Research Methods In Psychology Quiz

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  • 1/17 Questions

    Exact replication

    • Repeating the experiment exactly how it was previously done
    • Comparing a new population to the original population
    • Testing the same conceptual variable, but with a different operational definition
    • Adding new conditions to an experiment to rule out alternative explanations
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About This Quiz

This quiz assesses knowledge of research methods in psychology, focusing on key concepts like main effects, experimental errors, and validity types. It's designed to enhance understanding of experimental design and data interpretation.

Research Methods In Psychology Quiz - Quiz

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  • 2. 

    External validity

    • The extent to which results can be generalized outside the experimental conditions

    • The extent to which the research is conducted in situations that are similar to everyday life

    • When a study creates the hoped-for changes

    Correct Answer
    A. The extent to which results can be generalized outside the experimental conditions
    Explanation
    External validity refers to the degree to which the findings of a study can be applied or generalized to real-world settings and populations beyond the specific conditions of the experiment. It is concerned with whether the results obtained in a controlled laboratory setting can be applied to a wider range of situations and individuals. This concept helps researchers determine the extent to which their findings can be considered applicable and relevant in practical settings.

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  • 3. 

    Quasi-experiments lack random assignment

    • Cannot be sure that the program and the control group are equivalent

    • Unable to control for all threats to internal validity

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    The correct answer is "all of the above" because quasi-experiments lack random assignment, which means that the program and control group may not be equivalent. Additionally, without random assignment, it is difficult to control for all threats to internal validity, such as selection bias or confounding variables. Therefore, all of these statements are true and explain why quasi-experiments cannot provide the same level of certainty as true experiments with random assignment.

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  • 4. 

    Experimenter bias

    • When expectations effect the dependent variable

    • When participants guess the research hypothesis, to eliminate this cover stories, unrelated experiments, and nonreactive measures can be used

    • The experimenter may treat subjects differently because they know what condition they’re in, to avoid this naive or blind experimenters can be used

    Correct Answer
    A. The experimenter may treat subjects differently because they know what condition they’re in, to avoid this naive or blind experimenters can be used
    Explanation
    Experimenter bias refers to the situation where the experimenter's knowledge or expectations about the experimental conditions influence their treatment of the subjects. This can lead to biased results as the experimenter's behavior may inadvertently affect the dependent variable. To mitigate this bias, researchers can use naive or blind experimenters who are unaware of the experimental conditions, ensuring that they treat all subjects equally and minimizing the potential for bias.

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  • 5. 

    Pairwise comparisons

    • Which means differ significantly from one another

    • When one condition mean is compared with another condition mean

    • Some factors are between subjects and some are within subjects

    Correct Answer
    A. When one condition mean is compared with another condition mean
    Explanation
    This answer suggests that pairwise comparisons are used to compare the means of different conditions. It also indicates that these conditions can differ significantly from each other. Additionally, the answer mentions that there may be factors that are between subjects (i.e., different individuals) and factors that are within subjects (i.e., within the same individual).

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  • 6. 

    Conceptual replication

    • Repeating the experiment exactly how it was previously done

    • Comparing a new population to the original population

    • Testing the same conceptual variable, but with a different operational definition

    • Adding new conditions to an experiment to rule out alternative explanations

    Correct Answer
    A. Testing the same conceptual variable, but with a different operational definition
    Explanation
    Conceptual replication refers to the process of testing the same conceptual variable, but with a different operational definition. This means that researchers aim to replicate a study by examining the same underlying concept or idea, but they use a different way of measuring or operationalizing that concept. By doing so, they can verify the robustness and generalizability of the original findings, as well as gain a deeper understanding of the phenomenon under investigation.

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  • 7. 

    Time-series designs

    • Measuring changes in a single person

    • Grouping based on preexisting characteristics (e.g sex)

    • The dependent variable is assessed regularly both before and after the intervention

    Correct Answer
    A. The dependent variable is assessed regularly both before and after the intervention
    Explanation
    This answer suggests that in time-series designs, the dependent variable is measured at regular intervals both before and after the intervention. This allows for the observation of any changes in the variable over time and the assessment of the intervention's impact.

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  • 8. 

    Participant replication

    • Repeating the experiment exactly how it was previously done

    • Comparing a new population to the original population

    • Testing the same conceptual variable, but with a different operational definition

    • Adding new conditions to an experiment to rule out alternative explanations

    Correct Answer
    A. Comparing a new population to the original population
    Explanation
    The correct answer is comparing a new population to the original population. This involves studying a different group of individuals and comparing their results to those of the original group. By doing so, researchers can determine if the findings from the original study are consistent across different populations, increasing the generalizability and reliability of the results.

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  • 9. 

    Statistical conclusion validity

    • Conclusions are incorrect because a type I or II error was made

    • The measured variables do not relate to the conceptual variables

    • The dependent variable was changed by a confounding variable

    • The observed effects can be found only under very limited conditions

    Correct Answer
    A. Conclusions are incorrect because a type I or II error was made
    Explanation
    The correct answer is that the conclusions are incorrect because a type I or II error was made. This means that either a false positive (type I error) or a false negative (type II error) occurred in the statistical analysis, leading to incorrect conclusions. Type I errors happen when a true null hypothesis is rejected, while type II errors occur when a false null hypothesis is not rejected. Both types of errors can lead to incorrect conclusions and affect the validity of the statistical analysis.

