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The Effects Of Sleep Deprivation On Online University Students' Performance , Maureen Cort-Blackson Walden University

The Effects Of Sleep Deprivation On Online University Students' Performance , Maureen Cort-Blackson

Walden dissertations and doctoral studies.

Sleep deprivation affects the academic performance of online university students, and students who have family responsibilities and a full-time job have a higher prevalence of sleep deprivation. This phenomenological study examined the lived experiences of online university students regarding sleep patterns, sleep deprivation, and the impact on their academic performance. The theoretical foundation for this study was based on the opponent processing model that explains the 2 fundamental processes necessary for individuals to function at their optimum ability: the sleep-wake homeostatic process and the circadian rhythm processes. The research question explored the beliefs and perceptions of 10 online university students, …

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Happiness Index Methodology , Laura Musikanski, Scott Cloutier, Erica Bejarano, Davi Briggs, Julia Colbert, Gracie Strasser, Steven Russell Happiness Alliance

Happiness Index Methodology , Laura Musikanski, Scott Cloutier, Erica Bejarano, Davi Briggs, Julia Colbert, Gracie Strasser, Steven Russell

Journal of sustainable social change.

The Happiness Index is a comprehensive survey instrument that assesses happiness, well-being, and aspects of sustainability and resilience. The Happiness Alliance developed the Happiness Index to provide a survey instrument to community organizers, researchers, and others seeking to use a subjective well-being index and data. It is the only instrument of its kind freely available worldwide and translated into over ten languages. This instrument can be used to measure satisfaction with life and the conditions of life. It can also be used to define income inequality, trust in government, sense of community and other aspects of well-being within specific demographics …

Understanding Of Self-Confidence In High School Students , George Ballane Walden University

Understanding Of Self-Confidence In High School Students , George Ballane

Students at a private high school in New Jersey exhibited low academic self-confidence as compared to other indicators on the ACT Engage exam. The purpose of this qualitative case study was to gain an understanding of academic self-confidence, academic performance, and learning within a sample of students. This research explored students' and teachers' perceptions of self-confidence and their impact on academic performance. The research was guided by Weiner's attribution and Bandura's self-efficacy theories. The research questions focused on 3 areas: students' and teachers' perceptions of academic self-confidence as factors impacting students' academic performance; and the perceived relationship between academic self-confidence, …

An Introduction To The Analysis Of Ranked Response Data , Holmes Finch University of Massachusetts Amherst

An Introduction To The Analysis Of Ranked Response Data , Holmes Finch

Practical assessment, research, and evaluation.

Researchers in many disciplines work with ranking data. This data type is unique in that it is often deterministic in nature (the ranks of items k -1 determine the rank of item k ), and the difference in a pair of rank scores separated by k units is equivalent regardless of the actual values of the two ranks in the pair. Given its unique qualities, there are specific statistical analyses and models designed for use with ranking data. The purpose of this manuscript is to demonstrate a strategy for analyzing ranking data from sample description through the modeling of relative …

Apr Financial Stress Scale: Development And Validation Of A Multidimensional Measurement , Wookjae Heo, Soo Hyun Cho, Philseok Lee South Dakota State University

Apr Financial Stress Scale: Development And Validation Of A Multidimensional Measurement , Wookjae Heo, Soo Hyun Cho, Philseok Lee

Journal of financial therapy.

People usually experience financial stress in managing their financial resources. Despite financial stress’s importance in life outcomes and the need for a comprehensive and theory-based measurement of the construct, few studies have addressed the conceptual issues of financial stress and its measurement. Hence, by borrowing from theories of general stress, this study attempts to fill this gap. Using an expert panel and two separate online survey samples, we developed and validated a novel financial stress scale. A total of 688 responses were used in an exploratory factor analysis and 1,115 responses were used in a confirmatory factor analysis. This multidimensional …

Negative Social Media And Its Influence On Athlete's Performance , Bernd R. Huber Cal Poly Humboldt

Negative Social Media And Its Influence On Athlete's Performance , Bernd R. Huber

Cal poly humboldt theses and projects.

This study aimed to investigate the potential impact of negative social media content on athletes' cortisol levels and subsequent performance. The study focused on the change in cortisol levels and differences in free throw performance, based on previous research findings. We hypothesized that negative social media postings would increase the stress experienced by student-athletes, resulting in elevated cortisol levels and decreased performance. Additionally, participants ( n = 8) completed a questionnaire to examine the interaction between preexisting fear and the biological stress response. Contrary to expectations, there was no significant change in stress response, and negative postings did not have …

Leadership's Impact On Employee Work Motivation And Performance , Tonia Marilu Joseph-Armstrong Walden University

Leadership's Impact On Employee Work Motivation And Performance , Tonia Marilu Joseph-Armstrong

AbstractLeadership is a major factor in terms of motivating employees, leading to enhanced performance. A study was conducted to examine the influence of supervisory leadership style on employee work motivation and job performance in organizations, specifically in a K-12 school setting. The main goal was to determine if there is a relationship between type of leadership demonstrated by school administrators and its impact on the teaching staff’s motivation and performance. Data for this quantitative study were gathered and analyzed from various public and private schools and included a sample of 100 participants. The predictor variable leadership was assessed using the …

Student Engagement Scale: Development And The Underlying Factor Structure , Patricia Isabel Quiñones Pareja California State University, San Bernardino

Student Engagement Scale: Development And The Underlying Factor Structure , Patricia Isabel QuiñOnes Pareja

Theses digitization project.

The purpose of this thesis show the strong effects attributed to student engagement on a range of educational issues, the need for a scale to measure the construct of student engagement is great.

Factors Impacting Parental Acceptance Of An Lgbt Child , Dani E. Rosenkrantz University of Kentucky

Factors Impacting Parental Acceptance Of An Lgbt Child , Dani E. Rosenkrantz

Theses and dissertations--educational, school, and counseling psychology.

Chrisler’s (2017) Theoretical Framework of Parental Reactions When a Child Comes Out as Lesbian, Gay, or Bisexual suggests that parental reactions to having a non-heteronormative child are impacted by a process of cognitively appraising information about their child’s identity and experiencing and coping with emotional responses, both of which are influenced by contextual factors such as a parent’s value system. However, some religious values can challenge parents in the process of accepting a lesbian, gay, bisexual, or transgender (LGBT) child. The purpose of this study was to test a model that examines the influence of cognitive-affective factors (cognitive flexibility, emotional …

The Relationship Between Parenting Style And The Level Of Emotional Intelligence In Preschool-Aged Children , Giselle Farrell Philadelphia College of Osteopathic Medicine

The Relationship Between Parenting Style And The Level Of Emotional Intelligence In Preschool-Aged Children , Giselle Farrell

Pcom psychology dissertations.

The purpose of this study is to examine the relationship between parenting style and the level of emotional intelligence in preschool-aged children. The sample consisted of eighty parent participants of preschool-aged children between the ages of 3 and 6 years old. Participants completed the Parenting Styles and Dimensions Questionnaire (PSDQ) in order to assess their views on behaviors that parents typically demonstrate towards their children. Based on each participant’s responses on the PSDQ they were determined to favor one of the following three parenting styles: authoritarian, authoritative, or permissive. Participants also completed the Children’s Behavior Questionnaire- Very Short Form (CBQ-VSF) …

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The Application Of Bayesian Meta-Analytic Models In Cognitive Research On Neurodevelopmental Disorders , Nic Zapparrata 2024 The Graduate Center, City University of New York

The Application Of Bayesian Meta-Analytic Models In Cognitive Research On Neurodevelopmental Disorders , Nic Zapparrata

Dissertations, theses, and capstone projects.

Meta-analysis is the systematic review and quantitative synthesis of specific areas in literature and is an important quantitative tool for researchers interested in synthesizing a particular body of research. The current research used meta-analysis to investigate processing speed in two neurodevelopmental disorders. This dissertation consisted of four meta-analytic papers. The first paper was a meta-analysis that synthesized a large body of research on processing speed, measured via reaction time (RT) measures, in groups of individuals diagnosed with autism spectrum disorder (ASD) versus age-matched neurotypical comparison groups. This research was motivated by two previous meta-analyses in the literature on processing speed …

Embodied Co-Regulation: A Neuroregulatory-Informed Dance/Movement Therapy Transition Intervention Method For Arousal Regulation For Adolescents In A Partial Hospitalization Program , ANAMARIA GUZMAN 2024 Lesley University

Embodied Co-Regulation: A Neuroregulatory-Informed Dance/Movement Therapy Transition Intervention Method For Arousal Regulation For Adolescents In A Partial Hospitalization Program , Anamaria Guzman

Expressive therapies capstone theses.

