ITRIP

Question 1

What is qualitative research?
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Qualitative research is an empirical approach that relies primarily on the collection of non-numeric data, focusing on understanding people’s experiences, perceptions, and the richness of information in specific contexts.
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Why is qualitative research important?
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Qualitative research is important because it explores ‘how’ and ‘why’ questions, provides rich data on lived experiences, gives voice to participants, and complements quantitative research by offering deeper insights.
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What are some common qualitative research methods?
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Common qualitative research methods include interviews, focus groups, qualitative surveys, observation, participatory methods, and creative methods such as photo-methodology.
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What is the difference between unstructured and structured interviews?
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Unstructured interviews have a natural conversational flow with open-ended questions, while structured interviews use a set of standard questions that are asked uniformly to all participants.
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What is mixed methods research?
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Mixed methods research combines quantitative and qualitative approaches to help answer complex research questions, allowing for a more comprehensive understanding of the phenomena being studied.
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What is reflexive thematic analysis?
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Reflexive thematic analysis is a method for identifying, analyzing, and reporting patterns (themes) within qualitative data, emphasizing the researcher’s reflexivity and interpretation in the analysis process.
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What are the strengths of qualitative research?
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Strengths of qualitative research include its ability to provide context, flexibility, depth of insight, participant-centered perspectives, and the generation of new theories through rich narratives.
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What is phenomenology in qualitative research?
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Phenomenology is a qualitative research framework that focuses on exploring and understanding the essence of lived experiences of individuals regarding a particular phenomenon.
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What is the purpose of purposive sampling in qualitative research?
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Purposive sampling is used to select participants based on specific characteristics or qualities relevant to the research question, ensuring that the data collected is rich and informative.
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What are the main types of mixed methods research designs?
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The main types of mixed methods research designs include convergent parallel design, explanatory sequential design, exploratory sequential design, and embedded design.
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How does qualitative research contribute to psychological theories?
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Qualitative research contributes to psychological theories by generating and refining theories through in-depth exploration of participants’ experiences and contextual insights.
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What are the challenges of conducting mixed methods research?
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Challenges of mixed methods research include the need for researchers to be skilled in both qualitative and quantitative methods, potential time and resource intensiveness, and ensuring effective integration of diverse data types.
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What is thematic analysis?
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Thematic analysis is a method for analyzing qualitative data by identifying and interpreting patterns (themes) within the data. It includes steps like familiarization, coding, and theme development.
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How does one ensure quality in qualitative research?
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Quality in qualitative research can be ensured through strategies like reflexivity, member checking, thick description, triangulation, and adhering to established criteria such as credibility and transferability.
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What is the role of participant feedback in qualitative research?
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Participant feedback is used to validate and refine the researcher’s interpretations and conclusions, ensuring that findings accurately reflect the participants’ perspectives and experiences.
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What is grounded theory?
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Grounded theory is a qualitative research method that aims to develop a theory grounded in data systematically gathered from participants, focusing on the processes and interactions within a specific context.
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What is ethnography in qualitative research?
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Ethnography is a qualitative research approach that involves the in-depth study of cultural groups through observation and participation, aiming to understand the cultural characteristics and practices of the group.
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What is a case study in qualitative research?
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A case study is an in-depth exploration of a single case or multiple cases within a real-world context, aiming to gain insights into complex phenomena through detailed analysis.
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What is qualitative research?
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Qualitative research is an empirical approach that relies primarily on the collection of non-numeric data, focusing on understanding people’s experiences, perceptions, and the richness of information in specific contexts.
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Question 2

What is statistics?
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Statistics is a branch of mathematics concerned with organizing, analyzing, and interpreting groups of numbers.
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What are descriptive statistics?
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Descriptive statistics summarize and describe the characteristics of a group of numbers, providing a simple overview of the data.
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What are inferential statistics?
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Inferential statistics involve drawing conclusions about a population based on data from a sample, allowing researchers to make generalizations.
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What is the purpose of Null Hypothesis Significance Testing (NHST)?
