Student Name Capella University PSYC FPX 4700 Statistics for the Behavioral Sciences Prof. Name Date PSYC FPX 4700 Assessment 5 Research Report Data analysis helps researchers organize, evaluate, and interpret information to identify meaningful patterns and relationships. In this research report, the analysis examines quiz scores, GPA, total scores, and final exam scores for 105 students. The Pearson correlation results show that Quiz 1 has a weak and statistically nonsignificant relationship with GPA, while Quiz 1, GPA, total scores, and final exam scores show several statistically significant positive relationships. Most notably, total scores are strongly associated with final exam scores (r = .875, p < .001), while GPA has a moderate positive relationship with final exam scores (r = .379, p < .001). These findings describe associations among the variables but do not establish cause-and-effect relationships. Research Purpose and Data Analysis Data analysis involves examining, organizing, transforming, and interpreting data to generate useful information for decision-making (Kelley, 2020). Before statistical tests are performed, researchers typically clean and prepare the data to improve the accuracy and reliability of the findings (Côté, 2021). The purpose of this analysis is to examine student academic data collected across three sections of a course. The variables include Quiz 1 scores, GPA, total scores, and final exam scores. The analysis focuses on the distribution of the variables and the relationships among them. Variables and Scales of Measurement Four variables were included in the analysis: Quiz 1, GPA, Total Score, and Final Exam Score. Quiz 1 is a quantitative variable representing the number of points students received on the first quiz. Because the score can vary numerically within a defined range, it can be treated as a continuous variable for the purposes of this analysis. GPA represents students’ grade point averages. GPA is recorded numerically on a scale such as 0.00 to 4.00. Although GPA may sometimes be grouped into categories for reporting purposes, the numerical GPA values used in this analysis are treated as a continuous variable. Total Score represents students’ overall numerical score in the course. It is a continuous quantitative variable because students can receive different numerical scores across a range. Final Exam Score represents the numerical score earned on the final examination and is also treated as a continuous variable. Research Question and Hypotheses The original research question asks whether there is a significant difference in mean Quiz 1 scores across the three sections of the course. Research question: Is there a statistically significant difference in mean Quiz 1 scores across the three course sections? Null hypothesis (H₀): There is no statistically significant difference in mean Quiz 1 scores across the three sections of the course. Alternative hypothesis (H₁): There is a statistically significant difference in mean Quiz 1 scores across the three sections of the course. A one-way analysis of variance (ANOVA) would normally be used to answer this research question because it compares the mean of a continuous outcome across three independent groups. The Pearson correlation output provided in this report addresses a different question: whether pairs of continuous variables are linearly related. Therefore, the correlation table alone cannot be used to determine whether Quiz 1 means differ among the three course sections. Testing Statistical Assumptions The descriptive statistics provide information about the distribution of the four variables. The dataset contains 105 valid observations for each variable. Variable N Minimum Maximum Mean SD Skewness Kurtosis Quiz 1 105 0 10 7.47 2.481 -0.851 0.162 GPA 105 1.08 4.00 2.8622 0.71266 -0.220 -0.688 Total 105 54 123 100.09 13.427 -0.757 1.146 Final 105 40 75 61.84 7.635 -0.341 -0.277 The skewness and kurtosis statistics provide an initial indication of whether the distributions substantially depart from normality. The variables show some degree of skewness, particularly Quiz 1, which has a skewness of -0.851. However, the values do not indicate an extreme departure from normality based on common practical guidelines. It is important to note that normality should not be evaluated using skewness and kurtosis alone. Visual methods such as histograms and Q-Q plots, as well as the assumptions of the specific statistical test being used, should also be considered. For a one-way ANOVA, researchers should additionally examine independence of observations and homogeneity of variances. Descriptive Interpretation of the Data The average Quiz 1 score was 7.47, with scores ranging from 0 to 10. The standard deviation of 2.481 indicates noticeable variation in students’ quiz