This thesis introduces a novel Dance/Movement Therapy (DMT) approach, focusing on nervous system arousal regulation during transitions between therapy groups. The core of the method involves a brief 5-minute exercise designed to modulate arousal levels, encompassing alertness and energy, aiming to establish a baseline homeostasis. Rooted in Polyvagal Theory and Developmental Neurobiology, the approach assumes the co-regulation of nervous systems within a group therapeutic setting. Two primary outcomes are self-assessed: 1) somatic experiences documented through narratives and 2) nervous system biodata measured using the Flowtime headband monitoring of brainwaves, heart rate, and other biomarkers. Results indicated that all six sessions …

An Investigation Of The Effectiveness Of Student’S T-Test Under Heterogeneity Of Variance , Hayden Nelson 2024 Western Kentucky University

An Investigation Of The Effectiveness Of Student’S T-Test Under Heterogeneity Of Variance , Hayden Nelson

Masters theses & specialist projects.

Within the field of psychology, few tests have been as thoroughly investigated as Student’s t-test. One area of criticism is the use of the test when the assumption for heterogeneity of variance between two samples is violated, such as when sample sizes and observed sample variances are unequal. The current study proposes a Monte Carlo analysis to observe a broad range of conditions in efforts to identify the resulting fluctuations in the proportion obtained significant results for two conditions: no mean difference (𝜇􀬵 = 𝜇􀬶) compared to the set level of alpha, and small-to-moderate mean differences (𝜇􀬵 ≠ 𝜇􀬶) compared …

Effects Of Emotional Intelligence And Social Support On The Relationship Between Childhood Maltreatment And Disordered Eating , Rachel Kilby 2024 University of Northern Colorado

Effects Of Emotional Intelligence And Social Support On The Relationship Between Childhood Maltreatment And Disordered Eating , Rachel Kilby

Undergraduate honors theses.

Current research has established a connection between childhood maltreatment and eating disorders, and some studies have looked at emotional intelligence or social support as mediators. However, little research has looked at how emotional intelligence and social support work together in the relationship between childhood maltreatment and eating disorders. This study looked at how emotional intelligence and social support act as mediators in this relationship. Undergraduate students (N=134) were administered the Childhood Trauma Questionnaire (CTQ-90), Wong-Law Emotional Intelligence Scale (WLEIS), Difficulties in Emotion Regulation Scale (DERS), Perceived Social Support Scale (PSSS), and the Eating Attitudes Test (EAT-26). Correlations between scales were …

Investigating Bimanual Haptic Exploration With Arm-Support Exoskeletons , Balagopal Raveendranath 2024 Clemson University

Investigating Bimanual Haptic Exploration With Arm-Support Exoskeletons , Balagopal Raveendranath

All dissertations.

The ability to judge properties like weight and length of hand-held objects is essential in industrial work. Sometimes workers use devices like exoskeletons, which can augment their ability to lift and move heavy objects. Previous studies have investigated the perceptual information available for one-handed weight and length judgments. The current study investigated how blindfolded participants bimanually heft and wield objects to explore haptic information, to perceive object heaviness or length. The study also investigated the effects of using an arm-support exoskeleton (ASE) on the perceived weight of hand-held objects. We empirically tested whether people wield and manipulate objects differently, depending …

Praise In The Collegiate Classroom: How Narcissism, Entitlement, Empathy, And A Desire For Fame Impact Student’S Preferences For Praise , Alexandra Crissman 2024 University of Lynchburg

Praise In The Collegiate Classroom: How Narcissism, Entitlement, Empathy, And A Desire For Fame Impact Student’S Preferences For Praise , Alexandra Crissman

Student scholar showcase.

The purpose of this study was to examine the relationship between personality traits, namely narcissism, entitlement, empathy, and a desire for fame, and a preference for public praise in a student population. Past research has shown that those who are higher in narcissism are also higher in entitlement and that these rates have been rising with future generations. Past research has also indicated a negative relationship between narcissistic tendencies and empathy, inferring that those higher in narcissism tend to score lower on empathy measures. In this study, undergraduate students were surveyed online to determine scores on four measures of traits …

Buffering Effects Of Negative Intergroup Contact Through Complex Social Identities , Liora Morhayim 2024 University of Massachusetts Amherst

Buffering Effects Of Negative Intergroup Contact Through Complex Social Identities , Liora Morhayim

Masters theses.

Although negative intergroup contact occurs less frequently than positive contact, negative contact can more strongly influence outgroup attitudes and behaviors due to the effect of category salience in the generalization process. The present study ( N =306) tests whether being aware of an outgroup member’s complex social identity will serve as a buffer against the adverse impact of a negative intergroup contact experience on outgroup attitudes. In a 3X2 between-subjects design, social identity complexity (SIC) of an outgroup confederate (high versus low versus control) and the valence of contact (neutral versus negative) were manipulated. Participants interacted with an outgroup confederate …

The Relationship Between Cognitive Impairment In Psychiatric Patients And Readmission Rate To An Inpatient Facility , Cherilyn Isis Schuff 2024 Florida Institute of Technology

The Relationship Between Cognitive Impairment In Psychiatric Patients And Readmission Rate To An Inpatient Facility , Cherilyn Isis Schuff

Theses and dissertations.

The primary intention of this study was to further understand the impact of assessing cognitive impairment in psychiatric patients, as a mediating factor on readmission rates. Mild cognitive dysfunction impacts a patient’s functional outcomes (Bowie & Harvey, 2006; Davis et al., 2012; Marcantonio, et al., 2001). Little information exists to guide best practices in the treatment of adults with cognitive impairment who are hospitalized for acute conditions (Davis et al., 2012). A cognitive impairment may impact patient prognosis and ability to function outside of a setting focused on stabilization. Neuropsychological testing is a valuable tool in predicting a patient’s cognitive …

Examining Differences In Self-Concept And Language Between Monolingual And Bilingual Undergraduate Students , Marilyn Vega-Wagner 2024 University of the Pacific

Examining Differences In Self-Concept And Language Between Monolingual And Bilingual Undergraduate Students , Marilyn Vega-Wagner

University of the pacific theses and dissertations.

The literature is lacking in studies that examine self-concept and language status among individuals older than adolescence. The purpose of this study is to conduct a quantitative nonexperimental comparative design to examine differences in self-concept and language status (monolingual or bilingual) between male and female undergraduate students in California. A total of 97 participants were examined in the study. The researcher conducted descriptive statistics on the demographics as well as a MANOVA and an ANOVA to answer the proposed research question. Based on the findings presented, the researcher failed to reject the null hypothesis of research question 1: There is …

The Effect Of Email Communication On Professor-Student Rapport, Academic Self-Efficacy, Resiliency, Motivation, And Spirituality , David J. Heim 2024 Missouri State University

The Effect Of Email Communication On Professor-Student Rapport, Academic Self-Efficacy, Resiliency, Motivation, And Spirituality , David J. Heim

Msu graduate theses.

Student retention and success rates are an increasing concern among collegiate administrators and educators. This study examined the influence of a college instructor’s email communications on professor-student rapport, student academic self-efficacy, resilience, motivation, and success. Researchers hypothesized that the student participants who received the encouraging email communications from their professor would demonstrate higher levels of professor-student rapport, higher levels of academic self-efficacy, resiliency, and success compared to the students who receive standard email communications from their professor. Five scales were utilized in this study including Professor-Student Rapport Scale, Academic Self-Efficacy Scale, Academic Resilience Scale (ARS-30), Daily Spiritual Experience Scale (DSES), …

Consistent Across Situations? A Person Specific Approach To Examining A Long-Standing Paradox. , Muchen Xi 2023 Washington University in St. Louis

Consistent Across Situations? A Person Specific Approach To Examining A Long-Standing Paradox. , Muchen Xi

Arts & sciences electronic theses and dissertations.