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NHST tests hypotheses about differences in population means by determining whether observed data can be attributed to chance.
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What is a confidence interval?
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A confidence interval is a range of values, derived from sample statistics, that is likely to contain the population parameter with a certain level of confidence.
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Why do researchers study samples instead of populations?
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Studying samples is more practical and cost-effective than studying entire populations, allowing researchers to make inferences about the population based on sample characteristics.
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What are measures of central tendency?
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Measures of central tendency include the mean, median, and mode, which summarize a set of scores to represent the central point of the data.
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How is the mean calculated?
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The mean is calculated by summing all scores and dividing by the number of scores (M = ∑X / N).
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What does the standard deviation indicate?
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The standard deviation measures the average amount by which scores differ from the mean, indicating the spread or variability of the data.
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What is skewness in a frequency distribution?
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Skewness refers to the asymmetry of the distribution of scores; a distribution can be skewed to the right (positively skewed) or left (negatively skewed).
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What is the difference between the mode and the median?
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The mode is the most frequently occurring score in a distribution, while the median is the middle score when all scores are arranged in order.
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What is the range in statistics?
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The range is the difference between the maximum and minimum scores in a dataset, providing a measure of the spread of the data.
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What is the purpose of using a histogram?
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A histogram is a graphical representation of the frequency distribution of a dataset, showing the shape and spread of the data visually.
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How do outliers affect the mean?
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Outliers can significantly affect the mean, making it unrepresentative of the data set as they can pull the mean towards their extreme values.
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What is the significance of a normal distribution in statistics?
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In a normal distribution, the mean, median, and mode are all equal, and it is commonly assumed in statistical analysis to facilitate various inferential procedures.
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What is the role of variance in understanding data variability?
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Variance measures the average squared deviation from the mean, providing insight into how spread out the scores are in a dataset.
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What is a t-test used for?
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A t-test is used to determine whether there is a statistically significant difference between the means of two groups.
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What is the importance of presenting results in statistics?
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Presenting results helps to communicate findings effectively, allowing for interpretation and understanding of data patterns and relationships.
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What does it mean for data to be normally distributed?
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Data is normally distributed when it follows a bell-shaped curve, with most observations clustering around the central mean and fewer observations at the extremes.
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What is statistics?
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Statistics is a branch of mathematics concerned with organizing, analyzing, and interpreting groups of numbers.
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Question 3

What is the Standard Normal Distribution?
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The Standard Normal Distribution is a normal distribution where raw scores are converted into Z scores, with a mean (M) of 0 and a standard deviation (SD) of 1.
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How do you calculate a Z score?
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A Z score is calculated using the formula Z = (X - M) / SD, where X is the raw score, M is the mean, and SD is the standard deviation.
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What is the purpose of the Standard Error of the Mean (SEM)?
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The SEM estimates how much sample means tend to vary from the true population mean, calculated as SD divided by the square root of the sample size (n).
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What does NHST stand for?
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NHST stands for Null Hypothesis Significance Testing, a systematic procedure for testing a hypothesis to determine if sample results reflect real population trends.
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What is an Independent Groups t-test?
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An Independent Groups t-test evaluates whether the difference between the means of two independent samples is greater than what would occur by chance under the null hypothesis.
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What is the Central Limit Theorem?
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The Central Limit Theorem states that the distribution of sample means will be approximately normal if the sample size is sufficiently large, even if the original data is not normally distributed.
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Define variance.
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Variance is the average of the squared deviations from the mean, calculated as [∑ (X - M)²] / N, where N is the number of scores.
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What is the significance of a p-value?
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A p-value indicates the probability that the observed results occurred by chance. A common threshold is p < 0.05, suggesting that the results are statistically significant.
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Explain the 68-95-99 rule in relation to the normal distribution.
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The 68-95-99 rule states that approximately 68% of data falls within ±1 SD, 95% within ±2 SDs, and 99% within ±3 SDs of the mean in a normal distribution.
