Garth Neufeld, Cascadia College (Subcommittee Chair)
Samantha Estrada Aguilera, University of Texas at Tyler
Kelly Goedert, Seton Hall University
Janet Peters, Washington State University
V. N. Vimal Rao, University of Minnesota
Viji Sathy, University of North Carolina
Tamarah Smith, Gwynedd Mercy University
Jessica Hartnett, Gannon University (Committee Chair)
FROM THE OPENING PAGES OF THE FULL DOCUMENT:
On an annual basis, presidents of the Society for the Teaching of Psychology (STP) declare task forces related to personal areas of interest in teaching psychology. The current work was envisioned by 2021 President of STP, Dr. Susan Nolan, as a part of the Statistical Literacy, Reasoning, and Thinking: Guidelines 2.0 task force. It specifically looks at statistical literacy in the Introductory Psychology course.
In 2021, a team of volunteer task force members, representing diverse institutions, set out to make a recommendation for statistical literacy in the Introductory Psychology course. The result is this deliverable.
The purpose of this deliverable is to support Introductory Psychology instructors who desire to help their students learn essential outcomes for statistical literacy in an Introductory Psychology course. The guiding values of this work have led us to design easily implemented course assignments aligned with topics that are already being taught in the course.
Today, more than ever, students need skills to be able to decipher truth from fiction, and fact from opinion-based claims. One of the skills that psychologists can offer to help navigate this cognitive gauntlet is statistical literacy. Psychology is uniquely positioned to offer this to students, but most students will only ever take one psychology course, so the stakes are high. The introductory psychology curriculum offers this one-time opportunity for these future citizens, workers, and voters to develop a better understanding of real-world psychological topics including those related to statistical literacy.
Teachers of Introductory Psychology face an immense challenge in the course: there is so much content to teach, and they must choose it selectively with particular goals in mind. The APA IPI (Gurung & Neufeld, 2022) recommends that research methods and statistical literacy be taught as an essential part of Introductory Psychology. However, textbooks are generally set up to teach statistical literacy as a siloed chapter – the first chapter, to be certain – but a chapter to be covered and moved on from, nevertheless. Introductory Psychology instructors do not have the time or training to weave and scaffold research methods and statistical literacy throughout the course with intentionality, while simultaneously trying to teach the main content areas of psychology, let alone outcomes like APA’s Integrative Themes (Gurung & Neufeld, 2022). This current work is meant to provide instructors a solution by supporting both statistical literacy and Introductory Psychology content in a combined, strategic way.
The committee was also mindful of the variety of instructors who teach introduction to psychology: They can range from full professors to part-time adjuncts to first year graduate students. The materials are designed to be inclusive and useful to all introduction to psychology instructors, regardless of their own statistical training.
In 2014 an STP Presidential Task Force created a set of guidelines to help facilitate statistical literacy across the psychology curriculum. The task force was parsed into three sub-committees, including Statistical Literacy in the Introductory Psychology Course, Statistical Literacy in the Undergraduate Psychology Curriculum, and Statistical Literacy in Psychology: Resources, Activities, and Assessment Methods. The findings of the Introductory Psychology sub-committee (Nolan et al., 2014) can be found here.
This work is a continuation of the prior STP task force that recommended outcomes related to statistical literacy in the Introductory Psychology course. The current task force first developed values to guide the work and then differentiated essential and advanced outcomes at the introductory psychology level. Targeting only the essential outcomes, we set out to create a practical, meaningful, and helpful resource that would increase statistical literacy in Introductory Psychology students without burdening instructors. By offering formative assessments that infuse statistical literacy into course content instructors are already teaching, we believe we have accomplished our goal.
The essential outcomes recommendation for teaching statistical literacy in an Introductory Psychology course are listed below, in three categories: Essential Scientific Understanding, Experiments, and Correlations. This reduces the 26 former recommended outcomes from the 2014 STP Taskforce, to eight essential outcomes. One additional essential outcome (i.e., “Explain findings presented in simple graphs”) was also included by this taskforce, borrowed from APA Guidelines for the Undergraduate Psychology Major 2.0 (2013). It is recommended that Introductory Psychology instructors (and their students) achieve all of the essential-level outcomes in an Introductory Psychology course. By using the formative assessments that we have included in Part C, these essential outcomes should be achieved.
I. Essential Scientific Understanding
1. Explain why psychology is an empirical science
2. Explain the importance of replication in research
3. Distinguish between experimental and correlational research
4. Identify limitations, based on the sample used, on the generalizability of the findings of a study
5. Explain findings presented in simple graphs
6. Identify independent variables and their levels
7. Identify dependent variables
8. Interpret correlation coefficients (i.e., strength and direction of relationship)
9. Provide examples to illustrate the concept that correlation does not imply causation.
A final note: Outcome II. Experiments and Outcome III. Correlations are not intended to stand alone, but should be achieved along with I. Essential Scientific Understanding, so that students will understand the contexts and complexities associated with them.