performance. The mean GPA was 2.8622, with values ranging from 1.08 to 4.00. The relatively small negative skewness suggests that GPA scores were reasonably balanced, although more students may have been concentrated toward the higher end of the scale. The mean total score was 100.09, with scores ranging from 54 to 123. The final exam had a mean score of 61.84 and a standard deviation of 7.635, with scores ranging from 40 to 75. Overall, the descriptive statistics indicate meaningful variation among students in academic performance, providing an appropriate basis for examining relationships between the variables. Pearson Correlation Results The Pearson correlation analysis examined the linear relationships among Quiz 1, GPA, Total Score, and Final Exam Score. Variables Pearson r p-value Interpretation Quiz 1 and GPA .152 .121 Weak, not statistically significant Quiz 1 and Total .797 < .001 Strong, statistically significant Quiz 1 and Final .499 < .001 Moderate, statistically significant GPA and Total .318 < .001 Moderate, statistically significant GPA and Final .379 < .001 Moderate, statistically significant Total and Final .875 < .001 Very strong, statistically significant Interpretation of the Statistical Findings The correlation between Quiz 1 and GPA was r = .152, p = .121. Because the p-value is greater than the conventional significance level of .05, this relationship is not statistically significant. The result indicates only a weak positive association between Quiz 1 performance and GPA in this sample. Therefore, there is insufficient statistical evidence to conclude that Quiz 1 scores and GPA are linearly related. A strong positive relationship was found between Quiz 1 and Total Score (r = .797, p <

Student Name Capella University PSYC FPX 4700 Statistics for the Behavioral Sciences Prof. Name Date PSYC FPX 4700 Assessment 4 Anova Chi Square Tests and Regression ANOVA, chi-square tests, and regression are widely used statistical methods for analyzing relationships, differences, and patterns in research data. ANOVA is primarily used to compare the means of three or more groups, chi-square tests examine categorical data and observed versus expected frequencies, and regression determines whether one or more variables can predict an outcome. In this assessment, you will practice these statistical methods using F-tables, chi-square critical values, JASP, Excel, APA-style reporting, and interpretation of statistical results. Complete all problems in this Word document. Do not submit additional files. For questions that require calculations, show your work clearly. Make sure each final answer is easy to identify. You may highlight your answers or use a different font color to distinguish them from your calculations and explanations. ANOVA Analysis of variance (ANOVA) is a statistical procedure used to determine whether statistically significant differences exist among the means of multiple groups. A one-way ANOVA examines the effect of one independent variable with two or more levels on a continuous dependent variable. Problem Set 4.1: ANOVA Critical Values Criterion: Explain the relationship between the number of levels of an independent variable (k) and statistical power using calculated critical values. Review the F-distribution table and answer the questions below. This exercise is designed to help you become familiar with locating critical values in an F-table. In an F-table, the degrees of freedom for the numerator, calculated as k − 1, are displayed across the columns. The degrees of freedom for the denominator, calculated as N − k, are displayed across the rows. Separate tables are generally provided for different significance levels, including α = .05 and α = .01. Increasing the Number of Independent-Variable Levels Assume a sample contains 24 participants (N = 24). Determine the appropriate critical F value for each value of k below. Number of Levels (k) Critical F at α = .05 Critical F at α = .01 k = 2     k = 4     k = 6     k = 8     After completing the table, answer the following: As k increases from 1 to 8, does the critical F value generally increase or decrease? Explain how changes in the number of levels of the independent variable are related to statistical power. Problem Set 4.2: One-Way ANOVA Using JASP Criterion: Conduct a one-way ANOVA using JASP. Data: Use the stress.jasp dataset. The dataset contains information about the amount of fat, measured in grams, consumed during a buffet-style lunch by professional bodybuilders experiencing high, moderate, or low levels of stress. Open the stress.jasp dataset in JASP and complete the following procedure: Problem Set 4.3: One-Way ANOVA Using Excel Criterion: Conduct a one-way ANOVA using Microsoft Excel. Use the following data to perform a one-way ANOVA. High Stress Moderate Stress