Bem and Allen (1974) address the person situation debate by proposing that there are some people behave more consistently, which can be better “explained” try personality traits, than others who are more influenced by situations. However, with failures to directly replicate Bem and Allen’s study, the existence of individual difference in cross situation consistency of behaviors remaining unclear. The current study addressed open questions that arose from the personality consistency debate by employing Mixed Effect Location Scale Model (MELSM) in an intensive longitudinal study. We found 1) there are individual difference in the overall behavioral consistency across situations; 2) there …

Detecting Careless Cases In Practice Tests , Steven Nydick 2023 Duolingo

Detecting Careless Cases In Practice Tests , Steven Nydick

Chinese/english journal of educational measurement and evaluation | 教育测量与评估双语期刊.

In this paper, we present a novel method for detecting careless responses in a low-stakes practice exam using machine learning models. Rather than classifying test-taker responses as careless based on model fit statistics or knowledge of truth, we built a model to predict significant changes in test scores between a practice test and an official test based on attributes of practice test items. We extracted features from practice test items using hypotheses about how careless test takers respond to items and cross-validated model performance to optimize out-of-sample predictions and reduce heteroscedasticity when predicting the closest official test. All analyses use …

Critical Transitions In Mental Health: Van Gogh Case Study , Anna Singley 2023 University of Portland

Critical Transitions In Mental Health: Van Gogh Case Study , Anna Singley

Annual symposium on biomathematics and ecology education and research.

No abstract provided.

The Effectiveness Of Computerized Neurofeedback As An Accompanying Or Alternative Therapeutic Intervention For Pharmacological Treatment In Improving Attention And Other Symptoms For Children With Attention Deficit Hyperactivity Disorder (Adhd) , Eqbal Z. Darandari PhD, Nouf F. Alsultan 2023 King Saud University

The Effectiveness Of Computerized Neurofeedback As An Accompanying Or Alternative Therapeutic Intervention For Pharmacological Treatment In Improving Attention And Other Symptoms For Children With Attention Deficit Hyperactivity Disorder (Adhd) , Eqbal Z. Darandari Phd, Nouf F. Alsultan

International journal for research in education.

This study aimed to investigate the effectiveness of a treatment program using computerized neuro-feedback in improving attention for children with attention deficit hyperactivity disorder (ADHD). To achieve the aim of the study, the computerized neurofeedback program was applied to (56) children diagnosed with (ADHD), aged between (7-11) years. They were distributed into four groups: the first group was subjected to combined intervention (neurofeedback & pharmacological treatment), the second group was subjected to (neurofeedback only), while the third group was exposed to the intervention using (pharmacological treatment only), and the fourth group was (not exposed to any intervention). Test of Variables …

A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr. 2023 University of South Florida

A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Usf tampa graduate theses and dissertations.

Large language models (LLMs) are poised to transform both academia and industry. But the excitement around these generative AIs has also been met with concern for the true extent of their capabilities. This dissertation helps to address these questions by examining the capabilities of LLMs using the tools of psychometrics. We focus on analyzing the capabilities of LLMs on the task of natural language inference (NLI), a foundational benchmark often used to evaluate new models. We demonstrate that LLMs can reliably predict the psychometric properties of NLI items were those items administered to humans. Through a series of experiments, we …

A New Method To Determine The Posterior Distribution Of Coefficient Alpha , John Mart V. DelosReyes 2023 Old Dominion University

A New Method To Determine The Posterior Distribution Of Coefficient Alpha , John Mart V. Delosreyes

Psychology theses & dissertations.

There is a focus within the behavioral/social sciences on non-physical, psychological constructs (i.e., constructs). These constructs are indirectly measured using measurement instruments that consist of questions that capture the manifestations of these constructs. The indirect nature of measuring constructs results in a need of ensuring that measurement instruments are reliable. The most popular statistic used to estimate reliability is coefficient alpha as it is easy to compute and has properties that make it desirable to use. Coefficient alpha’s popularity has resulted in a wide breadth of research into its qualities. Notably, research about coefficient alpha’s distribution has led to developments …

Examining The Relationship Between Counselor Professional Identity And Burnout , Jessica Gaul 2023 Antioch University Seattle

Examining The Relationship Between Counselor Professional Identity And Burnout , Jessica Gaul

Antioch university full-text dissertations & theses.

This study examines counselor professional identity and burnout for clinical mental health counselors. The population of focus included licensed or license-eligible Clinical Mental Health Counselors, who were post-grad (N=53). Participants then completed the Professional Identity Scale in Counseling - Short Form and the Maslach Burnout Inventory–Human Services Survey. When examining the findings regarding the relationship between Counselor Professional Identity and Burnout for this study, the initial observation revealed the validity and applicability of the MBI-HSS to clinical mental health counselors. Though a relationship between Burnout and Counselor Professional Identity was not identified, relationships between sub-scale items were noteworthy. Implications for …

Recentering Psych Stats , Lynette Bikos 2023 Seattle Pacific University

Recentering Psych Stats , Lynette Bikos

Faculty open access books.

To center a variable in regression means to set its value at zero and interpret all other values in relation to this reference point. Regarding race and gender, researchers often center male and White at zero. Further, it is typical that research vignettes in statistics textbooks are similarly seated in a White, Western (frequently U.S.), heteronormative, framework. ReCentering Psych Stats seeks provide statistics training for psychology students (undergraduate, graduate, and post-doctoral) in a socially and culturally responsive way. All lessons use the open-source statistics program, R (and its associated packages). Each lesson includes a chapter and screencasted lesson, features a …

Exploring The Dimensions And Dynamics Of Partnered Sexual Behaviours: Scale Development And Validation Using Factor And Network Analysis , Devinder S. Khera 2023 Western University

Exploring The Dimensions And Dynamics Of Partnered Sexual Behaviours: Scale Development And Validation Using Factor And Network Analysis , Devinder S. Khera

Electronic thesis and dissertation repository.

Sexual behaviours are an integral part of most intimate relationships and can serve as mechanisms for building intimacy, enhancing emotional connection, and can serve as non-verbal communication to express care, love, and compassion for significant others. Sexually compatible behaviours are also associated with sexual satisfaction – something especially important given the downstream consequences of sexual satisfaction on relationship satisfaction, relationship stability, and general well-being. However, to date, no inclusive, psychometrically validated measure of partnered sexual interests and behaviours exists. Given the central role of sexual interests and behaviours in sexual satisfaction and in turn relationship quality, we sought to develop …

Average Or Outlier? Introductory Statistics Adjunct Instructors’ Beliefs, Practices, And Experiences , Samantha Estrada Aguilera, Erica Martinez 2023 University of Texas at Tyler

Average Or Outlier? Introductory Statistics Adjunct Instructors’ Beliefs, Practices, And Experiences , Samantha Estrada Aguilera, Erica Martinez

The qualitative report.

In recent years, the adjunct faculty phenomenon has grown steadily. This research focused on adjunct instructors teaching introductory statistics courses. The purpose of the study was to give a voice to adjunct instructors by allowing them to describe their experiences teaching statistics. We conducted a qualitative study with 15 adjunct instructors of introductory statistics through semi-structured interviews. The participants came from several fields: psychology, nursing, and business, among others. Thematic analysis was used to find themes of statistical anxiety, use of technology in the classroom, lack of curriculum flexibility, and connection to the host institution. Our findings can inform institutions …

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Chapter 11: Presenting Your Research

Writing a Research Report in American Psychological Association (APA) Style

Learning Objectives

• Identify the major sections of an APA-style research report and the basic contents of each section.
• Plan and write an effective APA-style research report.

In this section, we look at how to write an APA-style empirical research report , an article that presents the results of one or more new studies. Recall that the standard sections of an empirical research report provide a kind of outline. Here we consider each of these sections in detail, including what information it contains, how that information is formatted and organized, and tips for writing each section. At the end of this section is a sample APA-style research report that illustrates many of these principles.

Sections of a Research Report

Title page and abstract.

An APA-style research report begins with a  title page . The title is centred in the upper half of the page, with each important word capitalized. The title should clearly and concisely (in about 12 words or fewer) communicate the primary variables and research questions. This sometimes requires a main title followed by a subtitle that elaborates on the main title, in which case the main title and subtitle are separated by a colon. Here are some titles from recent issues of professional journals published by the American Psychological Association.