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What does a Z score of +2 indicate?
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A Z score of +2 indicates that the score is 2 standard deviations above the mean.
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What is the relationship between sample size and the Standard Error of the Mean (SEM)?
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As the sample size increases, the SEM decreases, leading to more precise estimates of the population mean.
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What is meant by the term ‘null hypothesis’?
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The null hypothesis (H0) is a statement that there is no effect or difference in the population, which researchers aim to test against.
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What distinguishes descriptive statistics from inferential statistics?
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Descriptive statistics summarize and describe the features of a dataset, while inferential statistics use sample data to make predictions or generalizations about a population.
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How is the t-statistic calculated in an Independent Samples t-test?
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The t-statistic is calculated by dividing the difference between the sample means by the standard error of the difference between the two sample means.
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What does it mean if a result is statistically significant?
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A statistically significant result indicates that the observed difference is unlikely to have occurred by chance, typically when p < 0.05.
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What is a frequency table?
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A frequency table organizes data to show the number of occurrences of each value or category within a dataset.
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What is the role of histograms in data analysis?
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Histograms visually represent the distribution of data, allowing for the identification of patterns such as skewness and modality.
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Define standard deviation.
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Standard deviation is the square root of variance and measures the amount of variation or dispersion in a set of values.
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What is the critical value in hypothesis testing?
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The critical value is a threshold that determines whether to reject the null hypothesis, based on the chosen significance level and the distribution of the test statistic.
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What is the significance level (alpha) in hypothesis testing?
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The significance level (alpha) is the threshold for determining statistical significance, commonly set at 0.05, indicating a 5% risk of concluding that a difference exists when there is none.
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How does variability within groups affect the t-test?
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High variability within groups can reduce the t-statistic, making it less likely to find a statistically significant difference between group means.
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What does it mean for a sample mean to be ‘unusual’?
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A sample mean is considered unusual if it falls beyond the range expected under the null hypothesis, often determined by its Z score or p-value.
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What is a two-tailed test?
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A two-tailed test evaluates whether a sample mean is significantly different from the population mean in either direction (higher or lower).
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What is the purpose of a Z table?
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A Z table provides the percentage of scores associated with different Z scores on the standard normal distribution, helping to determine probabilities.
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What is the Standard Normal Distribution?
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The Standard Normal Distribution is a normal distribution where raw scores are converted into Z scores, with a mean (M) of 0 and a standard deviation (SD) of 1.
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Question 4

What is Null Hypothesis Significance Testing (NHST)?
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NHST is a systematic procedure used to determine whether the results obtained from a sample support a hypothesis about a population, focusing on whether observed effects are statistically significant.
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What does the independent samples t-test evaluate?
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The independent samples t-test evaluates whether the means of two independent groups differ significantly from one another, taking into account the variability within each group.
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What is the null hypothesis (H0)?
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The null hypothesis (H0) states that there is no effect or difference between groups in the population, serving as a baseline for comparison.
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How is the t-statistic calculated?
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The t-statistic is calculated by taking the difference between the sample means and dividing it by the standard error of the difference between the means.
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What does a p-value indicate?
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A p-value indicates the probability of observing the test results, or something more extreme, under the null hypothesis. A small p-value suggests that the observed effect is unlikely due to chance.
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What is the significance level commonly used in hypothesis testing?
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The conventional significance level used in hypothesis testing is 0.05, meaning there’s a 5% risk of concluding that a difference exists when there is no actual difference.
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What is the Central Limit Theorem?
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The Central Limit Theorem states that the distribution of the sample means will approximate a normal distribution as the sample size becomes large, regardless of the population’s distribution.
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What are degrees of freedom (df) in the context of a t-test?
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Degrees of freedom (df) for a t-test involving two independent samples is calculated as the total sample size minus 2 (N1 + N2 - 2).
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Why is it important to consider effect sizes in addition to p-values?
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Effect sizes provide a measure of the magnitude of the difference between groups, whereas p-values indicate statistical significance. Reporting both gives a clearer picture of the practical significance of findings.
