WORKSHEETS

Welcome to the world of statistics in psychology! While the study of statistics can be intimidating, it is an essential skill for any psychology professional. Statistics are all around you and are integral to the activities you will engage in throughout your career. Whether you venture into research, clinical practice, or another data-driven field, understanding statistics is crucial.

In this course, you will learn about both descriptive and inferential statistics, which are fundamental tools in psychology. Whether youre aiming to become a therapist, researcher, sport psychologist, school psychologist, industrial-organizational psychologist, or any other professional where psychology plays a role, mastering these tools will help you make sense of data, uncover patterns, and make informed decisions that can significantly impact your work.

Imagine youre working as a school psychologist, and you need to understand the academic performance of students in your school to provide the best support. You could use a graph to visually display your data and descriptive statistics to succinctly summarize your data. Measures of central tendency allow you to summarize test scores with measures like the mean, median, and mode, giving you a clear picture of overall performance. Standard deviation and variance help you understand the spread of scores, indicating how consistent the performance is across the student body. Standard scores can help you identify students who need extra support as well as those who are ahead of their peers.

Statistics enable you to make informed decisions based on data. You might use them to assess an individual patient, evaluate your overall practices, or analyze trends in behavior. For instance, you may need to determine the impact of a specific treatment or evaluate the effectiveness of a new therapy.

To get started, you are encouraged to download JASP early. This will allow you plenty of time to reach out to IT support if you have any challenges. JASP is an open-source (i.e., free) statistical software developed by the University of Amsterdam. You will be using JASP throughout this course to perform statistical analyses, construct graphs, and complete your assessments. Its a powerful tool that will help you gain practical experience with the statistical concepts youll be learning.

In the first assessment, we will cover the basics of research design, including scales of measurement, reliability, validity, and experimental versus non-experimental design. Well also focus on descriptive statistics. Descriptive statistics include measures such as the mean, median, and mode, which help us identify central tendencies in our data. We will explore measures of variability, such as range, interquartile range, variance, and standard deviation, which provide insights into the spread and consistency of our data. Well also learn about skewness and kurtosis, which describe the shape of a distribution, and standard scores, which describe the location of a score in relation to all other scores. By mastering these concepts, you will be able to effectively summarize and interpret data, making it easier to identify patterns and trends.

Overview

In the first assessment, you will apply research methods from Chapters 1 and 2 to analyze a given scenario. You will explore the application of statistics in a graduate-level psychology program or a career related to psychology. Additionally, you will demonstrate proficiency in using JASP to compute descriptive statistics and summarize key findings in a sentence following APA style guidelines.

PreparationInstructions

Complete and submit the Assessment 1 worksheet.

There are several questions that ask you to include a screenshot. This should be a screenshot taken directly from your computer. Photos taken with a phone or camera are not acceptable.

How you will take a screenshot will depend on your operating system.

For Mac Users

• On the keyboard, press Shift + Command () + 4.
• Your cursor will turn into a crosshair. Click and drag to select the area you want to capture.
• The screenshot will:
• Either be saved to your desktop (by default).
• Or be copied to your clipboard (if you also press Control key), which you can then paste directly into your assignment.

For Windows Users

• Press Windows Key + Shift + S to open the Snipping Tool.
• Select the area you want to capture.
• The screenshot will be copied to your clipboard. You can then paste it into your assignment.

If you’re unsure how to take a screenshot on your device, try searching online using the phrase: “How to take a screenshot on [your operating system]” (for example, “How to take a screenshot on Windows 11”).

If you’re still having difficulty, reach out to Capella’s .

Note: The assessments in this course build upon each other. You are strongly encouraged to complete them in sequence.

Our second assessment will cover data visualizations and begin exploring inferential statistics with sampling distributions and confidence intervals.

In the first half of this assessment, we will delve into various techniques for visually representing data, making it easier to understand and interpret. Data visualization is a crucial skill in statistics, as it allows us to identify patterns, trends, and outliers that might not be immediately apparent from raw data.

For psychology majors, data visualization is particularly vital. It enhances understanding, communication, and the ability to identify patterns and trends in complex data. Visualizations make data more accessible and easier to interpret, which is essential in psychology, where researchers often deal with large datasets or subtle differences between groups. Effective visualizations help communicate findings clearly and effectively to various audiences, including fellow researchers, practitioners, and the general public.

In the second half of this assessment, we will learn about sampling distributions and confidence intervals. This marks the beginning of our exploration of inferential statistical methods, which will be the focus for the remainder of the course. Inferential methods use data from a sample to make conclusions about the larger population.

Consider a scenario where a large university wants to understand the average number of hours students spend on social media per day. Since surveying all students isn’t feasible, they survey a representative sample of 100 students. Using the concepts of sampling distributions and confidence intervals, the researchers can estimate the average social media usage for the entire student body and determine the precision of their estimate. This information is crucial for developing programs to promote healthier social media habits among students.