• Sex Differences in Coping Styles and Implications for Depressed Mood
• Effects of Aging and Divided Attention on Memory for Items and Their Contexts
• Computer-Assisted Cognitive Behavioural Therapy for Child Anxiety: Results of a Randomized Clinical Trial
• Virtual Driving and Risk Taking: Do Racing Games Increase Risk-Taking Cognitions, Affect, and Behaviour?

Below the title are the authors’ names and, on the next line, their institutional affiliation—the university or other institution where the authors worked when they conducted the research. As we have already seen, the authors are listed in an order that reflects their contribution to the research. When multiple authors have made equal contributions to the research, they often list their names alphabetically or in a randomly determined order.

In some areas of psychology, the titles of many empirical research reports are informal in a way that is perhaps best described as “cute.” They usually take the form of a play on words or a well-known expression that relates to the topic under study. Here are some examples from recent issues of the Journal Psychological Science .

• “Smells Like Clean Spirit: Nonconscious Effects of Scent on Cognition and Behavior”
• “Time Crawls: The Temporal Resolution of Infants’ Visual Attention”
• “Scent of a Woman: Men’s Testosterone Responses to Olfactory Ovulation Cues”
• “Apocalypse Soon?: Dire Messages Reduce Belief in Global Warming by Contradicting Just-World Beliefs”
• “Serial vs. Parallel Processing: Sometimes They Look Like Tweedledum and Tweedledee but They Can (and Should) Be Distinguished”
• “How Do I Love Thee? Let Me Count the Words: The Social Effects of Expressive Writing”

Individual researchers differ quite a bit in their preference for such titles. Some use them regularly, while others never use them. What might be some of the pros and cons of using cute article titles?

For articles that are being submitted for publication, the title page also includes an author note that lists the authors’ full institutional affiliations, any acknowledgments the authors wish to make to agencies that funded the research or to colleagues who commented on it, and contact information for the authors. For student papers that are not being submitted for publication—including theses—author notes are generally not necessary.

The  abstract  is a summary of the study. It is the second page of the manuscript and is headed with the word  Abstract . The first line is not indented. The abstract presents the research question, a summary of the method, the basic results, and the most important conclusions. Because the abstract is usually limited to about 200 words, it can be a challenge to write a good one.

Introduction

The  introduction  begins on the third page of the manuscript. The heading at the top of this page is the full title of the manuscript, with each important word capitalized as on the title page. The introduction includes three distinct subsections, although these are typically not identified by separate headings. The opening introduces the research question and explains why it is interesting, the literature review discusses relevant previous research, and the closing restates the research question and comments on the method used to answer it.

The Opening

The  opening , which is usually a paragraph or two in length, introduces the research question and explains why it is interesting. To capture the reader’s attention, researcher Daryl Bem recommends starting with general observations about the topic under study, expressed in ordinary language (not technical jargon)—observations that are about people and their behaviour (not about researchers or their research; Bem, 2003 [1] ). Concrete examples are often very useful here. According to Bem, this would be a poor way to begin a research report:

Festinger’s theory of cognitive dissonance received a great deal of attention during the latter part of the 20th century (p. 191)

The following would be much better:

The individual who holds two beliefs that are inconsistent with one another may feel uncomfortable. For example, the person who knows that he or she enjoys smoking but believes it to be unhealthy may experience discomfort arising from the inconsistency or disharmony between these two thoughts or cognitions. This feeling of discomfort was called cognitive dissonance by social psychologist Leon Festinger (1957), who suggested that individuals will be motivated to remove this dissonance in whatever way they can (p. 191).

After capturing the reader’s attention, the opening should go on to introduce the research question and explain why it is interesting. Will the answer fill a gap in the literature? Will it provide a test of an important theory? Does it have practical implications? Giving readers a clear sense of what the research is about and why they should care about it will motivate them to continue reading the literature review—and will help them make sense of it.

Breaking the Rules

Researcher Larry Jacoby reported several studies showing that a word that people see or hear repeatedly can seem more familiar even when they do not recall the repetitions—and that this tendency is especially pronounced among older adults. He opened his article with the following humourous anecdote:

A friend whose mother is suffering symptoms of Alzheimer’s disease (AD) tells the story of taking her mother to visit a nursing home, preliminary to her mother’s moving there. During an orientation meeting at the nursing home, the rules and regulations were explained, one of which regarded the dining room. The dining room was described as similar to a fine restaurant except that tipping was not required. The absence of tipping was a central theme in the orientation lecture, mentioned frequently to emphasize the quality of care along with the advantages of having paid in advance. At the end of the meeting, the friend’s mother was asked whether she had any questions. She replied that she only had one question: “Should I tip?” (Jacoby, 1999, p. 3)

Although both humour and personal anecdotes are generally discouraged in APA-style writing, this example is a highly effective way to start because it both engages the reader and provides an excellent real-world example of the topic under study.

The Literature Review

Immediately after the opening comes the  literature review , which describes relevant previous research on the topic and can be anywhere from several paragraphs to several pages in length. However, the literature review is not simply a list of past studies. Instead, it constitutes a kind of argument for why the research question is worth addressing. By the end of the literature review, readers should be convinced that the research question makes sense and that the present study is a logical next step in the ongoing research process.

Like any effective argument, the literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that demonstrate it, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or it might describe one phenomenon, then describe another phenomenon that seems inconsistent with the first one, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally suggest a way to test whether it does, in fact, apply to that situation.

Looking at the literature review in this way emphasizes a few things. First, it is extremely important to start with an outline of the main points that you want to make, organized in the order that you want to make them. The basic structure of your argument, then, should be apparent from the outline itself. Second, it is important to emphasize the structure of your argument in your writing. One way to do this is to begin the literature review by summarizing your argument even before you begin to make it. “In this article, I will describe two apparently contradictory phenomena, present a new theory that has the potential to resolve the apparent contradiction, and finally present a novel hypothesis to test the theory.” Another way is to open each paragraph with a sentence that summarizes the main point of the paragraph and links it to the preceding points. These opening sentences provide the “transitions” that many beginning researchers have difficulty with. Instead of beginning a paragraph by launching into a description of a previous study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:

Another example of this phenomenon comes from the work of Williams (2004).

Williams (2004) offers one explanation of this phenomenon.

An alternative perspective has been provided by Williams (2004).

We used a method based on the one used by Williams (2004).

Finally, remember that your goal is to construct an argument for why your research question is interesting and worth addressing—not necessarily why your favourite answer to it is correct. In other words, your literature review must be balanced. If you want to emphasize the generality of a phenomenon, then of course you should discuss various studies that have demonstrated it. However, if there are other studies that have failed to demonstrate it, you should discuss them too. Or if you are proposing a new theory, then of course you should discuss findings that are consistent with that theory. However, if there are other findings that are inconsistent with it, again, you should discuss them too. It is acceptable to argue that the  balance  of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in psychology can hope for), but it is not acceptable to  ignore contradictory evidence. Besides, a large part of what makes a research question interesting is uncertainty about its answer.

The Closing

The  closing  of the introduction—typically the final paragraph or two—usually includes two important elements. The first is a clear statement of the main research question or hypothesis. This statement tends to be more formal and precise than in the opening and is often expressed in terms of operational definitions of the key variables. The second is a brief overview of the method and some comment on its appropriateness. Here, for example, is how Darley and Latané (1968) [2] concluded the introduction to their classic article on the bystander effect:

These considerations lead to the hypothesis that the more bystanders to an emergency, the less likely, or the more slowly, any one bystander will intervene to provide aid. To test this proposition it would be necessary to create a situation in which a realistic “emergency” could plausibly occur. Each subject should also be blocked from communicating with others to prevent his getting information about their behaviour during the emergency. Finally, the experimental situation should allow for the assessment of the speed and frequency of the subjects’ reaction to the emergency. The experiment reported below attempted to fulfill these conditions. (p. 378)

Thus the introduction leads smoothly into the next major section of the article—the method section.

The  method section  is where you describe how you conducted your study. An important principle for writing a method section is that it should be clear and detailed enough that other researchers could replicate the study by following your “recipe.” This means that it must describe all the important elements of the study—basic demographic characteristics of the participants, how they were recruited, whether they were randomly assigned, how the variables were manipulated or measured, how counterbalancing was accomplished, and so on. At the same time, it should avoid irrelevant details such as the fact that the study was conducted in Classroom 37B of the Industrial Technology Building or that the questionnaire was double-sided and completed using pencils.

