Research methods flashcards

All 30 cards

1. Card 1

   Why is critical thinking considered a key benefit of using statistics in decision-making?

   Statistics enable critical thinking by providing a framework to examine empirical evidence and use data effectively. This helps individuals avoid making decisions based on biased or emotional reasoning, promoting objectivity.

2. Card 2

   Distinguish between an independent variable (IV) and a dependent variable (DV) in a psychological study.

   An independent variable is something that is manipulated or changed by the researcher, explaining or predicting variation in another variable. A dependent variable is what is measured, showing the change or variation that results from the manipulation of the independent variable.

3. Card 3

   How do inferential statistics allow researchers to generalize findings from a sample to a larger population?

   Inferential statistics assess the extent to which characteristics observed in a representative sample are likely to apply to the overall population. By accounting for sampling error, they help test hypotheses about whether relationships between variables observed in the sample generalize to the broader population.

4. Card 4

   Explain the primary difference between descriptive and inferential statistics.

   Descriptive statistics summarize and describe the characteristics of a dataset, such as means and standard deviations, without making predictions beyond that data. Inferential statistics, conversely, are used to draw conclusions and make generalizations about a population based on data from a sample, often involving hypothesis testing.

5. Card 5

   Define 'sampling error' and explain why it necessitates the use of inferential statistics.

   Sampling error refers to the natural variability that occurs because a sample is rarely a perfect representation of the entire population. Different samples from the same population will yield different results. Inferential statistics are needed to determine if an observed result is a real effect or merely due to this inherent sampling variability.

6. Card 6

   What is the purpose of a null hypothesis (H₀) in hypothesis testing?

   The null hypothesis (H₀) assumes there is no effect or no difference between groups or variables. It serves as a baseline assumption that researchers attempt to disprove or reject through statistical analysis, often stating that any observed effect is due to chance or sampling error.

7. Card 7

   How does an alternative hypothesis (H₁) differ from a null hypothesis (H₀)?

   The alternative hypothesis (H₁) proposes that there is an effect, a difference, or a relationship between variables, directly contradicting the null hypothesis. It represents the researcher's prediction or the effect they are trying to find evidence for.

8. Card 8

   Explain what a confidence interval (CI) represents and its typical reporting percentage.

   A confidence interval is a range of plausible values for a population effect, typically reported at 95% confidence. This means that if the study were repeated many times, 95% of the calculated intervals would contain the true population parameter.

9. Card 9

   What is the critical rule for interpreting a confidence interval in relation to the null hypothesis?

   The critical rule is that if the confidence interval includes zero, we fail to reject the null hypothesis, indicating no statistically significant effect. If the confidence interval does not include zero, we reject the null hypothesis, suggesting evidence of a real effect.

10. Card 10

    Define 'p-value' and explain its role in hypothesis testing.

    A p-value is the probability of obtaining the observed data, or more extreme data, if the null hypothesis were true. In hypothesis testing, if the p-value is less than a predetermined significance level (e.g., 0.05), the null hypothesis is rejected, indicating a statistically significant result.

11. Card 11

    How does the relationship between confidence intervals and p-values ensure consistent decision-making in hypothesis testing?

    Confidence intervals and p-values provide consistent decisions in hypothesis testing. If a confidence interval does not include zero, it implies a statistically significant effect, which will correspond to a p-value less than the chosen significance level (e.g., p < 0.05), both leading to the rejection of the null hypothesis.

12. Card 12

    What is an 'effect size' and why is it important in addition to knowing if an effect is statistically significant?

    An effect size quantifies the magnitude or strength of the relationship between variables or the difference between groups. It's important because statistical significance (e.g., from a p-value) only tells us if an effect is likely real, while effect size tells us how big or practically meaningful that effect is, which p-values alone do not convey.

13. Card 13

    Differentiate between a 'point estimate' and a 'confidence interval' in the context of effect sizes.

    A point estimate provides a single best guess for the effect size observed in a sample. A confidence interval, on the other hand, provides a range of values that are likely to contain the true population effect size, offering a measure of uncertainty around the point estimate.

14. Card 14

    Why are 'standardized effect sizes' often preferred when comparing results across different studies?

    Standardized effect sizes are preferred because they account for variability in the spread and measurement of data across studies. They are expressed in standard deviation units, making them comparable even when variables are measured on different scales or have different distributions, which is crucial for meta-analyses.

15. Card 15

    Define 'correlation' and explain what it indicates about the relationship between two variables.

    Correlation is present when the value of one variable provides information about the value of another variable. It indicates the direction (positive or negative) and strength of a linear relationship between two quantitative variables.

16. Card 16

    Explain the difference between a positive and a negative correlation.

