Research Exam 2 PDF

**1. What is the difference between descriptive and inferential statistics?** - **Descriptive statistics summarize and describe the features of a dataset, such as the mean, median, mode, variance, and standard deviation. They provide a clear and concise summary of the data.** - **Infer...

**1. What is the difference between descriptive and inferential statistics?** - **Descriptive statistics summarize and describe the features of a dataset, such as the mean, median, mode, variance, and standard deviation. They provide a clear and concise summary of the data.** - **Inferential statistics use sample data to make generalizations or predictions about a population. They involve hypothesis testing, confidence intervals, and regression analysis, allowing researchers to draw conclusions beyond the immediate data.** **2. Match specific descriptive statistics (e.g., frequency, variance) to each of the four descriptive attributes (e.g., magnitude, central tendency).** - **Magnitude: Variance, Standard Deviation** - **Central Tendency: Mean, Median, Mode** - **Variability: Range, Variance, Standard Deviation** - **Frequency: Frequency Distribution, Mode** **3. What is the difference between a percent and a percentile?** - **Percent refers to the proportion of a single value in relation to 100 (e.g., 50% means half of the total).** - **Percentile refers to the rank of a value in a distribution, indicating the percentage of data points below a particular score (e.g., 90th percentile means the value is higher than 90% of all other data points).** **4. Match the four types of measurement scales to their main features, with examples.** - **Nominal: Categories without order (e.g., gender, eye color).** - **Ordinal: Categories with a defined order, but intervals between them are not meaningful (e.g., rankings, education levels).** - **Interval: Ordered categories with meaningful intervals, but no true zero (e.g., temperature in Celsius).** - **Ratio: Ordered categories with meaningful intervals and a true zero (e.g., weight, height).** **5. What is a magnitude measure appropriate for a nominal scale?** **A magnitude measure for a nominal scale would be frequency or mode, as they describe how often categories occur or the most common category.** **6. Why are means not suitable to a teaching evaluation rating scale? Explain how mode and median are applicable.** **The mean is sensitive to outliers and may not accurately represent the typical response on a teaching evaluation scale, especially if there are extreme ratings. The mode (most frequent score) and median (middle value) are better because they are less influenced by extreme ratings and more representative of the overall distribution of responses.** **7. How do outliers affect measures of distribution?** **Outliers can skew the mean and increase variance, making it less representative of the data as a whole. They may distort the true central tendency and variability of the dataset, leading to inaccurate conclusions.** **8. What is the shape of normal, skewed, flat kurtosis, and bimodal distributions?** - **Normal: Symmetrical, bell-shaped curve with a peak at the mean.** - **Skewed: Asymmetrical distribution, either to the left (negatively skewed) or to the right (positively skewed).** - **Flat kurtosis: Distribution with a flatter peak, indicating lower than normal peak and wider tails.** - **Bimodal: Two distinct peaks in the distribution.** **9. Where is the mean, mode, and median located on a normal distribution?** **In a normal distribution, the mean, median, and mode all coincide at the center of the distribution.** **10. For a negatively skewed distribution like academic performance of graduate school applicants, how do mean, mode, and median differ?** **In a negatively skewed distribution, the mean will be lower than the median, which will be lower than the mode. The skew pulls the mean toward the lower end.** **11. What percent of the population are within plus and minus 1 standard deviation from the mean on a normal distribution?** **Approximately 68% of the population lies within ±1 standard deviation from the mean in a normal distribution.** **12. Identify standard scores and percentiles of a normal distribution for -1, -1.5, -2 standard deviations below the mean.** - **-1 SD: Standard score = -1, Percentile ≈ 15.87%** - **-1.5 SD: Standard score = -1.5, Percentile ≈ 6.68%** - **-2 SD: Standard score = -2, Percentile ≈ 2.28%** **13. What is the difference between a bivariate and a multivariate measure of association?** - **Bivariate measures the relationship between two variables (e.g., correlation between height and weight).** - **Multivariate measures the relationship between three or more variables (e.g., multiple regression analyzing the effect of age, gender, and income on spending habits).** **14. How are strength and direction represented by the correlation statistic r?** - **Strength: The magnitude of r (closer to 1 or -1 indicates a stronger relationship).** - **Direction: The sign of r (positive values indicate a positive relationship, negative values indicate a negative relationship).** **15. Predict performance on one variable from another variable on a bivariate scattergram.** **In a bivariate scattergram, if the correlation is strong, you can predict the value of one variable based on the value of the other by following the trend or line of best fit.** **16. Is a correlation a descriptive or an inferential statistic?** **A correlation is an inferential statistic when it is used to generalize findings from a sample to a population, but it can also be used as a descriptive statistic when simply summarizing the relationship between two variables in the sample.