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What is standard deviation and how is it related to variability?
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Standard deviation is a measure of the amount of variation or dispersion in a set of values. It quantifies how much scores deviate from the mean.
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What does variability between groups refer to?
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Variability between groups refers to the differences in how participants in different groups respond to treatments or conditions, indicating the effectiveness of interventions.
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What is the purpose of a one-tailed test?
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A one-tailed test is used to determine if there is a statistically significant effect in one specific direction (e.g., whether one group has a higher mean than another).
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What is the standard error of the mean (SEM)?
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The standard error of the mean (SEM) estimates the variability of the sample mean from the population mean, calculated as the standard deviation divided by the square root of the sample size.
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How does sample size affect the t-test?
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Larger sample sizes tend to provide more accurate estimates of the population parameters, reduce the standard error, and increase the power of the t-test to detect significant differences.
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What is the significance of a critical t value?
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The critical t value is the threshold that the calculated t-statistic must exceed to reject the null hypothesis, determined based on the chosen alpha level and degrees of freedom.
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What is the relationship between the null hypothesis and the research hypothesis?
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The null hypothesis (H0) posits no difference or effect, while the research hypothesis (H1) suggests that there is a difference or effect. Testing aims to reject H0 in favor of H1.
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What is a two-tailed test?
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A two-tailed test checks for the possibility of an effect in both directions (i.e., whether one group is different from another without specifying the direction of the difference).
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What does it mean if p < 0.05?
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If p < 0.05, it indicates that the observed difference is statistically significant, suggesting that the null hypothesis can be rejected and that a real effect may be present.
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What is the formula for the t-test?
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The formula for the t-test is t = (M1 - M2) / SED, where M1 and M2 are the sample means and SED is the standard error of the difference between the two means.
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What does the term ‘signal’ refer to in statistical analysis?
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In statistical analysis, ‘signal’ refers to the actual effect or relationship being measured, while ‘noise’ refers to random variability that obscures the signal.
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What is the importance of random allocation in true experiments?
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Random allocation helps ensure that groups are equivalent at the start of the experiment, minimizing biases and allowing for causal inferences about the effects of the independent variable.
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What is the role of confidence intervals in statistical reporting?
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Confidence intervals provide a range of values within which the true population parameter is likely to fall, offering more context about the precision and reliability of the estimate.
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What are descriptive and inferential statistics?
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Descriptive statistics summarize and describe the characteristics of a data set, while inferential statistics use sample data to make generalizations or predictions about a population.
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What is Null Hypothesis Significance Testing (NHST)?
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NHST is a systematic procedure used to determine whether the results obtained from a sample support a hypothesis about a population, focusing on whether observed effects are statistically significant.
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Question 5

What is the purpose of Null Hypothesis Significance Testing (NHST)?
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NHST is a systematic procedure for testing a hypothesis by attempting to disprove the null hypothesis, which states that there is no effect of the independent variable on the dependent variable.
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What does a t-test evaluate?
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A t-test evaluates whether the difference between the means of two samples is greater than what would occur by chance if there were no real difference in the population.
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What is an effect size?
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Effect size quantifies the magnitude of the difference between groups in a study, providing context for the practical significance of the findings beyond just statistical significance.
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What does Cohen’s d measure?
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Cohen’s d measures the effect size by expressing the difference between two group means in terms of their common standard deviation, allowing for comparison across different studies.
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What are Type I and Type II errors?
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Type I error occurs when the null hypothesis is rejected when it is true (false positive), while Type II error occurs when the null hypothesis is accepted when it is false (false negative).
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How is the t-statistic calculated?
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The t-statistic is calculated by dividing the difference between the sample means by the standard error of the differences.
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What is the significance of reporting exact p-values?
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Reporting exact p-values provides a more complete understanding of the statistical significance and allows for better interpretation of the results in context.
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What is the importance of confidence intervals in statistical analysis?
