both experimental and control groups. This arrangement helps isolate the effect of the treatment by comparing changes in the two groups. Since the stem outlines all these components, this option correctly names the design.

Option D:
Single-subject design focuses on repeated measures of a single participant or a small number of participants over time, often using AB or ABA patterns. It does not usually involve random assignment to separate experimental and control groups with group-based pretests and post-tests. Hence, single-subject design is not the appropriate label here.

Interaction of selection and treatment occurs when the characteristics of those selected for the study, such as being volunteers or highly motivated individuals, interact with the treatment in such a way that results may not hold for a broader population. This threatens external validity because the findings may not generalise beyond the specific type of participants used. Recognising and minimising such biases is important when designing experiments. Therefore, the threat described in the stem is the interaction of selection and treatment.

Option A:
Selection refers to how participants enter the study and the ways in which they may differ systematically from the target population. When these differences influence how they respond to treatment, generalisation becomes problematic, capturing the idea in the stem and making this option correct.

Option B:
Testing as a threat to validity involves effects of taking a pretest on later performance, not differences between volunteers and non-volunteers. It is more about sensitisation than about participant characteristics.

Option C:
History refers to external events occurring between measurements that affect outcomes for all participants, which is different from the differential response of specific selection groups.

Option D:
Instrumentation concerns changes in measurement tools or observers over time, affecting scores independently of the treatment and is unrelated to how volunteers might differ from non-volunteers in responsiveness.

The dependent variable is the outcome or response that researchers observe and measure to determine whether it has changed as a result of the treatment or manipulation. Its values are assumed to depend on the levels of the independent variable. By comparing the dependent variable across experimental conditions, researchers infer whether the treatment has had an effect. Since the stem mentions the variable measured to assess the effect of the treatment, it is defining the dependent variable.

Option A:
A control variable is one that the researcher holds constant or monitors to prevent it from influencing the results. It is not the primary outcome being measured for treatment effects. While control variables are important for internal validity, they are different from the variable described in the stem.

Option B:
The independent variable is the one that the researcher manipulates or varies deliberately to study its impact on another variable. It represents the presumed cause rather than the observed effect. Because the stem refers to the variable that is measured as an outcome, independent variable is not the correct answer.

Option C:
Dependent variable represents the effect or response side of the cause–effect relationship studied in experiments. Examples include test scores, reaction times or attitude ratings after exposure to different treatments. The stem’s focus on measuring the variable to assess treatment effect corresponds exactly to this concept, making dependent variable the right completion.

Option D:
An intervening variable is a hypothetical factor that helps explain how or why an independent variable affects a dependent variable, such as motivation or anxiety. It is not usually the primary variable measured to determine treatment effectiveness. Therefore, intervening variable does not fit the stem.

The independent variable is the one that the researcher systematically changes or manipulates to study its impact on another variable. It is considered the presumed cause in a cause–effect relationship. By varying the independent variable across groups or conditions, the researcher can observe differences in outcomes and infer potential causal effects. Therefore, the manipulated variable described in the stem is the independent variable.

Option A:
The dependent variable is the outcome that is measured to see how it responds to changes in the independent variable. It is not directly manipulated but observed. Because the stem emphasises deliberate manipulation, dependent does not fit.

Option B:
An intervening variable lies in the causal chain between independent and dependent variables and explains the process linking them. It is not usually under direct experimental control in basic designs.

Option C:
Extraneous variables are any uncontrolled factors that may influence the dependent variable and threaten internal validity. Researchers seek to eliminate or control them, but they are not the focus of deliberate manipulation.

Option D:
The independent variable is selected and manipulated according to the research design, such as using different teaching methods or time allocations. Observed differences in the dependent variable across conditions are then attributed, under proper controls, to this independent variable. This matches the description in the stem, making this option correct.

The history threat to internal validity arises when events outside the experiment, such as policy changes or natural disasters, occur between measurements and influence participants’ responses. These events are not part of the planned treatment but can mimic or obscure treatment effects. If not controlled, they make it difficult to attribute changes solely to the intervention. Thus, the external event effect described in the stem is known as history.