** **17. Do a visual-graphical analysis of the time series experiment data results in the lecture slides from Ingham et al. (2001) and the example reading of Petersen et al. (2014).** **Unfortunately, I cannot access or analyze the data or lecture slides directly. However, here are some general guidelines for analyzing time series data:** - **a. Say in words what level and trend is shown in baseline and in treatment. Look for the overall direction (upward/downward) and stability of the data points during each phase.** - **b. Determine mean phase difference and percent non-overlapping data. Calculate the mean level of each phase and compare them. Percent non-overlapping data is the percentage of data points from one phase that do not overlap with the next phase.** - **c. Make a judgment as to whether the data graphs show evidence of a treatment effect. If there's a clear, consistent change in the trend or level after the treatment phase compared to the baseline, it may indicate a treatment effect.** **18. How can you determine if a mean phase difference (MPD) is a large effect that might be practically significant?** **A large MPD indicates a large difference between the baseline and treatment phases, suggesting a strong effect. To assess practical significance, consider the magnitude of change relative to the context of the study (e.g., whether the change is meaningful in the real world or in clinical practice).** **19. How can MPD or PND be misleading about deciding whether treatment caused improvement?** **MPD (Mean Phase Difference) or PND (Percent Non-overlapping Data) can be misleading if pre-existing trends or other confounding factors are not considered. For example, if there were a trend before the treatment, the observed change may be due to that trend rather than the treatment itself.** **20. What does it mean when probability testing tells us to reject the null?** **Rejecting the null hypothesis means that the results of the test provide sufficient evidence to conclude that there is an effect or relationship that is unlikely to have occurred by chance. In other words, the difference observed is statistically significant.** **21. What does statistical significance mean?** **Statistical significance means that the observed results are unlikely to have occurred by random chance. If the p-value is less than the alpha level (e.g., p \< 0.05), the result is considered statistically significant.** **22. What is an alpha (α) level and how does it differ from a p-value?** - **The alpha (α) level is the threshold set before testing, typically 0.05, which determines the probability of rejecting the null hypothesis when it is actually true (Type I error).** - **The p-value is the calculated probability from the test, indicating the likelihood that the observed result occurred by chance. If p \< α, we reject the null hypothesis.** **23. What does the "p" in p-value stand for? How is it related to percent confident?** **The \"p\" in p-value stands for probability. A p-value of 0.05 means there is a 5% chance that the observed results are due to random chance. If you use a significance level of 0.05, you are 95% confident that the results are not due to chance.** **24. What are degrees of freedom and how are they related to sample size?** **Degrees of freedom (df) refer to the number of independent values in a statistical calculation that are free to vary. In general, df is related to the sample size by the formula: df = n - 1 for a single sample, where n is the sample size.** **25. What are the two types of degrees of freedom needed for an ANOVA?** - **Between-group degrees of freedom: Calculated as the number of groups minus 1 (k - 1).** - **Within-group degrees of freedom: Calculated as the total number of observations minus the number of groups (n - k).** **26. What are Type I and Type II errors? Explain both in two different ways so you have a solid understanding of them.** - **Type I error: False positive --- rejecting a null hypothesis that is actually true (e.g., concluding there is an effect when there is none).** - **Type II error: False negative --- failing to reject a null hypothesis that is actually false (e.g., concluding there is no effect when there is one).** **27. For Type I family-wise error, give two procedural and two statistical ways to reduce likelihood of this error.** **Procedural ways:** 1. **Limit the number of comparisons**: By reducing the number of hypotheses or tests conducted, the overall likelihood of making at least one Type I error (false positive) is reduced. This minimizes the chance of incorrectly rejecting a true null hypothesis. 2. **Pre-registering the study**: Pre-registration of hypotheses and analysis plans before collecting data helps limit the possibility of performing post hoc comparisons or analyses that could inflate the risk of Type I error. **Statistical ways:** 1. **Adjusting the significance level**: The significance level (alpha) can be adjusted using corrections like the **Bonferroni correction** or **Holm-Bonferroni method**. These methods reduce the alpha level for each individual test in a multiple comparisons situation to control the overall family-wise error rate. 2. **Using more powerful tests**: Using statistical methods that increase the power of individual tests (e.g., more accurate estimates or improved designs) can help reduce the likelihood of Type I errors by providing more reliable results in the first place. **28. What is a Bonferroni correction, when is it used, and for what type of error does it correct?