Low Stress 10 9 9 7 4 4 8 7 6 12 6 5 6 8 7 Complete the following steps: Problem Set 4.4: Reporting One-Way ANOVA Results in APA Style Criterion: Report ANOVA findings using APA style. Use the ANOVA results from the preceding analysis to complete the following: State the null hypothesis. Report the ANOVA results in APA format as they might appear in a peer-reviewed journal article. Your report should clearly identify the test statistic, degrees of freedom, p value, and whether the findings are statistically significant. Problem Set 4.5: Interpreting ANOVA Results Criterion: Interpret ANOVA findings and determine which factors are statistically significant. Drakou et al. (2006) examined life satisfaction among sport coaches. The researchers investigated whether life satisfaction differed according to sex, age, marital status, and education. The results are presented below. Independent Variable Group M SD F p Sex Men 3.99 0.51 0.68 .409   Women 3.94 0.49     Age 20s 3.85 0.42 3.04 .029   30s 4.03 0.52       40s 3.97 0.57       50s 4.02 0.50     Marital Status Single 3.85 0.48 12.46 < .001   Married 4.10 0.50       Divorced 4.00 0.35     Education High school 3.92 0.48 0.82 .536   Postsecondary 3.85 0.54       University degree 4.00 0.51       Master’s 4.00 0.59     Using an alpha level of .05, answer the following questions: Explain your answers using the reported F values and p values. Problem Set 4.6: Tukey HSD Post Hoc Test in JASP Criterion: Conduct and report a post hoc analysis using JASP. Data: Use the stress.jasp dataset from Problem Set 4.2. Repeat the initial ANOVA procedure and then complete the following: Keep this output available because you will use it to complete Problem Set 4.7. Problem Set 4.7: Interpreting the Tukey HSD Test Criterion: Interpret Tukey HSD results generated by JASP. Use the output from Problem Set 4.6 to determine which pairs of stress levels show statistically significant differences at the .05 significance level. Identify the specific group comparisons that are significant and explain what the findings mean in relation to fat consumption. Chi-Square Tests Chi-square tests are commonly used when researchers analyze categorical variables. A chi-square goodness-of-fit test compares observed frequencies with expected frequencies, while a chi-square test of independence evaluates whether two categorical variables are associated. Problem Set 4.8: Chi-Square Critical Values Criterion: Explain how critical values change based on degrees of freedom and significance level. Review the chi-square distribution table before answering the questions. In a chi-square table, the degrees of freedom (df) are listed in the far-left column, while the significance levels are displayed across the top of the table. These values are used to locate the appropriate critical value. For a goodness-of-fit test, calculate degrees of freedom using: df = k − 1 Complete the following table. k α = .10 α = .05 α = .01 10       16       22       30       Answer the following questions: Problem Set

Student Name Capella University PSYC FPX 4700 Statistics for the Behavioral Sciences Prof. Name Date Hypothesis, Effect Size, Power, and t Tests Hypothesis testing, effect size, statistical power, and t tests are fundamental concepts in psychological and behavioral research. They help researchers determine whether observed differences are statistically meaningful, estimate the magnitude of an effect, and evaluate whether a sample provides enough evidence to draw conclusions about a population. In this assessment, you will apply these concepts to sampling distributions, hypothesis testing, effect size and power, confidence intervals, and one-sample and independent-samples t tests using JASP and Excel. Complete all problems in the designated Word document. Do not submit additional files. For problems that require mathematical calculations, show your work clearly. Make sure each final answer is easy to identify; you may use bold text, highlighting, or a different font color to distinguish answers from your calculations. Problem Set 3.1: Sampling Distribution of the Mean Criterion: Interpret population mean and variance. A sampling distribution provides information about how sample statistics are distributed across repeated samples. Understanding the population mean and variance is important because these values provide the foundation for interpreting sample results. Suppose a researcher wants to investigate the mean attention span of individuals in a hypothetical population. Attention span is measured as the amount of time, in minutes, that an individual remains focused on a particular task. The population is normally distributed, with the parameters provided in the original problem. Use the information provided in the problem to answer the following questions: 