The classroom activities provided in Part C of this document follow best practices for teaching statistics. This includes teaching statistics in an applied manner using problem-based approaches with real data (GAISE, 2016). The goal is to deepen students’ knowledge about statistics. However, one thing the activities do not do is provide the basic background information on statistics that students will initially need to complete the activities. The information provided in this section is designed to help instructors fill that background knowledge gap in a way that will prepare students for the specific tasks in each activity.
Each of the key concepts included in the foundational objectives are summarized below and ways to emphasize them to students in Introductory Psychology are discussed. This information is meant to complement the research methods instruction done in the beginning chapters of Introductory Psychology textbooks. The focus is not on teaching calculations or in-depth knowledge on the topics; rather, it provides the essentials that will allow students to complete the activities in Part C, and it is by completing those activities that their knowledge regarding each concept will deepen. We recommend that instructors who have an interest in more details about teaching statistics refer to the GAISE: Guidelines for Assessment and Instruction in Statistics Education.
It is important for students to know first and foremost that psychology is a science. It may be that many of our students think of psychology as an intuitive practice--some people are just good at understanding other people’s problems and giving advice. But, we know that psychology is a scientific approach to understanding and supporting an individual's thoughts, feelings and behaviors.
The first step in introducing students to psychology as a science should be just that: explaining that it is a science. This should go beyond a presentation of the scientific method and instead help students to understand that science is based on the systematic observation of something in the most unbiased way possible. Emphasize the role of reducing bias in science and help students understand that science is not just a ‘gut feeling’ or opinion, but a conclusion based on objective data.
A key tenet of science is replication. If results cannot be replicated, we cannot trust them. When describing psychology as a science, help students understand the importance of this. You could communicate this at the individual level--we don’t just test something on one person, we make sure it works with many people; and at the study level--we don’t just test something in one research study, we make sure it works every time we study it. The ‘something’ in your examples can include anything from medicine, to therapeutic approaches, or even recommended study skills.
Experimental and Correlational Designs
Students confuse experiments and correlational studies. It may be that our colloquial use of the word ‘experiment’ is to blame here. When talking about research design, do emphasize the following:
● A true experiment begins with randomly assigning participants to one of two groups. The purpose of random assignment should be understood by students: to create equivalent groups.
● The word ‘experiment’ is reserved for the scenario when two groups are compared after experimenting with one of the groups and keeping everything constant with, or controlling, the other group. If it’s not a true experiment, it’s probably a correlational design.
● A true experiment with an experimental group and control group is the ONLY way to determine if one thing causes another. The word “cause” should ONLY be used if an experiment has been done.
● Correlational studies look at relationships specifically when there has been no control group to compare with an experimental group.
● Not only does the term ‘correlation’ refer to a study design (measuring relationship without an experimental group), but it also refers to a type of statistical test. These statistical tests help to measure the direction and strength of the relationship between the two things under investigation.
● Teach students what a positive correlation is, what a negative correlation is (i.e., direction) and the standard values for a small, moderate and large correlation (i.e., strength). Because correlations are not experimental designs we can conclude that finding a correlation between two things does not mean that one thing causes another. Only a true experiment can determine causation. Provide examples of this error in popular media sources.
Samples & Generalizability
When talking with students about sampling make sure they understand that one of the main reasons why we care about sampling is generalizability. Tie generalizability back to bias. When introducing psychology as a science, we note that science works to observe the world objectively. We cannot be objective if the samples we use to generate research data are chosen in a biased way. If we choose our friends to be our research sample, we would not be able to generalize the findings from them to other groups who are different from them. Therefore, we need to choose samples that are broader and give everyone a chance of being included. This is what is meant by random sampling; everyone has the same chance of being included. And, when everyone is represented in the sample we can then generalize the findings from that smaller group back to the whole population of people. In this case the sample will have less bias and therefore the data will be more trustworthy.
Independent and Dependent Variables
Students will need to know that learning the difference between an independent variable (IV) and dependent variable (DV) takes repetition. Give a formal definition of both types of variables, but more importantly give many many examples to illustrate that 1) an IV can be manipulated or not, and 2) DVs are dependent on the independent variables. Below is an example of a narrative that explains IVs and DVs making sure to highlight these key characteristics. Instructors can exchange their own examples of IVs and DVs in this narrative to customize for their course, or use it as is.
IV: This is a variable that we can manipulate when doing an experiment, for example the amount of a medication someone is given (e.g., 0mg, 10mg, or 20mg). In this example, there are three ‘levels’: 0mg, 10mg, and 20mg. The first level, 0mg, is the amount that a control group would receive.
If we are not doing an experiment, then the IV is not considered “manipulated”. It might be measured based on its naturally occurring groups (e.g., first-year, sophomore, junior, senior) or not grouped at all, such as with a person's age (18, 19, 20, 21, 22…).