Option A:
Maturation refers to natural changes within participants over time, such as ageing, fatigue or learning. These are internal to the individuals rather than external events affecting the group. Therefore, maturation is a different threat from the one described.

Option B:
Option B, history, focuses on time-related external events that coincide with the experiment and influence outcomes. For example, a media campaign or institutional change during the study period could create a history effect. Because the stem highlights external events between pre-test and post-test, this option is correct.

Option C:
Testing effects arise when taking a test influences performance on later administrations of the same test, such as through practice or sensitisation. This does not involve outside events, so it does not match the question.

Option D:
Instrumentation refers to changes in measurement instruments or observers over time, which can alter scores independently of the treatment. It is another threat to internal validity but not the one defined in the stem.

The independent variable is the factor that the researcher intentionally varies or manipulates in an experiment to determine its causal effect on the dependent variable. It represents the presumed cause in a cause–effect relationship. By changing the levels of the independent variable, researchers can observe corresponding changes in outcomes. Because the stem specifies the variable that is deliberately manipulated to examine its effect on another variable, it correctly refers to the independent variable.

Option A:
Control variables are held constant across conditions to prevent them from influencing the dependent variable, but they are not the primary variables being manipulated to test hypotheses. Their purpose is to strengthen internal validity rather than to represent the main cause under investigation. Thus, control is not the right term for the manipulated variable described in the question.

Option B:
Extraneous variables are any variables other than the independent variable that may affect the dependent variable if not controlled or randomised. They are potential sources of error or alternative explanations for observed effects. Since the stem describes a variable that is deliberately manipulated, extraneous does not fit.

Option C:
Independent variables are central to experimental design because manipulating them allows researchers to test causal hypotheses in a controlled way. For example, different teaching methods can serve as independent variables when studying their impact on achievement. This focus on deliberate manipulation matches the description in the stem, making independent the correct answer.

Option D:
A moderator variable is one that affects the strength or direction of the relationship between an independent and dependent variable, but it is not necessarily the variable being experimentally manipulated in the main design. Moderators help explain under what conditions a particular effect occurs. Therefore, moderator is not the term the stem is seeking.

The Latin square design is used when two extraneous variables, such as time and location, may systematically influence outcomes. Treatments are arranged in a square so that each treatment occurs exactly once in every row and once in every column, balancing their potential effects. This structure allows researchers to separate treatment effects from the two nuisance variables. Therefore, the design described in the stem is the Latin square design.

Option A:
Greek square is not a standard term in basic experimental design and does not refer to this specific arrangement controlling two extraneous variables.

Option B:
Latin square designs are efficient when full factorial designs would require too many conditions, yet the researcher still wishes to control for two blocking variables. The unique property of each treatment appearing once per row and column matches the stem precisely, making this option correct.

Option C:
Magic square is a mathematical concept involving numerical patterns that is unrelated to experimental control of extraneous variables.

Option D:
A balanced design usually refers more generally to equal group sizes or properly distributed treatments but does not specifically denote the row–column arrangement controlling two extraneous factors.

The dependent variable is the observed outcome whose values are expected to change in response to manipulation of the independent variable. It is “dependent” because its variation is presumed to be influenced by changes in the treatment conditions. Researchers measure this variable to test whether the intervention has made a difference. Hence, the variable measured to assess the treatment effect is correctly named the dependent variable.

Option A:
The independent variable is manipulated rather than measured as an outcome, so it does not align with the stem’s emphasis on measurement to assess effects. It functions as the presumed cause rather than the observed result.

Option B:
Option B, dependent variable, emphasises that this variable depends on the levels of the independent variable in the experimental design. Outcome indicators such as test scores, attitudes or performance measures are typical examples. This matches the description in the question, making this option correct.

Option C:
A moderator variable affects the strength or direction of the relation between independent and dependent variables but is not itself the main outcome being measured. It plays a more complex role and thus does not fit the stem.

Option D:
A control variable is held constant to prevent it from interfering with the interpretation of results. It is not the primary outcome of interest, so it is not the correct choice here.