1. What is the population mean (μ)? Answer: __________________________ 2. What is the population variance (σ²)? Answer: __________________________ 3. Sketch the population distribution. Draw a normal distribution curve and clearly label the population mean and the values representing plus and minus three standard deviations from the mean. Your diagram should show: A correctly labeled normal distribution should be symmetrical around the population mean. Problem Set 3.2: Effect Size and Statistical Power Criterion: Explain effect size and power. Effect size describes the magnitude of a difference or relationship, while statistical power refers to the probability that a statistical test will detect an effect when a true effect exists. In general, larger effect sizes and larger sample sizes increase statistical power. Smaller variability can also increase the ability to detect an effect. Read each scenario and determine which researcher has greater statistical power, assuming all other factors remain equal. Scenario 1: Effect Size Two researchers conduct studies examining the effectiveness of a drug-use treatment. Researcher A finds an effect size of d = 0.36 among males, whereas Researcher B finds an effect size of d = 0.20 among females. Which researcher has greater power to detect an effect? Explain your answer. Answer: __________________________ The researcher with the larger effect size is expected to have greater statistical power when all other factors are equal because larger effects are generally easier for a statistical test to detect. Scenario 2: Sample Size Two researchers investigate marital satisfaction among military families. Researcher A studies 22 married couples (n = 22), while Researcher B studies 40 married couples (n = 40). Which researcher has greater power to detect an effect? Explain your answer. Answer: __________________________ When other factors are equal, the researcher using the larger sample is expected to have greater statistical power because larger samples generally provide more precise estimates and reduce sampling variability. Scenario 3: Population Standard Deviation Two researchers examine standardized examination performance among senior high school students in two local communities. Researcher A studies students from a northern community where the population standard deviation is σ = 110. Researcher B studies students from a southern community where the population standard deviation is σ = 60. Which researcher has greater power to detect an effect? Explain your answer. Answer: __________________________ When all other factors are equal, the researcher working with the smaller standard deviation is generally expected to have greater power because lower variability makes differences easier to detect. Problem Set 3.3: Directional and Nondirectional Hypothesis Testing Criterion: Explain the relationship between hypotheses, statistical tests, and the population mean. Hypothesis testing allows researchers to compare competing explanations about a population. A directional hypothesis predicts the direction of a difference, while a nondirectional hypothesis predicts that a difference exists without specifying which group will have the higher value. Cho and Abe (2013) discussed the appropriate use of one-tailed and two-tailed tests in behavioral research. Consider the following hypotheses for a study examining whether males self-disclose more than females: H₀: μmales − μfemales ≤ 0 H₁: μmales − μfemales > 0 Is this a directional or nondirectional test? Answer: __________________________ Explain why the hypotheses represent a directional or nondirectional test. Do these hypotheses account for all possible values of the population mean difference? Answer: __________________________ Explain how the null and alternative hypotheses divide the possible outcomes for the population mean difference. Problem Set 3.4: Understanding p Values and Statistical Decisions Criterion: Explain decisions based on p values. A p value helps researchers determine whether the observed sample result provides sufficient evidence against the null hypothesis. The significance level, commonly represented by α, is established before conducting the statistical test. Lambdin (2012) emphasized the importance of applying significance criteria consistently, stating, “If a p < .05 result is ‘significant,’ then a p = .067 result is not ‘marginally significant’” (p. 76). Explain what this statement means in terms of the two possible statistical decisions a researcher can make. Answer: __________________________ Your explanation should distinguish between: At α = .05, a p value below .05 generally provides sufficient evidence to reject the null hypothesis. A p value of .067 does not meet that predetermined significance criterion, so the researcher would fail to reject the null hypothesis. Problem Set 3.5: One-Sample t Test in JASP Criterion: Calculate and interpret a one-sample t test in JASP. A one-sample t test determines whether the mean of a sample differs significantly from a known or hypothesized population mean. Use the minutesreading.jasp dataset. The dataset contains the reading