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  • 10. 

    External validity

    • Conclusions are incorrect because a type I or II error was made

    • The measured variables do not relate to the conceptual variables

    • The dependent variable was changed by a confounding variable

    • The observed effects can be found only under very limited conditions

    Correct Answer
    A. The observed effects can be found only under very limited conditions
    Explanation
    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 only be applicable in specific situations or settings. The results may not be generalizable to a larger population or different contexts.

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  • 11. 

    Attrition (mortality) threats:

    • Changes in the research participants over time

    • People who stay with the program may be different than those who drop out

    • Changes in the social climate over time

    Correct Answer
    A. People who stay with the program may be different than those who drop out
    Explanation
    Attrition (mortality) threats refer to the changes in research participants over time, specifically when people who drop out of a program are different from those who stay. This can introduce bias into the study results, as the characteristics and experiences of those who remain in the program may not be representative of the entire population. It is important to consider attrition rates and potential differences between dropouts and participants who complete the program to ensure the validity and generalizability of the findings.

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  • 12. 

    Extraneous variables

    • Random error may increase the likelihood of a type II error may reduce power

    • Systematic error, which provides an alternative explanation for the results

    • The extent to which the manipulation involves the participants in the research

    Correct Answer
    A. Random error may increase the likelihood of a type II error may reduce power
    Explanation
    Random error refers to the variability or fluctuations in data that are caused by chance factors. It is unrelated to the variables being studied and can occur in any research study. Random error can affect the accuracy and precision of measurements, leading to inconsistencies in the data. In the context of the given options, random error may increase the likelihood of a type II error, which is the failure to reject a false null hypothesis. This means that random error can make it more likely for researchers to incorrectly conclude that there is no effect or relationship when there actually is. Additionally, random error may reduce the statistical power of a study, which is the ability to detect an effect if it truly exists.

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  • 13. 

    Constructive replication

    • Repeating the experiment exactly how it was previously done

    • Comparing a new population to the original population

    • Testing the same conceptual variable, but with a different operational definition

    • Adding new conditions to an experiment to rule out alternative explanations

    Correct Answer
    A. Adding new conditions to an experiment to rule out alternative explanations
    Explanation
    The correct answer is adding new conditions to an experiment to rule out alternative explanations. This involves introducing additional variables or conditions to the experiment in order to eliminate any potential confounding factors or alternative explanations for the observed results. By systematically varying the conditions and controlling for potential confounds, researchers can strengthen the validity and reliability of their findings. This process is essential for ensuring that the observed effects are indeed caused by the manipulated variables and not by any other factors.

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  • 14. 

    Experiment-wise alpha

    • The probability that the experimenter made a type 1 error in at least one of the comparisons

    • Complex comparisons where more than two means are compared at the same time

    • Means comparisons that take into account experiment-wise alpha

    Correct Answer
    A. The probability that the experimenter made a type 1 error in at least one of the comparisons
    Explanation
    Experiment-wise alpha refers to the probability that the experimenter has made a type 1 error in at least one of the comparisons. It is used in complex comparisons where more than two means are compared simultaneously and takes into account the overall probability of making a type 1 error across all comparisons. By considering experiment-wise alpha, researchers can assess the likelihood of false positive results in their study, which helps to control the overall false positive rate and maintain the integrity of the findings.

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  • 15. 

    Construct validity

    • Conclusions are incorrect because a type I or II error was made

    • The measured variables do not relate to the conceptual variables

    • The dependent variable was changed by a confounding variable

    • The observed effects can be found only under very limited conditions

    Correct Answer
    A. The measured variables do not relate to the conceptual variables
    Explanation
    The correct answer is "the measured variables do not relate to the conceptual variables." This means that the variables being measured in the study do not accurately represent or correspond to the underlying concepts or constructs that the researcher is trying to study. As a result, any conclusions drawn from the study based on these measured variables would be invalid and incorrect.

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  • 16. 

    Internal validity

    • Conclusions are incorrect because a type I or II error was made

    • The measured variables do not relate to the conceptual variables

    • The dependent variable was changed by a confounding variable

    • The observed effects can be found only under very limited conditions

    Correct Answer
    A. The dependent variable was changed by a confounding variable
    Explanation
    The correct answer is "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 rather by another variable that was not accounted for. This confounding variable could have influenced the dependent variable and led to incorrect conclusions being drawn.

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  • 17. 

    The main effect

    • The effect of one factor within a level of another factor

    • When each level of an independent variable effects the dependent variable differently

    • Differences on the dependent variable across the levels of one

    Correct Answer
    A. Differences on the dependent variable across the levels of one
    Explanation
    The correct answer suggests that the main effect refers to the differences on the dependent variable across the levels of one factor. In other words, it is the impact or influence that a single independent variable has on the dependent variable, without considering any other factors. The main effect allows researchers to understand how changes in one factor affect the outcome variable, providing valuable insights into the relationship between variables.

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  • Current Version
  • Jul 20, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • May 28, 2015
    Quiz Created by
    Noyon
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