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Confidence intervals provide a range of values within which the population parameter is expected to fall, thus reflecting the precision of the estimate and the uncertainty around the sample mean.
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What factors influence the power of a statistical test?
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The power of a statistical test is influenced by sample size, the significance criterion used (e.g., alpha level), and the size of the effect being measured.
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How should effect sizes be interpreted?
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Effect sizes can be interpreted according to Cohen’s conventions: d = 0.2 is small, d = 0.5 is medium, and d = 0.8 is large, providing context for the practical significance of the findings.
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What is a critical value in the context of a t-test?
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A critical value is a threshold that the calculated t-statistic must exceed to reject the null hypothesis, determined by the significance level and degrees of freedom.
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Why is it important to report confidence intervals around effect sizes?
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Reporting confidence intervals around effect sizes helps to understand the range of possible values for the effect in the population, indicating the reliability and precision of the estimated effect.
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What is the relationship between sample size and margin of error (MoE)?
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A larger sample size typically results in a smaller margin of error, leading to more precise estimates of the population mean.
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What does a confidence interval that includes zero indicate?
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A confidence interval that includes zero suggests that there may be no significant difference between the groups being compared, implying that the null hypothesis cannot be rejected.
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What is the common alpha level used in hypothesis testing?
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The most commonly used alpha level in hypothesis testing is 0.05, which indicates a 5% risk of committing a Type I error.
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What does SEM stand for and what does it represent?
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SEM stands for Standard Error of the Mean, which represents the standard deviation of the sampling distribution of the sample mean, indicating how much the sample mean is expected to vary from the population mean.
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How is the pooled standard deviation used in calculating Cohen’s d?
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The pooled standard deviation is used as the denominator in the calculation of Cohen’s d, providing a common measure of variability for the two groups being compared.
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What does it mean when a confidence interval is wide?
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A wide confidence interval indicates high variability in the data or a small sample size, suggesting that the estimate of the population parameter is less precise.
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How can researchers reduce Type II errors?
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Researchers can reduce Type II errors by increasing sample size, using more sensitive measures, and adopting a higher significance level (alpha).
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What is the significance of using a two-tailed test in hypothesis testing?
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A two-tailed test assesses the possibility of an effect in both directions (greater than and less than), providing a more conservative approach to testing hypotheses.
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Why is NHST considered a starting point in research?
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NHST is considered a starting point because it provides initial evidence for or against a hypothesis, but additional context such as effect sizes and confidence intervals is necessary for a complete understanding of the results.
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What does it mean to reject the null hypothesis?
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Rejecting the null hypothesis means that the evidence from the data suggests that an effect or difference exists in the population, contrary to what the null hypothesis states.
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What is the role of statistical power in hypothesis testing?
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Statistical power is the probability that a test will correctly reject a false null hypothesis, thereby detecting an actual effect when it exists.
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What is meant by ‘sampling variability’?
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Sampling variability refers to the natural fluctuations in sample statistics that occur when different samples are drawn from the same population, leading to different estimates of population parameters.
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What is the purpose of Null Hypothesis Significance Testing (NHST)?
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NHST is a systematic procedure for testing a hypothesis by attempting to disprove the null hypothesis, which states that there is no effect of the independent variable on the dependent variable.
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Question 6

What is Cohen’s d and its significance in psychological research?
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Cohen’s d is a scale-free measure of the separation between two group means. It quantifies the effect size in standardized units, with conventions suggesting that d = 0.2 indicates a small effect, d = 0.5 indicates a medium effect, and d = 0.8 indicates a large effect.
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What is a Type I error?
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A Type I error occurs when the null hypothesis is rejected when it is actually true, leading to a false positive conclusion about the existence of a difference between group means.
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What is a Type II error?
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A Type II error happens when the null hypothesis is accepted when it is false, resulting in a false negative conclusion, where a true effect is missed.
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How does sample size affect the power of a study?
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Larger sample sizes increase the power of a study, reducing the likelihood of Type II errors and improving the chances of detecting a true effect if it exists.
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What is the purpose of confidence intervals (CIs) in research?