Student Name Capella University PSYC FPX 4700 Statistics for the Behavioral Sciences Prof. Name Date PSYC FPX 4700 Assessment 2: Central Tendency and Probability Central tendency, variability, probability, conditional probability, and normal distributions are important concepts in psychological research because they help researchers summarize data and understand the likelihood of different outcomes. In this assessment, you will calculate and interpret measures such as the mean, median, mode, range, variance, and standard deviation. You will also apply probability formulas, interpret normally distributed data, and use Excel and JASP to analyze statistical information. Complete all problems in this Word document. Do not submit additional files unless specifically instructed. For problems that require calculations, show your work clearly. Make sure each final answer is easy to identify. You may highlight your answers or use a different font color to distinguish them from your calculations and explanations. Problem Set 2.1: Characteristics of the Mean Criterion: Describe a distribution. Read the research scenario below and use the data provided to answer the questions. A researcher is studying how people perceive differences in weight. A sample of 12 participants is asked to hold pairs of objects that differ in weight but are the same size, with one object placed in each hand. Participants report when they first notice that one object is heavier than the other. The differences in weight, measured in pounds, are: 4, 8, 9, 5, 12, 7, 6, 15, 10, 4, 8, 8 Use these data to calculate and identify the following measures of central tendency: Based on the relationship among the mean, median, and mode, describe the shape of the distribution. Explain how these three measures help you determine whether the distribution is approximately symmetrical, positively skewed, or negatively skewed. Problem Set 2.2a: Interpreting Means in a Chart Criterion: Interpret means in a chart. General life satisfaction can vary across cultures and demographic groups. Gilman and colleagues (2008) examined general life satisfaction among 1,338 adolescents from two individualistic nations, Ireland and the United States, and two collectivist nations, China and South Korea. Participants completed the Multidimensional Students’ Life Satisfaction Scale (MSLSS). Higher mean scores indicate greater reported life satisfaction. The mean MSLSS scores are presented below: Nation Men Women United States 4.39 4.61 Ireland 4.37 4.64 China 4.41 4.56 South Korea 3.92 3.78 Answer the following questions by comparing the mean scores in the table. Among which group was general life satisfaction lowest on average? ______ Among which group was general life satisfaction highest on average? ______ Briefly explain how you identified the highest and lowest groups using the mean scores. Problem Set 2.2b: Understanding Standard Deviations in a Chart Criterion: Interpret standard deviations in a chart. Standard deviation describes how much individual scores vary around the mean. A larger standard deviation indicates greater variability, while a smaller standard deviation indicates that scores tend to be more closely grouped around the average. Salska and colleagues (2008) investigated height preferences among dating partners. In their first study, the researchers reviewed Yahoo! Personals profiles from heterosexual individuals living within 250 miles of Los Angeles, California. They recorded participants’ acceptable ranges for the height of potential dating partners. The results are summarized below: Height Preference Women: M Women: SD Men: M Men: SD Shortest acceptable height, inches 68.9 2.6 60.6 3.7 Tallest acceptable height, inches 75.3 2.2 69.8 2.7 Question: Overall, did men or women show greater variability in their responses? Explain your answer by comparing the standard deviations for men and women. Remember that the group with the larger standard deviations demonstrated greater variability in height preferences. Problem Set 2.3: Range, Variance, and Standard Deviation in Excel Criterion: Calculate measures of variability in Excel from a group of raw scores. Use the following sample data representing the number of likes per Facebook post: 45, 789, 16, 5, 486, 1, 87, 18, 48, 1 Use Microsoft Excel to calculate descriptive statistics, including the range, mean, variance, and standard deviation. First, make sure the Data Analysis ToolPak is installed in Excel. If you need assistance