** - **Bonferroni correction** is a method used to address the problem of multiple comparisons in statistical testing. It is applied when multiple hypotheses are being tested simultaneously. - **Formula**: The Bonferroni correction involves dividing the desired alpha level (e.g., 0.05) by the number of comparisons (tests) being performed. For example, if you\'re conducting 5 tests, each test would need to meet a significance level of 0.01 (0.05 / 5) to be considered statistically significant. - **When used**: This correction is used when multiple hypothesis tests are conducted simultaneously to avoid inflating the family-wise error rate (the probability of making at least one Type I error). - **Type of error corrected**: The Bonferroni correction specifically addresses **Type I error**, which is the likelihood of incorrectly rejecting a null hypothesis (false positive). By lowering the alpha threshold, it controls the chance of making any Type I errors across multiple comparisons. **29. What are two procedural and two statistical ways to reduce Type II error?** **Procedural ways:** 1. **Increase the sample size**: By increasing the sample size, the statistical power of a test increases, which reduces the likelihood of a Type II error (failing to reject a false null hypothesis). A larger sample provides more precise estimates and makes it easier to detect a true effect. 2. **Use more reliable measurement tools**: Using more accurate and consistent measurement tools reduces variability in the data, leading to clearer and more confident results that help to avoid Type II errors. **Statistical ways:** 1. **Increase the effect size**: By designing an experiment or study that maximizes the potential effect size (e.g., stronger treatments, clearer group differences), you increase the chances of detecting a true effect, thus reducing the likelihood of Type II errors. 2. **Use a more powerful statistical test**: Certain statistical tests may have higher power depending on the study design. For example, parametric tests generally have higher power than non-parametric tests when the assumptions of the parametric tests are met. **30. Why is a proximal outcome measure better than a distal outcome measure for reducing Type II error?** A **proximal outcome measure** is typically closer to the intervention or the experimental manipulation and more directly related to the effects being studied. It is usually more sensitive to changes, and because of its stronger connection to the intervention, it is more likely to show statistically significant results if an effect is present. This leads to a **reduced likelihood of Type II error**. In contrast, a **distal outcome measure** is more removed or indirect and may be influenced by various other factors that are not part of the experiment, making it harder to detect a true effect. As a result, distal measures can be less sensitive to changes, increasing the risk of a Type II error, where the test fails to detect an effect that actually exists. Thus, proximal measures are better at reducing Type II error because they are typically more sensitive, making it easier to detect real changes in the outcome of interest. 4o mini **You said:** 31\. When we increase a study's power to find treatment effects, what type of error are we reducing? 32. Identify the four assumptions that must be met to use a parametric statistical test validly. 33. Why are parametric tests, despite all their assumption requirements, preferred over non-parametric tests? 34. What parametric tests are used to compare the mean performance in Treatment vs. Control conditions, and in Treatment vs. Alternate treatment vs. Control? 35. What is a simple and a fancy statistical way to control for pre-test differences **ChatGPT said:** ChatGPT **31. When we increase a study's power to find treatment effects, what type of error are we reducing?** Increasing a study\'s **power** reduces the likelihood of a **Type II error** (false negative). Type II error occurs when a study fails to reject a false null hypothesis, i.e., when it fails to detect a true treatment effect. Increasing power improves the chances of correctly identifying a true effect when it exists, making it less likely to miss significant findings. **32. Identify the four assumptions that must be met to use a parametric statistical test validly.** The four key assumptions for using parametric tests are: 1. **Normality**: The data should follow a normal distribution (or the sampling distribution of the mean should be approximately normal if the sample size is large enough, due to the Central Limit Theorem). 2. **Homogeneity of variance (Homoscedasticity)**: The variance within each group being compared should be approximately equal. 3. **Independence**: The observations or measurements should be independent of each other (i.e., one participant\'s data does not influence another\'s). 4. **Interval or ratio data**: The data should be measured on an interval or ratio scale (i.e., continuous data), where the distances between values are meaningful. **33. Why are parametric tests, despite all their assumption requirements, preferred over non-parametric tests?** Despite their assumptions, **parametric tests** are preferred over **non-parametric tests** for several reasons: 1. **Greater statistical power**: Parametric tests are more powerful when the assumptions are met. They are more likely to detect a true effect compared to non-parametric tests, which may require larger sample sizes to achieve the same power. 2. **More precise estimates**: Parametric tests often provide more accurate and precise estimates because they take advantage of more information (e.g., means and variances). Non-parametric tests, on the other hand, are typically based on ranks or medians and use less information from the data. 3. **Standardized assumptions**: When the assumptions of parametric tests are met, they provide more reliable results. Non-parametric tests, while useful when assumptions are violated, are generally less efficient and may not be as precise. 