The Solomon four-group design uses two groups that receive a pre-test and two that do not, with both pre-tested and non-pre-tested groups being split into treatment and control subgroups. This structure allows researchers to evaluate both the main effect of the treatment and any interaction between pre-testing and treatment. It is particularly useful when there is concern that pre-testing may sensitise participants. Hence, the design mentioned is the Solomon four-group design.

Option A:
Latin refers to the Latin square design, which controls for two extraneous variables arranged in rows and columns; it is a different form of experimental design.

Option B:
The Solomon design maintains random assignment and includes both pre-test and post-test observations for some groups while others receive only post-tests. By comparing patterns across the four groups, researchers can detect testing effects and adjust interpretations accordingly. This comprehensive control for testing effects fits the stem, making this option correct.

Option C:
Factorial designs study the combined effects of two or more independent variables by including all combinations of their levels, but they do not inherently incorporate the four-group structure designed for testing effects.

Option D:
Quasi experimental designs lack full random assignment or control groups and are therefore different from the rigorously controlled Solomon design described.

The instrumentation threat arises when measurement conditions are not equivalent across occasions, such as when instruments are recalibrated or observers become more lenient or strict over time. These changes can create apparent differences in scores that are unrelated to the treatment. As a result, it becomes difficult to determine whether observed effects are due to the intervention or to altered measurement. Therefore, the situation described in the stem is called instrumentation.

Option A:
Instrumentation problems can be reduced by standardising procedures, training observers, and checking calibration regularly. When such precautions are ignored, internal validity is compromised because score changes may reflect measurement drift rather than true change in participants. This focus on changes in instruments or scoring matches the stem, making this option correct.

Option B:
History refers to external events occurring between measurements that affect participants’ performance and is conceptually distinct from changes in measurement tools.

Option C:
Mortality, or attrition, occurs when participants drop out of the study, potentially biasing results if those who leave differ from those who remain. It does not involve changes in instruments or scoring.

Option D:
Selection threats arise when groups differ systematically at the start of an experiment due to non-random assignment. While serious, they are not related to shifts in measurement conditions over time.

The independent variable is the factor that the researcher deliberately manipulates or varies in an experiment in order to examine its impact on a dependent variable. It is considered the presumed “cause” in a cause–effect relationship. By changing its levels while controlling other variables, the researcher can test specific hypotheses about its influence. Therefore, the manipulated variable described in the stem is correctly called the independent variable.

Option A:
Option A, independent variable, captures the idea of a variable that is “independent” of changes in other variables within the experimental framework, because it is set by the researcher. Different groups or conditions receive different levels of this variable. Since the stem emphasises manipulation to observe effects, this option is appropriate.

Option B:
The dependent variable represents the outcome or response that is measured to see whether it changes as a result of the manipulation. It is not itself manipulated, so it does not fit the description.

Option C:
A control variable is kept constant across conditions to prevent it from confounding the relationship between independent and dependent variables. While important, it is not the variable being manipulated.

Option D:
Extraneous variables are uncontrolled factors that may influence the dependent variable unintentionally and threaten internal validity. They are neither intentionally manipulated nor central to the experiment’s main hypothesis, so they are not the right answer.

A control group provides a baseline against which the effects of the experimental treatment can be compared. By keeping conditions for this group similar to those of the experimental group except for the treatment, researchers can attribute observed differences in outcomes more confidently to the intervention itself. Control groups are central to establishing internal validity in experimental designs. Thus, the group described in the stem is correctly called a control group.

Option A:
Control groups may receive no treatment, standard treatment or a placebo, depending on the study, but their key role is to mirror the experimental conditions minus the active ingredient being tested. This comparative function aligns exactly with the stem, making this option correct.

Option B:
A placebo group receives an inert or sham treatment and is one specific type of control group, particularly in clinical trials, but the more general term used in basic experimental design is control.

Option C:
Random describes a procedure for assignment or selection, not a type of group, so it does not fit the blank, which asks about the role of a group.

Option D:
Matched groups are formed by pairing participants on key variables before assigning them to conditions, which is a method of forming comparable groups but not the standard name of the comparison group itself.