The method section begins immediately after the introduction ends with the heading “Method” (not “Methods”) centred on the page. Immediately after this is the subheading “Participants,” left justified and in italics. The participants subsection indicates how many participants there were, the number of women and men, some indication of their age, other demographics that may be relevant to the study, and how they were recruited, including any incentives given for participation.

After the participants section, the structure can vary a bit. Figure 11.1 shows three common approaches. In the first, the participants section is followed by a design and procedure subsection, which describes the rest of the method. This works well for methods that are relatively simple and can be described adequately in a few paragraphs. In the second approach, the participants section is followed by separate design and procedure subsections. This works well when both the design and the procedure are relatively complicated and each requires multiple paragraphs.

What is the difference between design and procedure? The design of a study is its overall structure. What were the independent and dependent variables? Was the independent variable manipulated, and if so, was it manipulated between or within subjects? How were the variables operationally defined? The procedure is how the study was carried out. It often works well to describe the procedure in terms of what the participants did rather than what the researchers did. For example, the participants gave their informed consent, read a set of instructions, completed a block of four practice trials, completed a block of 20 test trials, completed two questionnaires, and were debriefed and excused.

In the third basic way to organize a method section, the participants subsection is followed by a materials subsection before the design and procedure subsections. This works well when there are complicated materials to describe. This might mean multiple questionnaires, written vignettes that participants read and respond to, perceptual stimuli, and so on. The heading of this subsection can be modified to reflect its content. Instead of “Materials,” it can be “Questionnaires,” “Stimuli,” and so on.

The  results section  is where you present the main results of the study, including the results of the statistical analyses. Although it does not include the raw data—individual participants’ responses or scores—researchers should save their raw data and make them available to other researchers who request them. Several journals now encourage the open sharing of raw data online.

Although there are no standard subsections, it is still important for the results section to be logically organized. Typically it begins with certain preliminary issues. One is whether any participants or responses were excluded from the analyses and why. The rationale for excluding data should be described clearly so that other researchers can decide whether it is appropriate. A second preliminary issue is how multiple responses were combined to produce the primary variables in the analyses. For example, if participants rated the attractiveness of 20 stimulus people, you might have to explain that you began by computing the mean attractiveness rating for each participant. Or if they recalled as many items as they could from study list of 20 words, did you count the number correctly recalled, compute the percentage correctly recalled, or perhaps compute the number correct minus the number incorrect? A third preliminary issue is the reliability of the measures. This is where you would present test-retest correlations, Cronbach’s α, or other statistics to show that the measures are consistent across time and across items. A final preliminary issue is whether the manipulation was successful. This is where you would report the results of any manipulation checks.

The results section should then tackle the primary research questions, one at a time. Again, there should be a clear organization. One approach would be to answer the most general questions and then proceed to answer more specific ones. Another would be to answer the main question first and then to answer secondary ones. Regardless, Bem (2003) [3] suggests the following basic structure for discussing each new result:

• Remind the reader of the research question.
• Give the answer to the research question in words.
• Present the relevant statistics.
• Qualify the answer if necessary.
• Summarize the result.

Notice that only Step 3 necessarily involves numbers. The rest of the steps involve presenting the research question and the answer to it in words. In fact, the basic results should be clear even to a reader who skips over the numbers.

The  discussion  is the last major section of the research report. Discussions usually consist of some combination of the following elements:

• Summary of the research
• Theoretical implications
• Practical implications
• Limitations
• Suggestions for future research

The discussion typically begins with a summary of the study that provides a clear answer to the research question. In a short report with a single study, this might require no more than a sentence. In a longer report with multiple studies, it might require a paragraph or even two. The summary is often followed by a discussion of the theoretical implications of the research. Do the results provide support for any existing theories? If not, how  can  they be explained? Although you do not have to provide a definitive explanation or detailed theory for your results, you at least need to outline one or more possible explanations. In applied research—and often in basic research—there is also some discussion of the practical implications of the research. How can the results be used, and by whom, to accomplish some real-world goal?

The theoretical and practical implications are often followed by a discussion of the study’s limitations. Perhaps there are problems with its internal or external validity. Perhaps the manipulation was not very effective or the measures not very reliable. Perhaps there is some evidence that participants did not fully understand their task or that they were suspicious of the intent of the researchers. Now is the time to discuss these issues and how they might have affected the results. But do not overdo it. All studies have limitations, and most readers will understand that a different sample or different measures might have produced different results. Unless there is good reason to think they  would have, however, there is no reason to mention these routine issues. Instead, pick two or three limitations that seem like they could have influenced the results, explain how they could have influenced the results, and suggest ways to deal with them.

Most discussions end with some suggestions for future research. If the study did not satisfactorily answer the original research question, what will it take to do so? What  new  research questions has the study raised? This part of the discussion, however, is not just a list of new questions. It is a discussion of two or three of the most important unresolved issues. This means identifying and clarifying each question, suggesting some alternative answers, and even suggesting ways they could be studied.

Finally, some researchers are quite good at ending their articles with a sweeping or thought-provoking conclusion. Darley and Latané (1968) [4] , for example, ended their article on the bystander effect by discussing the idea that whether people help others may depend more on the situation than on their personalities. Their final sentence is, “If people understand the situational forces that can make them hesitate to intervene, they may better overcome them” (p. 383). However, this kind of ending can be difficult to pull off. It can sound overreaching or just banal and end up detracting from the overall impact of the article. It is often better simply to end when you have made your final point (although you should avoid ending on a limitation).

The references section begins on a new page with the heading “References” centred at the top of the page. All references cited in the text are then listed in the format presented earlier. They are listed alphabetically by the last name of the first author. If two sources have the same first author, they are listed alphabetically by the last name of the second author. If all the authors are the same, then they are listed chronologically by the year of publication. Everything in the reference list is double-spaced both within and between references.

Appendices, Tables, and Figures

Appendices, tables, and figures come after the references. An  appendix  is appropriate for supplemental material that would interrupt the flow of the research report if it were presented within any of the major sections. An appendix could be used to present lists of stimulus words, questionnaire items, detailed descriptions of special equipment or unusual statistical analyses, or references to the studies that are included in a meta-analysis. Each appendix begins on a new page. If there is only one, the heading is “Appendix,” centred at the top of the page. If there is more than one, the headings are “Appendix A,” “Appendix B,” and so on, and they appear in the order they were first mentioned in the text of the report.

After any appendices come tables and then figures. Tables and figures are both used to present results. Figures can also be used to illustrate theories (e.g., in the form of a flowchart), display stimuli, outline procedures, and present many other kinds of information. Each table and figure appears on its own page. Tables are numbered in the order that they are first mentioned in the text (“Table 1,” “Table 2,” and so on). Figures are numbered the same way (“Figure 1,” “Figure 2,” and so on). A brief explanatory title, with the important words capitalized, appears above each table. Each figure is given a brief explanatory caption, where (aside from proper nouns or names) only the first word of each sentence is capitalized. More details on preparing APA-style tables and figures are presented later in the book.

Sample APA-Style Research Report

Figures 11.2, 11.3, 11.4, and 11.5 show some sample pages from an APA-style empirical research report originally written by undergraduate student Tomoe Suyama at California State University, Fresno. The main purpose of these figures is to illustrate the basic organization and formatting of an APA-style empirical research report, although many high-level and low-level style conventions can be seen here too.

Key Takeaways

• An APA-style empirical research report consists of several standard sections. The main ones are the abstract, introduction, method, results, discussion, and references.
• The introduction consists of an opening that presents the research question, a literature review that describes previous research on the topic, and a closing that restates the research question and comments on the method. The literature review constitutes an argument for why the current study is worth doing.
• The method section describes the method in enough detail that another researcher could replicate the study. At a minimum, it consists of a participants subsection and a design and procedure subsection.
• The results section describes the results in an organized fashion. Each primary result is presented in terms of statistical results but also explained in words.
• The discussion typically summarizes the study, discusses theoretical and practical implications and limitations of the study, and offers suggestions for further research.
• Practice: Look through an issue of a general interest professional journal (e.g.,  Psychological Science ). Read the opening of the first five articles and rate the effectiveness of each one from 1 ( very ineffective ) to 5 ( very effective ). Write a sentence or two explaining each rating.
• Practice: Find a recent article in a professional journal and identify where the opening, literature review, and closing of the introduction begin and end.
• Practice: Find a recent article in a professional journal and highlight in a different colour each of the following elements in the discussion: summary, theoretical implications, practical implications, limitations, and suggestions for future research.