4. **Flexibility in analyzing complex data**: Parametric tests allow for more complex models (e.g., ANOVA, regression analysis), providing more flexibility to analyze different kinds of relationships between variables. **34. What parametric tests are used to compare the mean performance in Treatment vs. Control conditions, and in Treatment vs. Alternate treatment vs. Control?** - **Treatment vs. Control**:\ The appropriate **parametric test** for comparing the mean performance between a treatment group and a control group is typically the **Independent Samples t-test** (also known as the **two-sample t-test**). This test compares the means of two independent groups to determine if there is a statistically significant difference between them. - **Treatment vs. Alternate Treatment vs. Control**:\ When comparing means across more than two groups (e.g., Treatment vs. Alternate Treatment vs. Control), the appropriate test is an **Analysis of Variance (ANOVA)**. Specifically, a **One-Way ANOVA** is used when there is one independent variable (e.g., group type: treatment, alternate treatment, control) with three or more levels. If the data has multiple factors (e.g., time and treatment group), a **Two-Way ANOVA** would be used. **35. What is a simple and a fancy statistical way to control for pre-test differences?** - **Simple statistical way**:\ One simple way to control for pre-test differences is to use **Analysis of Covariance (ANCOVA)**. ANCOVA allows for the comparison of post-test scores while controlling for pre-test scores as a covariate. By adjusting for the initial differences in the groups\' pre-test scores, ANCOVA provides a more accurate estimate of the treatment effects. - **Fancy statistical way**:\ A more complex approach involves using a **Multilevel (Hierarchical) Linear Model (HLM)** or **Mixed-Effects Model**, which can account for both within-subject and between-subject variability. This method allows researchers to model pre-test differences and other covariates in a more flexible manner, particularly useful when data is collected over multiple time points or when the assumptions of ANCOVA are not fully met. These models can handle repeated measures, nested data, and more complex dependencies in the data. 4o mini **You said:** 38\. What is a multiple regression test and what does it show? 39. What statistical test is used to examine whether norm-referenced measures have clusters of performance across subtest scores that allow them to be grouped into composites? 40. Identify the parts of the following three statistical statements: t(27) = 2.39, p =.023, d =.34; and F(2, 35) = 6.19, p =.005, ŋ = 0.51, and x2(1) = 4.98, p =.026, f = 0.348. **ChatGPT said:** ChatGPT **38. What is a multiple regression test and what does it show?** A **multiple regression test** is a statistical technique used to examine the relationship between one dependent variable and two or more independent variables (predictors). It assesses how multiple predictors together influence the dependent variable and allows for controlling other variables to isolate the effect of each predictor. - **What it shows**: - The **strength** and **direction** of the relationship between each independent variable and the dependent variable (via regression coefficients). - The **statistical significance** of each predictor (using p-values). - The **proportion of variance** in the dependent variable that is explained by the independent variables (via R-squared). Multiple regression can also be used to model complex relationships where predictors interact or have nonlinear effects on the dependent variable. **39. What statistical test is used to examine whether norm-referenced measures have clusters of performance across subtest scores that allow them to be grouped into composites?** The statistical test used to examine whether norm-referenced measures have clusters of performance across subtest scores that can be grouped into composites is called **Factor Analysis**. - **Factor analysis** is a technique that identifies underlying factors (or latent variables) that explain the correlations between observed variables (e.g., subtest scores). It helps to identify if there are clusters or groupings of subtests that measure similar constructs, which can then be combined into composite scores. - There are two types of factor analysis: **Exploratory Factor Analysis (EFA)** and **Confirmatory Factor Analysis (CFA)**. EFA is used when the underlying structure is unknown, while CFA tests a hypothesized factor structure. **40. Identify the parts of the following three statistical statements: t(27) = 2.39, p =.023, d =.34; and F(2, 35) = 6.19, p =.005, ŋ = 0.51, and x²(1) = 4.98, p =.026, f = 0.348.** Here is the breakdown of each statistical statement: 1. **t(27) = 2.39, p =.023, d =.34** - **t(27)**: This is the **t-statistic** for a t-test, with 27 representing the **degrees of freedom (df)**. - **= 2.39**: This is the **calculated value** of the t-statistic, which compares the difference between the means relative to the variability within the groups. - **p =.023**: This is the **p-value**, which indicates the probability of observing the data (or more extreme data) if the null hypothesis is true. A p-value less than 0.05 suggests statistical significance. - **d =.34**: This is **Cohen\'s d**, a measure of **effect size**, indicating the standardized difference between the groups. A value of 0.34 is a small to medium effect size. 2. **F(2, 35) = 6.19, p =.005, ŋ = 0.51** - **F(2, 35)**: This is the **F-statistic** for an ANOVA, with **2** representing the **numerator degrees of freedom** (between-group variance) and **35** representing the **denominator degrees of freedom** (within-group variance). - **= 6.19**: This is the **calculated value** of the F-statistic, comparing the variance between groups relative to the variance within groups. - **p =.005**: This is the **p-value**, indicating a significant result (p \< 0.05). - **ŋ = 0.51**: This is **partial eta-squared (η²)**, a measure of effect size in ANOVA, showing the proportion of variance in the dependent variable explained by the independent variable. A value of 0.51 indicates a large effect. 