DV: The dependent variable depends on the independent variable. For example, anxiety levels might depend on the amount of anti-anxiety drug taken (e.g., 0mg, 10mg, or 20mg). If we find that anxiety levels do vary based on the amount of medication taken, we may be able to generalize the findings to people outside of the study.
Students will need to know some basic descriptive statistics and how to read simple common graphs used in psychological research. Descriptive statistics would include sample size, mean, standard deviation, minimum, maximum, and confidence intervals. Do not emphasize how to calculate these values, but instead focus on what they mean. Here are some examples of how to explain some of these descriptive statistics to students:
● Sample size: This is the number of people that participate in a study from whom data are collected. A common symbol for sample size is “N” but you may also see some graphs use “Frequency” to indicate a specific number of people in the sample.
● Mean: The mean is the mathematical average. Researchers will use “M” to symbolize when they are discussing the mean. Oftentimes, when researchers are talking about the average, they use the word “mean” in its place. For example, instead of saying, the average height of the participants was 5’8”, they may say the mean height of the participants was 5’8”.
● Standard Deviation: When you gather data from a group of participants, you can calculate the mean. For example, you might be calculating the mean GPA for a group of students. But not every student in the group, maybe no one in the group will have a GPA that is the same as the mean GPA for that group. The standard deviation, or SD, is a number that tells us how much the scores for the individuals in the sample differed from the mean. For example, you might find that the mean GPA for a sample of students is M=3.45 with a SD=.23. This tells us that the group as a whole had an average GPA of 3.45. Certainly, not every student would have a GPA of 3.45. Their GPAs would vary. The standard deviation, SD, tells us that students who did not have a GPA of 3.45 would typically deviate from this by .23 points.
● Confidence Intervals: Confidence intervals are typically symbolized with “CI” and in psychology we often look at the 95% CI. The CI has two values: the lower CI and the upper CI. These values provide an estimate of what mean score we would expect in 95% of cases if we repeated a study. For example, the mean GPA for students in this class will not be identical to the mean GPA for students in another section of Introductory Psychology. They may be similar, but not identical. Confidence intervals use the data from this class to help estimate a range for what we might expect the mean to be with a different sample. If the lower 95% CI for GPA is 3.36 and the upper 95% CI for GPA is 3.54, then we could expect another class similar to this one to have a mean GPA between 3.36 and 3.45 most of the time.
Show students examples of common simple graphs including: bar charts, histograms, pie charts and line charts. Point out to students that the axes of graphs typically show values for a particular variable, or concept being measured, and sometimes show the frequency of people for a given value of a variable. Remember that students may not be familiar with concepts such as “axes” and “variables”, so do explain what each of these are.
In a typical 16-chapter Introductory Psychology textbook, chapters 2 through 16 are content areas (Personality, Social, Bio, etc.). Each of these fundamental chapters has a corresponding formative assessment that will enhance the essential outcomes of statistical literacy in an Introductory Psychology course. But the first chapter on Research Methods, Psychological Science, and Statistics is where essential outcomes should be first introduced and taught. And so, before instructors jump into the formative assessments, we suggest that they build a firm foundation for further learning. Part B helps instructors assess whether the content of their initial lessons on statistical literacy in Introductory Psychology are comprehensive with regards to essential outcomes.
Please note that these formative assessments include three parts: 1) an instructor-facing cover sheet, 2) a student-facing assignment, and 3) an answer key. The assignment is categorized according to the APA Pillar Model (APA, 2021). In the Strengthening the Common Core of the Introductory Psychology Course (APA, 2014) document that originally introduced this model, it was recommended that Introductory Psychology instructors teach two areas from each of the five domains (i.e., Pillars).
These activities represent some of the areas of psychology that an instructor would find in a typical Introductory Psychology textbook. Scroll down or click the links below to navigate to each activity.
American Psychological Association. (2013). APA guidelines for the undergraduate psychology major: Version 2.0.
American Psychological Association. (2014). Strengthening the common core of the introductory psychology course. Washington, DC: American Psychological Association, Board of Educational Affairs.
American Psychological Association. (2021). The APA introductory psychology initiative.
GAISE College Report ASA Revision Committee. (2016). Guidelines for assessment and instruction in statistics education college report 2016.
Gurung, R. A.R., & Neufeld, G. (Eds.) (2022) Transforming introductory psychology: Expert advice on teacher training, course design, and student success. Washington, DC: American Psychological Association.
Nolan, S. A., Bramesfeld, K., Freeman, J., Grosofsky, A., Moore, B., Scott-Johnson, P., & Wyner, E. (2014). Statistical Literacy in the Introductory Psychology Course: Society for the Teaching of Psychology 2012 Presidential Statistical Literacy Task Force Report. Office of Teaching Resources in Psychology, Society for the Teaching of Psychology.