Long Descriptions

Figure 11.1 long description: Table showing three ways of organizing an APA-style method section.

In the simple method, there are two subheadings: “Participants” (which might begin “The participants were…”) and “Design and procedure” (which might begin “There were three conditions…”).

In the typical method, there are three subheadings: “Participants” (“The participants were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”).

In the complex method, there are four subheadings: “Participants” (“The participants were…”), “Materials” (“The stimuli were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”). [Return to Figure 11.1]

• Bem, D. J. (2003). Writing the empirical journal article. In J. M. Darley, M. P. Zanna, & H. R. Roediger III (Eds.),  The compleat academic: A practical guide for the beginning social scientist  (2nd ed.). Washington, DC: American Psychological Association. ↵
• Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility.  Journal of Personality and Social Psychology, 4 , 377–383. ↵

A type of research article which describes one or more new empirical studies conducted by the authors.

The page at the beginning of an APA-style research report containing the title of the article, the authors’ names, and their institutional affiliation.

A summary of a research study.

The third page of a manuscript containing the research question, the literature review, and comments about how to answer the research question.

An introduction to the research question and explanation for why this question is interesting.

A description of relevant previous research on the topic being discusses and an argument for why the research is worth addressing.

The end of the introduction, where the research question is reiterated and the method is commented upon.

The section of a research report where the method used to conduct the study is described.

The main results of the study, including the results from statistical analyses, are presented in a research article.

Section of a research report that summarizes the study's results and interprets them by referring back to the study's theoretical background.

Part of a research report which contains supplemental material.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Writing Research Papers

• Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

• Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
• Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

• Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
• Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
• Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

• For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

• How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
• Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
• Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

• Writing Research Paper Videos

APA Journal Article Reporting Guidelines

• Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
• Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

• Formatting APA Style Papers in Microsoft Word
• How to Write an APA Style Research Paper from Hamilton University
• WikiHow Guide to Writing APA Research Papers
• Sample APA Formatted Paper with Comments
• Sample APA Formatted Paper
• Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

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• Formatting Research Papers
• Using Databases and Finding References
• What Types of References Are Appropriate?
• Evaluating References and Taking Notes
• Citing References
• Writing a Literature Review
• Writing Process and Revising
• Improving Scientific Writing
• Academic Integrity and Avoiding Plagiarism
• Writing Research Papers Videos

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Quantitative Methods in Psychology: Inevitable and Useless

Aaro toomela.

1 Institute of Psychology, Tallinn University, Tallinn, Estonia

Science begins with the question, what do I want to know? Science becomes science, however, only when this question is justified and the appropriate methodology is chosen for answering the research question. Research question should precede the other questions; methods should be chosen according to the research question and not vice versa. Modern quantitative psychology has accepted method as primary; research questions are adjusted to the methods. For understanding thinking in modern quantitative psychology, two epistemologies should be distinguished: structural-systemic that is based on Aristotelian thinking, and associative-quantitative that is based on Cartesian–Humean thinking. The first aims at understanding the structure that underlies the studied processes; the second looks for identification of cause–effect relationships between the events with no possible access to the understanding of the structures that underlie the processes. Quantitative methodology in particular as well as mathematical psychology in general, is useless for answering questions about structures and processes that underlie observed behaviors. Nevertheless, quantitative science is almost inevitable in a situation where the systemic-structural basis of behavior is not well understood; all sorts of applied decisions can be made on the basis of quantitative studies. In order to proceed, psychology should study structures; methodologically, constructive experiments should be added to observations and analytic experiments.

Science begins with questions. Everybody can have questions, and even answers to them. What makes science special is its method of answering questions. Therefore a scientist must ask questions both about the phenomenon to be understood and about the method. There are actually not one or two but four principal questions that should be asked by every scientist when conducting studies (Toomela, 2010b ):

• What do I want to know, what is my research question?
• Why I want to have an answer to this question?
• With what specific research procedures (methodology in the strict sense of the term) can I answer my question?
• Are the answers to three first questions complementary, do they make a coherent theoretically justified whole?

First, there should be a question about some phenomenon that needs an answer. Next, the need for an answer should be justified – in science it is quite possible to ask “wrong” questions, which answers do not help understanding the studied phenomena. Vygotsky ( 1982a ) gave in his colorful language an ironic example of answering scientifically wrong questions:

One can multiply the number of citizens of Paraguay with the number of versts [an obsolete Russian unit of length] from Earth to Sun and divide the result with the average length of life of an elephant and conduct this whole operation without a flaw even in one number; and yet the number found in the operation can confuse anybody who would like to know the national income of that country (p. 326; my translation).

It can be said that modern psychology is more advanced than science of Vygotsky's time; perhaps the questions asked in the modern science are meaningful. This opinion, however, may be wrong. One source of wrong questions about the studied phenomena is unsatisfactory answer to the last question – when answers to first three questions do not agree one with another. In this paper I am going to suggest that psychology asks “wrong” questions far too often. The problem is related to the mismatch in answers to the first and third question. Specifically, quantitative methodology that dominates psychology of today is not appropriate for achieving understanding of mental phenomena, psyche.

The number of substantial problems with quantitative methods brought out by scholars is increasing every year. Already one observation could make scientists cautious. The questions provided above are in a certain order – first we should have a question about the phenomenon and only then the appropriate method for finding an answer should be looked for. Substantial part of modern psychology follows the opposite order of decisions – first it is decided to use quantitative methods, and the question about the phenomenon is already formulated in the language of data analysis. Between 1940 and 1955, statistical data analysis became the indispensable tool for hypothesis testing; with this change of scientific methodology, statistical methods began to turn into theories of mind. Instead of looking for theory that perhaps can be elaborated with the help of statistical tools, statistical tools began to determine the shape of theories (Gigerenzer, 1991 , 1993 ).

For instance, a researcher may ask, how many factors emerge in the analysis of personality or intelligence test results. But why to look for the number of factors if personality or intelligence is studied? We would guess here that the original question may be something like, is it possible to identify distinguishable components in the structure of personality or intelligence? However, the decision to use factor analysis for that purpose must be justified before this method is chosen. This justification seems to be missing; it is only a hypothesis – ungrounded hypothesis – that factor analysis is an appropriate tool for identifying distinct mental processes that underlie behavioral data (filling in a questionnaire is behavior). The problems emerge already with the determination of the number of factors to retain. There are formal and substantial criteria for that. Formal decisions are based on Kaiser's criterion, Cattel's scree test, Velicer's Minimum Average Partial test, or Horn's parallel analysis. There is no evidence that any of these criteria is actually suitable for distinguishing the number of distinct processes that underlie behavior. Researchers also decide the number of factors on the basis of comprehensibility – the solution which generates the most comprehensible factor structure is chosen. But this substantial criterion is always used after applying formal criteria; nobody starts from the possibility that all, say, 248 items of an inventory correspond to 248 distinct mental processes. The number of factors usually retained – from two to six or seven – seems to correspond to processing limitations of the researcher's working memory rather than to true structure of the mind.

In this paper, epistemological issues that underlie quantitative methods used in psychology are discussed. I suggest that, regardless of the research area in psychology, mathematical procedures of any kind cannot answer questions about the structure of mind. The discussion focuses primarily on the statistical methodology as used in psychology today; yet there are fundamental problems inherent to other kinds of mathematical approaches as well. My intention is not to suggest that scientific studies of mind should reject mathematical approaches. Rather, it should be made clear, which questions can be answered with the help of mathematical methods and which cannot.

Which Questions Can and Which Cannot be Answered by the Statistical Data Analysis Procedures?

We should look for reasons to use statistical data analysis into the works of those, who introduced quantitative methodology into sciences in general and psychology in particular. Today, as a rule, users and developers of statistical data analysis procedures do not ask any more which questions can and which cannot be answered with the help of those procedures. Scholars who introduced mathematical procedures, however, made it clear, what kinds of answers they are looking for. We will see that these scholars would reject the questions answered by statistical procedures today for reasons that are largely ignored without any scientific reason by modern researchers. One of the most influential figures in introducing factor analysis into psychology was Thurstone. There are several ideas in his fundamental work The vectors of mind that are worthy of attention (Thurstone, 1935 ). These ideas, in the most part, underlie the use of not only factor analysis but the use of all forms of covariation-based data analysis procedures.