3. **x²(1) = 4.98, p =.026, f = 0.348** - **x²(1)**: This is the **chi-square statistic** for a chi-square test of independence or goodness-of-fit, with **1** representing the **degrees of freedom (df)**. - **= 4.98**: This is the **calculated value** of the chi-square statistic, assessing whether the observed data fits the expected distribution. - **p =.026**: This is the **p-value**, indicating a statistically significant result (p \< 0.05). - **f = 0.348**: This is the **Cohen\'s f**, a measure of effect size used in the context of chi-square tests or other tests, which quantifies the strength of the association or effect. A value of 0.348 suggests a small to medium effect. **Summary:** - **t(27)**, **F(2, 35)**, and **x²(1)** refer to the **degrees of freedom** for each respective test. - The numbers following the **t**, **F**, and **x²** represent the **calculated statistic values**. - The **p-values** indicate whether the results are statistically significant. - **d**, **η²**, and **f** represent **effect size measures**, indicating the magnitude of the effect observed in the analysis. 4o mini **You said:** 42\. What does practical significance mean? 43. Which types of significance involves probability testing: statistical, practical, clinical, or educational **ChatGPT said:** ChatGPT **42. What does practical significance mean?** **Practical significance** refers to the **real-world relevance** or **meaningfulness** of a result, beyond just statistical significance. It assesses whether the magnitude of the effect or difference observed in a study is large enough to be important or useful in practice. - For example, a study may show that a new drug reduces symptoms by a statistically significant amount, but if the actual reduction is so small that it doesn't noticeably improve patients\' daily lives, it may lack practical significance. - It often involves looking at the **effect size**, which measures the strength or magnitude of the relationship or difference, and considering whether that effect is large enough to warrant attention or application in the field. Practical significance emphasizes the **real-world impact** of the findings, while statistical significance simply means that the result is unlikely to have occurred by chance. **43. Which types of significance involve probability testing: statistical, practical, clinical, or educational?** **Statistical significance** is the only type of significance that directly involves **probability testing**. - **Statistical significance** involves determining whether the observed results of a study are likely to have occurred due to chance. This is typically assessed using probability values (p-values), which help researchers decide whether to reject the null hypothesis in favor of the alternative hypothesis. If the p-value is below a predefined threshold (often 0.05), the results are considered statistically significant. Other types of significance do not directly involve probability testing: - **Practical significance** refers to the real-world importance of a finding, and while it may be informed by statistical tests, it does not involve probability testing itself. - **Clinical significance** refers to the importance of a finding in a medical or healthcare context and focuses on whether an intervention makes a meaningful difference to patients. Like practical significance, it is influenced by statistical results but does not involve probability testing itself. - **Educational significance** pertains to whether a finding has meaningful implications for education practices or outcomes. It may be influenced by statistical significance, but it also involves judgment about what is useful or relevant in the educational context. In summary, **statistical significance** is the only one directly tied to probability testing through methods like hypothesis testing and p-value calculations. 4o mini **You said:** 46\. What is an effect size? Why do we use it in addition to testing statistical significance? 47. What are two main types of effect size statistics? Which one cannot be greater than 1.0? 48. What are the standard intervals for small, medium, and large effect sizes for Cohen's d and for correlation r? 49. Why can we expect a larger effect size for treatment versus no-treatment control than for treatment versus alternate treatment? **ChatGPT said:** ChatGPT **46. What is an effect size? Why do we use it in addition to testing statistical significance?** An **effect size** is a **quantitative measure** of the strength or magnitude of a relationship or difference observed in a study. It helps to understand how meaningful the results are, beyond just determining whether they are statistically significant. - **Why we use it**: - **Statistical significance** tells us whether an observed effect is likely due to chance, but it doesn\'t convey how large or important that effect is. - **Effect size** provides context by measuring the **practical importance** of the result. For example, a study may yield a statistically significant p-value, but if the effect size is very small, the result may not be meaningful in real-world terms. Using effect size alongside statistical significance helps researchers interpret the practical relevance of their findings. **47. What are two main types of effect size statistics? Which one cannot be greater than 1.0?