installing the ToolPak, use Microsoft’s instructions for loading the Analysis ToolPak in Excel. Enter the data into a new Excel worksheet. In cell A1, enter Data. Enter the ten scores in cells A2 through A11. Next, select Data Analysis from the Data toolbar and choose Descriptive Statistics. Select OK and enter the following input range: $A$2:$A$11 Make sure Summary statistics is selected before choosing OK. Excel will create a new worksheet containing the descriptive statistics. Copy the descriptive statistics table into the appropriate section of the Word document. Clearly identify the following values: Highlight the range, mean, and standard deviation so that your final answers are easy to identify. Problem Set 2.4: Range, Variance, and Standard Deviation in JASP Criterion: Calculate measures of variability in JASP. Use the likes.jasp dataset for this activity. The dataset contains sample information about the number of likes per Facebook post. Download and open the likes.jasp file in JASP. Follow these steps to obtain the required descriptive statistics. Question: Does the mean calculated in JASP equal the mean calculated in Problem Set 2.3? Answer: ______ Briefly explain whether the results agree and why the same dataset should produce the same descriptive statistics when analyzed correctly in different statistical programs. Problem Set 2.5: Probability and Conditional Probability Criterion: Compute probability. Probability allows researchers to estimate how likely a particular event is to occur. In psychological and social science research, probability can be used to examine the likelihood of selecting individuals with particular characteristics from a population. For this activity, one student will be selected from a hypothetical population consisting of new and returning students who live either on campus or off campus. Use the student-population table provided with the assessment to calculate the requested probabilities. For simple probability, divide the number of individuals who meet the specified condition by the total number of individuals in the population. For conditional probability, use the relevant subgroup as the denominator. PSYC FPX 4700 Assessment 2 Central Tendency and Probability

Student Name Capella University PSYC FPX 4700 Statistics for the Behavioral Sciences Prof. Name Date PSYC FPX 4700 Assessment 1 Basics of Research and Statistics Frequency Distributions Percentiles and Graphical Representations Research and statistics provide the foundation for understanding how data are collected, organized, analyzed, and interpreted. This assessment focuses on essential statistical concepts, including independent and dependent variables, samples and populations, frequency distributions, percentages, percentiles, and graphical representations. It also introduces JASP, a statistical software program that can be used to organize data and create frequency tables and graphs. Complete all problems in the provided Word document. Do not submit additional files unless specifically instructed. For questions that involve calculations, show your work so that the calculation process is easy to follow. Make sure each final answer is clearly visible. You may highlight your answers or use a different font color to distinguish them from the questions. Problem Set 1.1: Identifying Variables Criterion: Identify dependent and independent variables. Researchers use variables to examine relationships between different factors. An independent variable is the factor that is manipulated or used to predict changes, while a dependent variable is the outcome being measured. A quasi-independent variable is a naturally occurring characteristic or group difference that cannot be directly manipulated by the researcher. For each scenario below, identify the independent variable, quasi-independent variable when applicable, and dependent variable. Scenario 1: Cocaine Use and Impulsive Behavior A researcher investigates whether cocaine use increases impulsive behavior by comparing cocaine-dependent mice with mice that have no previous experience with cocaine. Independent Variable: __________ Quasi-Independent Variable: __________ Dependent Variable: __________ Scenario 2: Test Format and Student Performance A professor examines whether students perform differently when completing a multiple-choice test compared with a fill-in-the-blank test. Independent Variable: __________ Quasi-Independent Variable: __________ Dependent Variable: __________ Scenario 3: Parental Smoking and Children’s Attitudes A researcher examines whether parents’ smoking behavior influences their children’s attitudes toward smoking. Independent Variable: __________ Quasi-Independent Variable: __________ Dependent Variable: __________ Scenario 4: Political Affiliation and Moral Attitudes A social