Research Methods MCQ: Quiz! Test

The correct answer is "the extent to which results can be generalized outside the experimental conditions". External validity refers to the ability to apply research findings to real-world situations beyond the specific conditions of an experiment. It is important to assess external validity to determine the generalizability and relevance of research findings to a broader population or context.

The correct answer is "repeating the experiment exactly how it was previously done." This means conducting the experiment again using the same procedures, materials, and conditions as the original experiment. By doing so, researchers can determine if the same results are obtained, which helps to establish the reliability and validity of the findings. It allows for the evaluation of the consistency and reproducibility of the experiment, ensuring that the results are not due to chance or random factors.

The statement "all of the above" is the correct answer because quasi-experiments lack random assignment, which means that participants are not randomly assigned to either the program or control group. Without random assignment, we cannot be sure that the program and control group are equivalent, leading to potential differences between the groups that may affect the outcome. Additionally, quasi-experiments are unable to control for all threats to internal validity, meaning that there may be other factors or variables that could influence the results. Therefore, all of these statements are true regarding quasi-experiments.

The given answer suggests that in time-series designs, the dependent variable is measured regularly both before and after the intervention. This means that data is collected at multiple time points to track any changes in the variable of interest. This design allows for the examination of the impact of the intervention over time and helps to establish a cause-and-effect relationship between the intervention and the dependent variable.

Conceptual replication involves testing the same conceptual variable, but with a different operational definition. This means that the researcher aims to replicate the original study by examining the same underlying concept or idea, but using a different method or measurement to operationalize it. This helps to strengthen the validity of the findings by demonstrating that the results are consistent across different ways of measuring the same concept. By using different operational definitions, researchers can further validate the robustness and generalizability of their findings.

Experimenter bias refers to the tendency of the experimenter to treat subjects differently based on their knowledge of the condition they are in. This can lead to biased results and affect the dependent variable. To eliminate this bias, naive or blind experimenters can be used. Naive experimenters are unaware of the conditions being tested, while blind experimenters are aware but do not have direct contact with the subjects. Using these approaches helps ensure that the experimenter's expectations do not influence the outcome of the study.

Pairwise comparisons refer to the process of comparing the means of different conditions or groups in a study. This means that the researcher is looking at how the mean of one condition differs significantly from the mean of another condition. In this context, it is mentioned that there are some factors that are between subjects (i.e., different participants are assigned to different conditions) and some factors that are within subjects (i.e., the same participants are exposed to different conditions). Therefore, the answer suggests that when comparing the means of different conditions, some factors are manipulated between participants and some factors are manipulated within participants.

Experiment-wise alpha refers to the probability that the experimenter has made a type 1 error (rejecting a true null hypothesis) in at least one of the comparisons conducted in a study. This concept is particularly relevant in complex comparisons where more than two means are compared simultaneously. By considering experiment-wise alpha, researchers can assess the overall risk of making a type 1 error across multiple comparisons, rather than focusing solely on individual comparisons.

The correct answer is comparing a new population to the original population. This involves conducting the same experiment with a different group of individuals to see if the results are consistent across different populations. By comparing the new population to the original population, researchers can determine if the findings are generalizable or if they were specific to the original sample. This helps to establish the external validity of the study and strengthens the overall conclusions that can be drawn from the research.

Attrition (mortality) threats refer to the changes in the research participants over time. This means that the people who stay with the program may be different from those who drop out. This can introduce bias into the study as the characteristics and behaviors of those who remain in the program may differ from those who choose to leave. Therefore, the answer correctly identifies that attrition can lead to differences between the two groups and potentially impact the validity of the research findings.

The correct answer is that the conclusions are incorrect because a type I or II error was made. This means that the researcher either falsely rejected a true null hypothesis (type I error) or failed to reject a false null hypothesis (type II error), leading to incorrect conclusions. This suggests that there may be issues with the statistical analysis or the interpretation of the results, leading to inaccurate conclusions.

The correct answer is that the dependent variable was changed by a confounding variable. This means that the observed effects in the study were not actually caused by the independent variable being studied, but instead by another variable that was not accounted for. This confounding variable could have influenced the dependent variable and therefore led to incorrect conclusions being drawn.

The correct answer is "the measured variables do not relate to the conceptual variables." This means that the variables that were actually measured in the study do not accurately represent or capture the intended conceptual variables that the researchers were interested in studying. As a result, any conclusions drawn from the study may be invalid or inaccurate because they are based on measurements that are not relevant to the research question.

Constructive replication involves adding new conditions to an experiment to rule out alternative explanations. This means that when conducting a replication study, researchers introduce additional variables or conditions to ensure that the original findings are not due to any confounding factors. By doing so, they aim to strengthen the validity and reliability of the original results and provide further evidence for the phenomenon under investigation. This approach helps to establish the robustness of the findings and increase confidence in the conclusions drawn from the study.

The correct answer is "the observed effects can be found only under very limited conditions." This means that the conclusions drawn from the study may not be applicable or generalizable to a broader population or different settings. The effects observed in the study may be specific to the particular conditions or context in which the study was conducted and may not hold true in other situations.

Random error refers to the unpredictable and uncontrollable fluctuations that occur in measurements or observations. These errors can arise due to various factors such as human error, instrument malfunction, or environmental conditions. Random error can have negative effects on the accuracy and precision of the data collected in a study. In the context of the given options, random error can increase the likelihood of a type II error, which is the failure to reject a false null hypothesis. This means that random error can lead to the incorrect acceptance of a null hypothesis, resulting in a false conclusion. Additionally, random error can also reduce the power of a study, which refers to the ability to detect a true effect or relationship. Therefore, random error can have detrimental effects on the validity and reliability of research findings.

The correct answer is "differences on the dependent variable across the levels of one". This means that the main effect refers to the differences observed in the dependent variable when comparing the different levels of one independent variable. In other words, it measures how the independent variable influences the dependent variable across its various levels.