What are the (statistical) causes of relationships between variables?

Thurstone suggested that the object of factor analysis is to discover the mental faculties. It is interesting that for him factor analysis alone would have never been sufficient for proving that a new faculty has been discovered – he held a position that results of factor analysis must be supported by the experimental observations. In another work he found that in some fields of studies tests are used that are not tests at all – “They are only questionnaires in which the subject controls the answers completely. It would probably be very fruitful to explore the domain of temperament with experimental tests instead of questionnaires.” (Thurstone, 1948 , p. 406). So, Thurstone would very likely reject the modern practice to study many psychological phenomena – personality, values, attitudes, mental states, etc. – with questionnaires alone as it is often done now. There seems to be no theory that would justify studies of the structure of mind only by questionnaires. Without a theory that links subjectively controlled patterns of answers to objective structure of mind the results of all such studies are not grounded. Thorough analysis of this issue is beyond the scope of the current paper.

We can ask, what was the general question Thurstone aimed at answering with the help of factor analysis? Thurstone was not asking how mental faculties operate; he was looking for identification of what he called abilities , i.e., traits (which are attributes of individuals) which are defined by what an individual can do (Thurstone, 1935 , p. 48).

The same questions about identification of “abilities” underlie the use of not only factor analysis but other covariation-based statistical data-analysis procedures as well. Here it is feasible to go deeper into the roots of introducing quantitative data-analysis into sciences. Methods for calculating correlation coefficients entered sciences somewhere in the middle of the 19th century but became popular with the works of Pearson (cf., 1896 ). He formulated the tasks of statistical data analysis in the following way:

One of the most frequent tasks of the statistician, the physicist, and the engineer, is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, i.e., it may enable us to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. (Pearson, 1902 , p. 266).

I think it is especially noteworthy – formula that is searched for, represents observations or measurements – i.e., variables . This fact is so obvious that consequences that follow from it are usually not thought through. The main problem related to use of observations and measurements is that they do not necessarily reflect the reality objectively; they are subjective interpretations of the world by the researcher. This is especially true in the situation when the observation – of external behavior, in psychology – is supposed to reflect operation of the hidden from direct observation construct, mental faculty. Thurstone acknowledged that externally similar behaviors can be based on internally different mechanisms; and he was only interested in finding formulas that express regularities in the external behavior. Pearson essentially did the same; he assumed that with the help of correlations, it is possible to get closer to identification of different causes of external regularities. Pearson also did not aim at describing how these causes operate. He, similarly with Thurstone, was looking for identifying regularities (faculties in Thurstone's terms) in the observable cause → effect chains without claiming that unique cause, hidden from direct observation, is necessarily identified. This limitation for the aim of statistical analyses can be found in many of his works, as in the following passage, for instance:

We shall now assume that the sizes of this complex of organs are determined by a great variety of independent contributory causes, for example, magnitudes of other organs not in the complex, variations in environment, climate, nourishment, physical training, various ancestral influences, and innumerable other causes, which cannot be individually observed or their effects measured. (Pearson, 1896 , p. 262).

When Pearson correlated sizes of organs he was, thus, aware that mathematical formulas that reflect certain commonalities in the variation of two variables do not reflect unique roles of individual contributory causes; these causes determine the measured sizes in ways that are not known.

The general form of the question Pearson answered with statistical analyses can be formulated: What is the value of a certain variable when we know a value of another variable that is correlated to the first? An example of this kind of use was provided by Pearson when he reconstructed “the parts of an extinct race from a knowledge of a size of a few organs or bones, when complete measurements have been or can be made for an allied and still extant race.” (Pearson, 1899 , p. 170).

Correlation, in this case, can be understood as a representation of some abstract cause which “makes” variables to covary. Thus, the same question can be reformulated: Is it possible to discover an abstract cause-like communality of different variables that is expressed as covariation? I think Pearson was very clear in understanding that correlation reflects covariations of appearances; the true underlying causes of covariation, the mechanisms that determine how the covariation emerges, cannot be known with statistical procedures – there are many independent causal agents operating, “which cannot be individually observed or their effects measured.” At the same time, he could interpret covariations between variables in non-mathematical terms; he interpreted them as reflecting common cause. For instance, he concluded on the basis of statistical analyses that fertility and fecundity are inherited characteristics (Pearson et al., 1899 ) – he, thus, suggested that some non-mathematical factor, inheritance, underlies the correlations he discovered.

Thurstone went a step further and suggested – it is possible to find formulas for expressing patterns of covariations; factor analysis identifies “faculties” or “abilities” that underlie correlation among several variables simultaneously. He was also clear that factor analysis expresses relationships between appearances; possible differences in internal mechanisms that may underlie externally similar behaviors are not reflected in the results of factor analysis.

Limits on the questions that can be answered with the help of statistical data analysis procedures

Statistical theories reflect regularities only in appearances.

Pearson was fully aware of the limits of statistical theories. Theory that looks for mechanisms should be clearly distinguished from pure descriptions of regularities in superficial observations; statistical laws… “have nothing whatever to do with any physiological hypothesis” (Pearson, 1904 , p. 55). According to him, “the statistical view of inheritance is not at basis a theory, but a description of observed facts, with which any physiological theory must be in accord” (Pearson, 1903–1904 , p. 509). We know from the modern biological theory of inheritance, how correct he was: the statistical laws discovered by him had really nothing to do with the discovery of the structure of DNA, even though they may have directed biologists to look for possible substrate of inheritance. It is also noteworthy that, contrary to Pearson, after discovering the structure of DNA and explaining the biological mechanisms of inheritance, there was no need at all to check whether this theory of structure accords with Pearson's laws or not; these laws became irrelevant for the theory.

Thurstone was looking for discovering mental faculties with the help of the factor analysis. Similarly with Pearson, he did not assume that discovered faculties can be directly related to mental operations; he did not assume one-to-one correspondence between observed behaviors and mechanisms that underlie them:

The attitudes of people on a controversial social issue have been appraised by allocating each person to a point in a linear continuum as regards his favorable or unfavorable affect toward the psychological object. Some social scientists have objected because two individuals may have the same attitude score toward, say, pacifism, and yet be totally different in their backgrounds and in the causes of their similar social views. If such critics were consistent, they would object also to the statement that two men have identical incomes, for one of them earns when the other one steals. They should also object to the statement that two men are of the same height. The comparison should be held invalid because one of the men is fat and the other is thin. (Thurstone, 1935 , p. 47)

It was shown above that Thurstone was not asking how mental faculties operate; he was looking for just identification of abilities. There can be, thus, an ability to make money; and this ability is treated the same independently of whether the income is made by earning or by stealing. Statistical procedures used by Thurstone aim at discovering an ability to make income, for instance, but there would be no clue as to the mechanisms of the income-making. So, if there is a phenomenon like income in the world, perhaps factor analysis would be helpful to discover it.

There are, however, reasons to disagree partly with Thurstone's interpretation of this procedure. He suggested, for example, that incomes based on different sources should be considered to be different if social scientists were fully consistent; he disagreed with this idea. Essentially, Thurstone seems to assume that it is possible to isolate the phenomenon from the world and perhaps study it after isolation as a thing in itself. In the real world, however, a thing that exists completely isolated from the world would be unknowable in principle, because we know the world only being in relation with it. Income is by definition the amount of money or its equivalent received during a period of time. If we analyze the phenomenon of money, we discover that it is a relational phenomenon. Money is a medium of exchange and unit of account; money, thus, is a phenomenon that mediates certain economic relationships. Outside the society, money ceases to be money; it becomes just a physical object. Societies determine relations toward money in much more complex ways than just in economic terms. For instance, in modern democratic societies it would be legally possible to confiscate money if the money turns out to be stolen; but there are no societies where money would be confiscated because it was earned. So, the incomes of two men, for one who earns and for the other who steals, are not the same indeed.