** The two main types of effect size statistics are: 1. **Cohen's d**: This is commonly used to measure the **magnitude of the difference** between two groups (e.g., treatment vs. control). It expresses the difference in means between two groups in terms of **standard deviations**. - **Interpretation**: Larger values of Cohen\'s d indicate a larger effect (i.e., greater difference between the groups). 2. **Pearson's r**: This is used to measure the **strength and direction** of the relationship between two continuous variables. - **Interpretation**: The closer r is to 1 or -1, the stronger the relationship. A value of 0 indicates no relationship. **Which cannot be greater than 1.0?** - **Pearson's r** cannot be greater than 1.0. The value of **r** ranges from -1.0 (a perfect negative relationship) to +1.0 (a perfect positive relationship). A value above 1.0 would not be possible, as it would imply an impossible relationship strength. - **Cohen's d** can exceed 1.0, and in fact, larger values indicate more substantial differences between groups. **48. What are the standard intervals for small, medium, and large effect sizes for Cohen's d and for correlation r?** - **Cohen's d** (used for comparing group differences): - **Small effect size**: d=0.2d = 0.2d=0.2 - **Medium effect size**: d=0.5d = 0.5d=0.5 - **Large effect size**: d=0.8d = 0.8d=0.8 These values provide a general guideline, but interpretation can vary based on the context and field of study. - **Pearson's r** (used for measuring correlation or relationship between two variables): - **Small effect size**: r=0.1r = 0.1r=0.1 - **Medium effect size**: r=0.3r = 0.3r=0.3 - **Large effect size**: r=0.5r = 0.5r=0.5 Again, these are general benchmarks, and effect size interpretation can depend on the specific field or study context. **49. Why can we expect a larger effect size for treatment versus no-treatment control than for treatment versus alternate treatment?** We can expect a **larger effect size** for **treatment versus no-treatment control** than for **treatment versus alternate treatment** for the following reasons: 1. **Clearer contrast**: The **treatment vs. no-treatment** comparison represents a **more distinct contrast**. The difference between having the treatment and not having it is often more pronounced, especially if the treatment is expected to have a substantial impact on the outcome. This can result in a larger effect size because the groups are more different. 2. **Smaller differences between treatments**: In a **treatment vs. alternate treatment** comparison, both groups are likely receiving interventions that have some effect on the outcome. Since both treatments are aimed at improving the same outcome, their effects may be more **similar**, which reduces the **magnitude of the difference** between the two groups, leading to a **smaller effect size**. 3. **Ceiling effects**: If the alternate treatment is already somewhat effective, it might create a \"ceiling effect,\" where improvements due to the treatment are less noticeable when compared to the no-treatment group, resulting in a smaller effect size. In summary, the difference between **treatment and no-treatment** is often more pronounced, leading to a larger effect size compared to the comparison between **treatment and alternate treatment**, where the effects of the two treatments may overlap more. 4o mini **You said:** Qualitative Research 1. What does it mean to say that qualitative research is an alternate epistemological paradigm? 2. What are two premises of qualitative research that conflict with the hypothetico-deductive basis of quantitative research? 3. What do these words mean in qualitative research: emergent, inductive, interpretive? 4. According to Hengst et al. (2015), what does thick description mean? 5. What types of questions are answered by qualitative research? 6. What questions did Stillman et al. (1999) investigate? 7. Did the Stillman et al. (1999) questions involve hypothesis-testing? 8. What is qualitative data versus qualitative methods, and which of the two can occur in quantitative research? 9. Identify six types of qualitative research. 10. What type of research was used by Hengst et al. (2009)? 11. What is mixed method research? 12. How was the participant selection in Ukrainetz and Fresquez (2003) conducted emergently? 13. Identify three procedures for obtaining data in qualitative research. 14. How does a participant interview procedure differ between quantitative and qualitative research? 15. What is an example of emergent data analysis in Stillman et al. (1999)? 16. What is the difference between a survey and a qualitative interview? 17. What is triangulation and why is it used? 18. What does a constant-comparison method involve? 19. What is the difference between an observation and an interpretation in data recording? 20. What are the qualitative method quality features of credibility, transferability, dependability, and confirmability? 21. Which of the four quality features are closest to external validity and reliability? 22. Find three checks on soundness in Ukrainetz and Fresquez (2003) 23. What are codes, categories, and themes in qualitative analyses? 24. What themes were identified in Ukrainetz and Fresquez (2003)? 25. How is qualitative research both part and not part of the scientific method? **ChatGPT said:** ChatGPT **1. What does it mean to say that qualitative research is an alternate epistemological paradigm?** Qualitative research is considered an **alternate epistemological paradigm** because it is grounded in a different understanding of how knowledge is created and understood. While quantitative research relies on objective, measurable data and aims to test hypotheses or theories, qualitative research emphasizes subjective experiences, meanings, and interpretations. It recognizes that knowledge is socially constructed and context-dependent, often focusing on understanding the \"why\" and \"how\" of phenomena rather than establishing causal relationships or generalizable results. **2. What are two premises of qualitative research that conflict with the hypothetico-deductive basis of quantitative research?