An extraneous variable (EV) is any variable other than the independent variable (IV) that might affect the dependent variable (DV). These variables, if not controlled, can introduce unwanted variability into the experiment, potentially affecting the results. However, an EV becomes a confounding variable when it varies systematically along with the IV and influences the DV, thus confounding or mixing up the results. In essence, if an EV is not controlled and correlates with the IV, it can lead to misleading conclusions, making it appear as though the IV is causing an effect when it may not be. This can seriously compromise the internal validity of a study, as it becomes difficult to ascertain whether changes in the DV were due to the IV or the uncontrolled confounding variable. Researchers must identify potential confounding variables during the design phase and employ strategies such as randomisation, matching, or including control variables in statistical analyses to mitigate their impact.

Operationalisation of variables involves defining them in a measurable and quantifiable manner, which is crucial for enhancing the reliability of a psychological study. When variables are operationalised effectively, it ensures that they are consistently and accurately measured across different instances of the experiment. This uniformity in measurement is essential for the reliability of the study, as it allows the results to be replicated. For instance, if a study measures 'stress levels,' operationalisation would specify whether this is quantified through self-report scales, physiological measures, or observational criteria. Clear operationalisation also facilitates clearer communication among researchers and aids in the standardisation of procedures, which is another key aspect of maintaining reliability. Moreover, well-defined operationalised variables enable other researchers to replicate the study in different settings or with different populations, further reinforcing the reliability of the original findings.

Random allocation is a fundamental technique in managing variables, particularly in experimental research. It involves randomly assigning participants to different experimental conditions, which helps ensure that each group is comparable at the start of the experiment. This comparability is crucial for controlling extraneous variables, particularly participant variables like age, gender, intelligence, or personality traits, that might otherwise skew the results. By randomising, these variables are evenly distributed across groups, reducing their potential to confound the results.

However, random allocation has its limitations. In small sample sizes, random allocation may not always result in perfectly matched groups, and some extraneous variables might still differ across groups. Additionally, random allocation doesn’t control for all types of extraneous variables, particularly those that arise during the experiment itself, such as environmental factors or participant reactions to the experimental setting. Therefore, while random allocation is a powerful tool for controlling certain types of variables, it should be used in conjunction with other control techniques for more robust variable management.

Standardisation is a crucial method in controlling variables in psychological research. It involves ensuring that every aspect of the research procedure is consistent and uniform across all participants. This consistency includes the environment in which the research is conducted, the instructions given to participants, the time of day the research takes place, and the way in which the independent variable is manipulated or measured.

Standardisation is implemented through detailed research protocols that specify every step of the procedure. Researchers follow these protocols strictly to ensure that each participant experiences the experiment in the same way. This control method minimises the impact of extraneous variables, particularly those related to the research setting or procedure, and helps in isolating the effect of the independent variable on the dependent variable. For example, in a study assessing the effect of a cognitive task on stress levels, standardisation would ensure that the cognitive task is of the same complexity and duration for all participants, and that the environment is equally quiet and free from distractions. This helps in ensuring that any differences in stress levels are attributable to the cognitive task and not to variations in the experimental setup.

Blinding in research refers to the practice where either the participants, the researchers, or both are kept unaware of certain aspects of the study, such as the allocation of participants to different experimental groups. This is done to prevent biases that might influence the study's outcome.

In single-blind studies, participants are not told about the specific treatment or condition they are receiving, which helps in controlling for participant expectations and placebo effects. In double-blind studies, both participants and researchers are unaware of the group allocations, which further reduces biases. For instance, if researchers are unaware of which participants are receiving a real treatment versus a placebo, they are less likely to treat participants differently or interpret results in a biased manner.

Blinding is particularly important in managing variables that are subjective or influenced by human expectations. By controlling for these biases, blinding enhances the validity of research findings, ensuring that any observed effects are due to the independent variable and not due to preconceived notions or expectations.