    A positive correlation means that as values on one variable increase, values on the other variable also tend to increase. A negative correlation means that as values on one variable increase, values on the other variable tend to decrease.

17. Card 17

    What does a correlation coefficient close to 0 suggest about the relationship between two variables?

    A correlation coefficient close to 0 suggests that there is no linear relationship between the two variables. This means that knowing the value of one variable tells us nothing about what to expect for the value of the other variable.

18. Card 18

    What are the necessary conditions for calculating Pearson's correlation coefficient (r)?

    To calculate Pearson's correlation coefficient (r), the variables must be quantifiable (numerical) and on at least an ordinal scale of measurement. Additionally, Pearson's r can only be used if the underlying relationship between the variables is linear.

19. Card 19

    How is the magnitude of a correlation coefficient (r) typically interpreted using Cohen's conventions?

    Cohen's conventions suggest that an 'r' value of 0.10 indicates a small effect, 0.30 a moderate effect, and 0.50 a large effect. However, these are rules of thumb, and the interpretation should also consider the specific subject matter and previous research.

20. Card 20

    In the context of statistical analysis, what does it mean for a variable to be 'dichotomous' or 'binary'?

    A dichotomous or binary variable is a type of categorical variable that has exactly two categories or levels. These categories can be fixed (e.g., male/female) or designed (e.g., yes/no response to a question).

21. Card 21

    Distinguish between 'nominal' and 'ordinal' levels of measurement for categorical variables.

    Nominal variables have two or more categories with no intrinsic order (e.g., nationality, favorite color). Ordinal variables also have two or more categories, but they possess an intrinsic order or can be ranked (e.g., Likert scale responses from 'strongly disagree' to 'strongly agree').

22. Card 22

    Explain the difference between 'interval' and 'ratio' levels of measurement for continuous variables.

    Interval variables have numerical values that can be measured along a continuum, where the difference between values is meaningful, but there is no true zero point (e.g., Celsius temperature). Ratio variables are interval variables with an additional condition: a measurement value of zero means the complete absence of that variable (e.g., Kelvin temperature, height).

23. Card 23

    What is the 'variance' in statistics, and how does it relate to the spread of data?

    Variance is the average of the squared differences from the mean, serving as a measure of how spread out the data points are. A higher variance indicates that data points are more dispersed from the mean, while a lower variance suggests they are clustered closer to the mean.

24. Card 24

    How does 'standard deviation' provide a more intuitive understanding of data spread compared to variance?

    Standard deviation is the square root of the variance and provides a more intuitive understanding of data spread because it is in the same units as the original data. It indicates, on average, how far individual scores typically deviate from the mean, making it easier to interpret than squared differences.

25. Card 25

    What is the 'range' as a measure of dispersion, and what is its main limitation?

    The range is the simplest measure of dispersion, calculated as the difference between the highest and lowest values in a dataset. Its main limitation is that it only considers the two extreme values and tells us nothing about the distribution or spread of the values in between.

26. Card 26

    Explain how the PICO framework aids in the operationalization of a research question in psychology.

    The PICO framework helps operationalize a research question by breaking it down into specific, measurable components: Population, Intervention, Comparison, and Outcome. This specificity transforms a broad topic into a manageable and researchable question, guiding the researcher to define who is being studied, what is being tested, against what it is compared, and what is being measured.

27. Card 27

    DEFINITION: Distinguish between the functions of the Boolean operators 'AND' and 'OR' in a database search strategy.

    The 'AND' operator narrows a search by combining different concepts, requiring all specified terms to be present in the search results. In contrast, the 'OR' operator broadens a search by including synonyms or related terms, retrieving results that contain any of the specified terms.

28. Card 28

    Why is it crucial to develop a comprehensive list of synonyms for key concepts when formulating a search strategy for psychological literature?

    It is crucial to develop a comprehensive list of synonyms because databases do not understand the meaning of words, only the words themselves. Researchers must anticipate different terminology authors might use to describe the same concept to ensure a thorough search and avoid missing relevant studies that use alternative phrasing.

29. Card 29

    Describe the process of 'critical reading' in the context of evaluating psychological research, highlighting at least three key questions a critical reader should ask.

    Critical reading involves questioning claims, checking sources, and being suspicious of absolute statements. A critical reader should ask: Is evidence provided to support the claims? Is the evidence from a credible scientific journal? Are the conclusions drawn by the authors justified by the results presented?

30. Card 30

    DEFINITION: What is 'operationalizing your research question' and why is it important in psychological research?

    Operationalizing a research question means defining it in such a way that it can be measured and studied empirically. This is important because it transforms abstract concepts into concrete variables, making the research question specific, manageable, and testable, which is essential for designing effective studies and interpreting results accurately.

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