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Confidence intervals provide a range of values within which the true population parameter is expected to lie, offering a measure of precision and uncertainty around the estimate.
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What is the significance of a 95% confidence interval?
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A 95% confidence interval indicates that if the same study were repeated many times, approximately 95% of the calculated intervals would contain the true population mean, reflecting a high level of confidence in the estimate.
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What is meta-analysis?
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Meta-analysis is a statistical technique that combines and analyzes results from multiple studies to estimate the overall effect size and identify patterns across research findings.
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What are the PRISMA guidelines?
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The PRISMA guidelines are a set of standards for reporting systematic reviews and meta-analyses, ensuring transparency and consistency in the methodology and findings.
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What are effect sizes and why are they important?
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Effect sizes measure the magnitude of a treatment’s effect, providing context beyond p-values. They are crucial for understanding the practical significance of research findings.
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What is the relationship between statistical significance and effect size?
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Statistical significance indicates whether an effect exists, while effect size indicates the strength or magnitude of that effect. A result can be statistically significant without being practically significant if the effect size is small.
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What is the purpose of conducting a meta-analysis?
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Meta-analysis aims to synthesize findings from multiple studies, enhancing the reliability and generalizability of results, and addressing variability in study outcomes.
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What are the common measures of effect size used in meta-analysis?
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Common measures include Cohen’s d, Hedge’s g, Glass’ Δ, and the odds ratio, which quantify the effect in standardized terms, allowing comparisons across different studies.
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What is the ‘file drawer’ problem in meta-analysis?
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The ‘file drawer’ problem refers to the bias introduced when studies that find no significant effects are less likely to be published, leading to an overrepresentation of positive results in meta-analyses.
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What factors determine the power of a study?
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The power of a study is influenced by sample size, the significance criterion (alpha level), and the size of the effect being measured. Higher power increases the likelihood of detecting true effects.
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What is the importance of using standardised effect sizes in meta-analysis?
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Standardised effect sizes allow researchers to compare results across studies that may use different measures or scales, facilitating a clearer synthesis of findings.
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Why is transparency important in research?
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Transparency in research enhances the credibility and reproducibility of findings, allowing for critical evaluation and fostering trust in scientific conclusions.
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What is the significance of publication bias in meta-analysis?
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Publication bias can skew meta-analysis results by favoring published studies with significant findings, which may not represent the true effect size or variability in the research area.
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What is the role of moderator variables in meta-analysis?
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Moderator variables help identify factors that influence the size of the effect across studies, allowing researchers to explore conditions under which effects may vary.
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How can confidence intervals inform the interpretation of results?
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Confidence intervals provide information on the precision of estimates; narrower intervals indicate greater precision and reliability, while intervals that include zero suggest no significant effect.
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What is the significance of effect size estimates in research?
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Effect size estimates provide crucial information about the magnitude of findings, allowing for better understanding and application of research results in practical settings.
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What is ‘meta-analytic judgment’?
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Meta-analytic judgment involves critically assessing which studies are comparable enough to be included in a meta-analysis, considering factors like similar dependent variables and participant characteristics.
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What are the implications of low statistical power in research studies?
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Low statistical power can lead to missed true effects (Type II errors), overestimation of effect sizes, and reduced replicability of results, undermining the credibility of research findings.
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What is the impact of a study’s quality on meta-analysis outcomes?
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The quality of individual studies included in a meta-analysis affects the overall validity of the findings; poor-quality studies can introduce bias and variability, skewing the results.
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What should be included in a systematic review protocol?
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A systematic review protocol should outline criteria for selecting studies, procedures for locating studies, and plans for analyzing data, ensuring methodological rigor and transparency.
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What is Cohen’s d and its significance in psychological research?
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Cohen’s d is a scale-free measure of the separation between two group means. It quantifies the effect size in standardized units, with conventions suggesting that d = 0.2 indicates a small effect, d = 0.5 indicates a medium effect, and d = 0.8 indicates a large effect.
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