** 1. **Inductive Reasoning**: Qualitative research often begins with **inductive reasoning**, meaning theories and hypotheses emerge from the data rather than being tested against it. In contrast, quantitative research typically follows a **hypothetico-deductive** approach, where hypotheses are developed before data collection and tested through data analysis. 2. **Subjectivity and Context**: Qualitative research values the **subjective** experiences and meanings of participants, and acknowledges that understanding is shaped by context. Quantitative research, on the other hand, tends to prioritize objectivity and seeks to minimize or control for context in order to isolate variables. **3. What do these words mean in qualitative research: emergent, inductive, interpretive?** - **Emergent**: Refers to the process in which the research design, questions, and analysis evolve as the study progresses, rather than being rigidly predetermined. New insights and themes emerge from the data as the research unfolds. - **Inductive**: In qualitative research, this means that researchers develop theories or hypotheses from the data itself rather than testing preconceived hypotheses. The goal is to build a deeper understanding based on observed patterns or themes. - **Interpretive**: Qualitative research is interpretive in that it seeks to understand how individuals or groups make sense of their experiences, emphasizing meaning-making processes. Researchers interpret data through their understanding of the context and the perspectives of participants. **4. According to Hengst et al. (2015), what does thick description mean?** **Thick description** refers to a detailed and rich account of the context, events, and actions involved in a study, particularly in qualitative research. It provides not just a surface-level observation, but also the meaning, context, and nuances behind the behavior or phenomena being studied. This approach allows readers to understand the **\"why\"** and **\"how\"** of the actions or events, rather than just the **\"what\"**. **5. What types of questions are answered by qualitative research?** Qualitative research typically answers questions about: - **How** and **why** something occurs (e.g., How do individuals experience a particular phenomenon? Why do they behave in a certain way?) - **Meaning and interpretation** (e.g., What does this experience mean to the participants?) - **Process** (e.g., How do people navigate specific challenges over time?) - **Contextual understanding** (e.g., What social, cultural, or environmental factors influence the situation?) These questions seek to understand the depth and complexity of human behavior and experiences. **6. What questions did Stillman et al. (1999) investigate?** **Stillman et al. (1999)** investigated the **social and emotional aspects** of children\'s communication development. Specifically, they looked into how children with specific speech and language disorders perceive their own communication abilities and the social challenges they face. **7. Did the Stillman et al. (1999) questions involve hypothesis-testing?** No, **Stillman et al. (1999)** did not involve hypothesis-testing in the traditional quantitative sense. Their study was qualitative in nature, focusing on understanding the experiences and perceptions of children with speech and language disorders rather than testing a specific hypothesis. **8. What is qualitative data versus qualitative methods, and which of the two can occur in quantitative research?** - **Qualitative Data**: This refers to non-numerical data such as interviews, observations, or textual materials that provide insights into people's experiences, behaviors, or perceptions. - **Qualitative Methods**: These are the strategies or techniques used to gather and analyze qualitative data, such as in-depth interviews, focus groups, and participant observation. **Qualitative data** can occur in quantitative research, such as when open-ended survey responses are analyzed for themes, but **qualitative methods** are typically distinct from quantitative methods like statistical analysis or controlled experiments. **9. Identify six types of qualitative research.** 1. **Phenomenology**: Focuses on the lived experiences of individuals and how they make sense of those experiences. 2. **Grounded Theory**: Aims to develop theories grounded in the data, usually focusing on processes and interactions. 3. **Ethnography**: Involves the study of cultures and communities, often through immersive observation. 4. **Case Study**: An in-depth exploration of a specific case, individual, or group. 5. **Narrative Research**: Focuses on personal stories and the way individuals make sense of their lives. 6. **Action Research**: Aims to solve practical problems through collaboration and reflection with participants. **10. What type of research was used by Hengst et al. (2009)?** **Hengst et al. (2009)** used a **qualitative research design** to examine the processes of language intervention with preschool-aged children. They employed **case study methods** to explore the effectiveness of the intervention in specific contexts. **11. What is mixed method research?** **Mixed-method research** combines both qualitative and quantitative research methods in a single study to provide a more comprehensive understanding of a research problem. For example, a researcher might collect quantitative data through surveys and qualitative data through interviews, then integrate both types of data for a fuller analysis. **12. How was the participant selection in Ukrainetz and Fresquez (2003) conducted emergently?