scientist investigates whether attitudes toward morality differ according to political affiliation, such as Democrat or Republican. Independent Variable: __________ Quasi-Independent Variable: __________ Dependent Variable: __________ Scenario 5: Culture and Beliefs About Dreams A cultural researcher examines whether people from different cultural backgrounds differ in their beliefs about whether dreams have meaning. Independent Variable: __________ Quasi-Independent Variable: __________ Dependent Variable: __________ Problem Set 1.2: Understanding Samples and Populations Criterion: Describe the relationship between a population and a sample. A population refers to the entire group that a researcher wants to understand, whereas a sample is a smaller group selected from that population. Researchers commonly use samples because studying an entire population may be impractical, expensive, or impossible. Height and Educational Attainment Szklarska et al. (2007) proposed that taller young men may be more likely to achieve higher levels of educational attainment than shorter individuals from similar social backgrounds. The researchers recruited 91,373 nineteen-year-old men to participate in the study. Question: Do these participants most likely represent a sample or a population? Explain your answer. Response: ______________________________________________________ Problem Set 1.3: Creating a Dataset for JASP Criterion: Enter and display data for use in JASP. JASP can be used to organize and analyze quantitative data. Before importing data into JASP, the dataset can be created in Microsoft Excel and saved as a CSV file. Five social media users spent the following number of minutes viewing Twitter: 15.21, 46.18, 12.45, 65.486, 26.852 Follow these steps to prepare the dataset: Problem Set 1.4a: Grouped and Ungrouped Data Criterion: Explain how different types of data are identified. Grouped data organize observations into categories, intervals, or other meaningful groups. Ungrouped data present individual observations without combining them into categories. Complete the table by identifying whether each example represents grouped or ungrouped data and explain your reasoning. Example Grouped or Ungrouped Why? The time, measured in seconds, that 100 children take to complete a cognitive skills game. __________ __________ The number of single mothers who have 1, 2, 3, or 4 children. __________ __________ The number of teenagers who have experimented with smoking, reported as yes or no. __________ __________ The ages, in years, of freshman students at a local college. __________ __________ Problem Set 1.4b: Descriptive and Inferential Statistics Criterion: Explain the identification of types of data. Descriptive statistics summarize and organize information collected from a sample or population. Inferential statistics use sample data to make estimates, predictions, or conclusions about a larger population. Gun Ownership in the United States Gallup polling data collected over several decades provide information about reported gun ownership in the United States. The following table shows the percentage of Americans who reported owning guns during five different years. Year Percentage 1972 43% 1982 42% 1992 48% 2002 40% 2012 43% Source: Gallup, Guns. Question 1: Are the percentages reported in this table examples of descriptive statistics or inferential statistics? Answer: __________ Question 2: Based on the percentages shown, how has reported gun ownership in the United States changed over the 40-year period? Answer: ______________________________________________________ Problem Set 1.5: Reading a Chart Criterion: Locate and interpret data presented in a chart. Charts and tables allow researchers to compare numerical information quickly. Carefully examine the participant characteristics below and answer the questions. Participant Characteristics Profession Type Count Token Count College Professor 24,541 878,261 Clinical Psychologist 23,617 751,188 Unknown 479 927 Total 1,630,376   Question 1: Do college professors or clinical psychologists in this sample speak more words overall, based on Token Count? Answer: __________ Question 2: Do college professors or clinical psychologists in this sample use more different words, based on Type Count? Answer: __________ Problem Set 1.6: Frequencies and Percentages Criterion: Identify the most appropriate frequency or percentage measure. Frequency distributions help researchers describe how often values or categories occur. Depending on the question, researchers may use frequency, relative frequency, relative percentage, cumulative frequency, cumulative relative frequency, or cumulative percentage. For each situation below, identify the most appropriate measure. If a cumulative distribution is appropriate, specify whether it should