Overview

For this assessment, you will demonstrate your ability to use JASP to construct graphs. You will also demonstrate your ability to interpret graphs. Finally, you will demonstrate your understanding of sampling distributions and confidence intervals.

Preparation

Before you begin this assessment, complete the following:

• Download the .
• Download the file. These data were extracted from the . In this file, data were limited to individuals between the ages of 30 and 39 who completed the survey in 2022.
• Download the file.

3Note: The assessments in this course build upon each other. You are strongly encouraged to complete them in sequence.

Our third assessment will cover hypothesis testing methods, including binomial, z, and t tests. We will explore the essential concept of hypothesis testing, a cornerstone of psychological research. Hypothesis testing allows us to make informed decisions based on data, helping us determine whether our observations are due to chance or reflect true effects. This skill is crucial for psychology students, as it underpins much of the research and evidence-based practice in the field.

You will learn how to formulate clear and testable hypotheses. We will cover the distinction between the null hypothesis (H0) and the alternative hypothesis (H1), and you’ll gain hands-on experience using JASP to conduct hypothesis tests for proportions and means.

Understanding and interpreting p values will be a key focus. The p value helps us determine the statistical significance of our results, guiding us in making decisions about whether to reject the null hypothesis. This process is fundamental in psychology, where we often seek to understand the effectiveness of interventions, the relationships between variables, and the prevalence of behaviors or conditions.

By the end of this assessment, you will be equipped with the skills to interpret the results of a hypothesis test and draw meaningful conclusions. These abilities are not only vital for your coursework but also for your future career in the social sciences. Hypothesis testing is the backbone of evidence-based practice, ensuring that the methods and interventions you use are scientifically validated and effective. Let’s dive into the fascinating world of hypothesis testing and enhance our understanding of psychological phenomena!

4Note: The assessments in this course build upon each other. You are strongly encouraged to complete them in sequence.

In the fourth assessment, we will explore correlation and regression. These are often students favorite topics.

While you will be introduced to various correlation coefficients, we will focus primarily on Pearsons r, which is the most commonly used correlation coefficient in psychological research. Pearson’s r measures the strength and direction of the linear relationship between two interval- or ratio-level variables.

For example, imagine you’re conducting a study to understand the relationship between stress levels and academic performance among college students. In one large course, you collect data on students’ self-reported stress levels and their scores on the most recent quiz. Earlier in the course, we learned that the relationship between these two variables could be visually displayed on a scatterplot. This week, well learn how to summarize the relationship between these two variables numerically using a correlation coefficient. If you find a significant negative correlation, it suggests that higher stress levels are associated with lower scores, providing valuable insights into how stress and academic performance are related.

After learning about correlation, youll delve into regression. Regression allows us to predict a dependent variable based on one or more independent variables, making it a frequently used tool in psychological research. Linear regression takes correlation a step further by not only determining if there is a relationship but also modeling it to make predictions. In essence, while correlation tells us about the strength and direction of the relationship between two variables, regression enables us to make predictions.

5.The assessments in this course build upon each other. You are strongly encouraged to complete them in sequence.

Welcome to the final assessment of PSYC-FPX3700! In this assessment, we will delve into categorical data analysis and analysis of variance (ANOVA) methods, both of which are frequently used in psychological research.

First, we will explore categorical analyses, focusing on two essential statistical tests: the chi-square goodness-of-fit test and the chi-square test of independence.

• Chi-square goodness-of-fit test: This test determines whether the observed frequencies of a single categorical variable differ significantly from expected frequencies. It helps us understand if a sample comes from a specific distribution. For example, a psychologist might investigate whether the distribution of personality types (e.g., introverted, extroverted, and ambiverted) among a sample of students from one university matches the expected distribution based on national statistics.
• Chi-square test of independence: This test assesses whether two categorical variables are related to each other. For instance, a psychologist might examine whether there is an association between the level of stress (categorized as low, moderate, and high) and the type of coping strategy an individual tends to use (such as problem-focused, emotion-focused, and avoidance). By applying the chi-square test of independence, researchers can determine if the observed relationship is statistically significant or if it occurred by chance.

Next, we will cover ANOVA. If you read a lot of quantitative research studies, you will encounter various types of ANOVA. We will start by exploring the basic logic behind ANOVA and how it works. ANOVA helps us test hypotheses about group differences by partitioning the total variability in the data into components attributable to different sources. You will learn how to run an ANOVA in JASP and interpret the output, which is similar to the output produced by other statistical software and the formatting seen in published research articles. You will also learn about post-hoc tests, conducted after finding a significant ANOVA result to identify which specific groups differ from each other, and the assumptions of ANOVA. At the end of the chapter, more advanced forms of ANOVA will be introduced, including repeated measures ANOVA, factorial ANOVA, analysis of covariance (ANCOVA), and multivariate analysis of variance (MANOVA).

Congratulations on making it to the final assessment! Statistics is often viewed as a challenging and anxiety-provoking subject, but youve made it this far. Keep up the great work!