** In **Ukrainetz and Fresquez (2003)**, participant selection was **emergent** in that the researchers chose participants based on the **needs of the study as it unfolded**, rather than having a fixed list at the start. This approach allowed flexibility, enabling the researchers to adapt to the data and ensure they were focusing on relevant participants. **13. Identify three procedures for obtaining data in qualitative research.** 1. **Interviews**: One-on-one or group conversations to gather in-depth personal experiences and perceptions. 2. **Observations**: Watching participants in their natural settings to gather contextual and behavioral data. 3. **Document Analysis**: Analyzing existing materials such as field notes, written records, or media content. **14. How does a participant interview procedure differ between quantitative and qualitative research?** In **quantitative research**, interviews tend to be structured, with fixed questions aimed at gathering specific data that can be easily quantified. In **qualitative research**, interviews are often semi-structured or unstructured, allowing for more open-ended questions and a focus on exploring the participant's perspectives and experiences in depth. **15. What is an example of emergent data analysis in Stillman et al. (1999)?** In **Stillman et al. (1999)**, emergent data analysis refers to the **unanticipated themes or patterns** that arose as the researchers interacted with the data. For example, as interviews progressed, new questions or areas of inquiry emerged that were not initially planned for, allowing the researchers to refine their analysis based on what was relevant to participants' experiences. **16. What is the difference between a survey and a qualitative interview?** A **survey** is typically a **structured tool** used to collect standardized responses (often numerical) from many participants. A **qualitative interview**, on the other hand, is more flexible and open-ended, allowing the researcher to explore the participant's experiences, thoughts, and feelings in depth. **17. What is triangulation and why is it used?** **Triangulation** refers to using **multiple data sources, methods, or researchers** to cross-check and validate findings in qualitative research. It is used to increase the **credibility** and **validity** of the results by ensuring that the findings are not biased or limited by a single perspective. **18. What does a constant-comparison method involve?** The **constant-comparison method** is an approach to qualitative data analysis where data are continuously compared against each other to identify patterns and develop categories or themes. Researchers compare new data to existing data throughout the study, refining and adjusting their understanding as they progress. **19. What is the difference between an observation and an interpretation in data recording?** An **observation** is a **factual** account of what is seen or heard during data collection. It is a **descriptive** statement about what occurred. An **interpretation**, however, involves **inferring meaning** or making judgments about the significance of what was observed. **20. What are the qualitative method quality features of credibility, transferability, dependability, and confirmability?** - **Credibility**: Ensures the findings accurately represent the participants' perspectives or experiences (akin to internal validity in quantitative research). - **Transferability**: Refers to the extent to which the findings can be applied or transferred to other settings or groups (akin to external validity). - **Dependability**: Ensures that the research process is consistent and reliable over time. - **Confirmability**: Ensures that the findings are shaped by the participants and the data, not by the researcher's biases or perspectives (akin to objectivity in quantitative research). **21. Which of the four quality features are closest to external validity and reliability?** - **Transferability** is closest to **external validity**, as it concerns whether the findings can be applied to other settings or populations. - **Dependability** is closest to **reliability**, as it focuses on the consistency of the research process and results. **22. Find three checks on soundness in Ukrainetz and Fresquez (2003)** In **Ukrainetz and Fresquez (2003)**, checks on soundness include: 1. **Member checks** (participants reviewing findings to ensure accuracy). 2. **Triangulation** (using multiple data sources to validate findings). 3. **Peer debriefing** (discussing findings with other researchers for feedback). **23. What are codes, categories, and themes in qualitative analyses?** - **Codes**: Labels or tags applied to pieces of data to categorize and organize information. - **Categories**: Groupings of similar codes that help to organize the data into broader themes. - **Themes**: Broad patterns or insights that emerge from the analysis, representing the core findings of the study. **24. What themes were identified in Ukrainetz and Fresquez (2003)?** In **Ukrainetz and Fresquez (2003)**, some themes included the **importance of communication support** for children with language impairments and the **need for collaboration** between educators and speech-language pathologists. **25. How is qualitative research both part and not part of the scientific method?** Qualitative research is **part** of the scientific method in that it involves systematic data collection, analysis, and interpretation. However, it is **not part** in the traditional sense, as it doesn\'t focus on hypothesis testing or statistical generalization. Instead, it emphasizes understanding complex phenomena and the meanings behind them, often through inductive reasoning and context-specific insights.