Thurstone would likely – and fairly – reject this critique by telling that “Every scientific construct limits itself to specified variables without any pretense to cover those aspects of a class of phenomena about which it has said nothing” (Thurstone, 1935 , p. 47). By saying that a factor represents some isolated characteristic of the studied phenomenon, Thurstone retains consistency of his approach. And this is exactly where the weakness of statistical theories lies: these theories are about regularities in appearances with no necessary connection to the underlying mechanisms. Thurstone, similarly with Pearson, was fully aware of this limitation:

This volume is concerned with methods of discovering and identifying significant categories in psychology and in other social sciences. […] It is the faith of all science that an unlimited number of phenomena can be comprehended in terms of a limited number of concepts or ideal constructs. […] The constructs in terms of which natural phenomena are comprehended are man-made inventions. To discover a scientific law is merely to discover that a man-made scheme serves to unify, and thereby to simplify, comprehension of a certain class of natural phenomena. A scientific law is not to be thought of as having an independent existence which some scientist is fortunate to stumble upon. A scientific law is not a part of nature. It is only a way of comprehending nature. […] While the ideal constructs of science do not imply physical reality, they do not deny the possibility of some degree of correspondence with physical reality. But this is a philosophical problem that is quite outside the domain of science. (Thurstone, 1935 , p. 44).

If biologists would have accepted this view, there would be no modern science of inheritance, for example. Modern biological theories do not look for “some degree of correspondence” between theories and physical reality; these theories aim at full correspondence. 1 In other words, scientific theories are not assumed to represent human-made generalizations based on covariations between appearances with no necessary connection to the reality that underlies these connections. On the contrary, the aim of sciences has become to understand exactly what Thurstone, Pearson and other statistical theorists did not aim at – to understand phenomena as they exist, not as they seem to us.

Statistical theories of mechanisms depend on postulates that are not grounded and on conditions that are not satisfied

Modern quantitative psychology may sometimes claim that its aims are similar to modern biology or physics – the discovery of the mechanisms that underlie the appearances, the observable behaviors. Founders of the statistical theorizing denied such possibility by means of quantitative data analysis that is based on analysis of covariations between variables; perhaps they missed something fundamental that makes possible what they declared not to be possible? Perhaps it became possible to discover, for instance, by means of factor analysis the structure of mind as it is, not just as a man-made law that reflects only superficial covariations between observed events?

There are reasons to suggest that quantitative tools are not appropriate for this aim. Statistical data analysis procedures used in modern quantitative psychology are based on postulates that do not contradict the aims of statistical theorizing Thurstone, Pearson and their followers had. The same postulates, however, are incompatible with the aims of those who look for properties of mind as it is and not only for the generalizations that can be made about any kind of observations.

Postulate of quantitative measurement

Modern psychology must postulate that variables that are entered into analyses can be interpreted in terms of underlying mechanisms. Otherwise interpretation of the results of analyses in terms of those mechanisms would not be valid. Reasons to doubt whether this postulate is actually true, emerge already from the Pearson's works. Namely, he extended statistical theorizing to characteristics that cannot be quantified (Pearson and Lee, 1900 ). For Thurstone and Pearson, it was not a problem that measured variables represent events with essentially unknown underlying causes because they did not aim at understanding those causes; they just looked for descriptions of statistical regularities in different observations. So for them even the question whether a variable represents something that can be quantified or not, was not an issue. But it must be one of the first problems to solve if physical or psychological reality is aimed to understand – what exactly is encoded in variables?

Psychology of today can be called pathological – many hypotheses are accepted as true without attempts being made to test them; the hypothesis that psychological attributes are quantitative is not tested in psychology of today (Michell, 2000 ). Worse, there are all reasons to suggest that attributes that are “measured” in psychology cannot be measured, because they are not quantitative (e.g., Essex and Smythe, 1999 ; Michell, 2010 ). Therefore, covariations between variables have no meaningful interpretation as to the underlying mechanisms in principle, because different levels of variables may denote qualitatively different phenomena. This alone would be sufficient for rejecting interpretations of quantitative analyses about underlying mechanisms. But there is more – as Thurstone also pointed out – externally similar behaviors can rely on internally different mechanisms. Thus even the same level on some variable may represent qualitatively different phenomena in different cases. It follows that under such circumstances no quantitative procedure can distinguish qualitatively different mechanisms that may underlie externally the same behavior – the variable that encodes behavior independently of differences in (psychic) mechanisms simply does not contain information about mechanism (Toomela, 2008 ).

If a researcher would be interested in distinguishing the psychological mechanisms of behavior, other procedures would be needed. A researcher would invent different methods to reveal differences in externally similar behaviors. For instance, in many situations it could be possible just to ask directly from the person a justification for his or her behavior. It is important that the methods that must be created for discovering potential differences in externally similar behaviors are only qualitative because, as we saw, variables entered into quantitative data analyses lack the necessary information.

Postulate of continuity

There is another postulate, which underlies quantitative data analysis procedures. Thurstone, for instance, postulated:

The standard scores of all individuals in an unlimited number of abilities can be expressed, in first approximation, as linear functions of their standard scores in a limited number of abilities (Thurstone, 1935 , p. 50).

So, there is a postulate that linear functions characterize relationships between the abilities (i.e., mental faculties) and individual acts of behavior. The main question that should be answered here is not only about postulate of linearity – the same problem would be related to non-linear relationships between variables – but about the postulate of continuity that is also made with this postulate of linearity. If it would turn out that some relationships between events are in essence qualitative then no factor analysis, or any other kind of quantitative data manipulation, can reveal those qualitative aspects of changes.

Often qualitative relationships hold between events. Lack of one nucleotide in a gene may be related to qualitatively different processes of protein synthesis, related to that gene. One extra chromosome does not just end up with more proteins; it ends up with qualitatively different pathologies, depending on the chromosome. It is also not meaningful to postulate a continuous quantitative series of events in the following continuum: one chromosome missing – the normal number of chromosomes – one extra chromosome in addition to the normal set.

Postulate of correspondence between inter-individual and intra-individual levels of analysis

In modern psychology, it is often assumed that intra-individual faculties can be revealed by studying inter-individual differences. This can be a major problem with all theories about individual attributes that are based on studies of differences between individuals: differences between individuals do not reflect distinctions inside individual minds (e.g., Lewin, 1935 ; Epstein, 1980 ; Toomela, in press-b ).

Several quantitative scholars have provided substantial reasons why inter-individual differences cannot ground interpretations at the intra-individual level. They propose that quantitative analyses should be conducted with variables that encode intra-individual variability (e.g., Molenaar, 2004 ; Hamaker et al., 2005 ; Molenaar and Valsiner, 2005 ; Nesselroade et al., 2007 ; Boker et al., 2009 ). This approach, however, still assumes continuity and quantification. Before analyses of intra-individual variabilities, it must be demonstrated that the attributes, which are encoded as variables, can be quantified at all. The variables that are used in intra- individual analyses, however, are usually based on the scores of the same tests and inventories as used in inter-individual analyses. Therefore conducting analyses at the intra-individual level still cannot ground interpretations about attributes of mind. Another problem related to intra-individual quantitative approach follows from their assumption that data collected over time, reflect qualitatively the same processes. This assumption is in many cases wrong. A person answering the same question repeatedly does not necessarily rely on the same mental operations – already second time the same question is asked, a person can answer in a certain way because he remembers answering the same question before. Data encoded as variables, again, do not reflect such qualitative changes of mental operations that underlie externally similar answers (see also Toomela, 2008 , in press-b ).

Postulate of interpretability of covariations between variables

Modern quantitative psychology also assumes that components of mental attributes can be discovered by analyzing covariations of variables. This postulate is questionable as well. As a rule, qualitatively different wholes emerge from the same elements in qualitatively different relationships. Quantitative data analysis, however, is not suitable for taking quality of relationships into account.

Human language, for instance, is based on units – words – that are composed from a limited number of sounds or letters in different relationships. We can take a series of events, words, and find perfect covariation between variables, sounds, in those events. Let us take, for instance, a series of events – words – this-shit-hits-pool-loop-polo. We create the following data-file from our observation of those six cases so that variables represent presence or absence of letters in each event/word:

We could make many different statistical analyses with those data and would not get any closer to understanding what is happening. Perhaps we would discover that all variables are perfectly correlated; we would discover that this data set can be perfectly “explained” by one factor, etc. Statistically, such results would be a perfect dream for a quantitative scientist. And yet all this would have no meaning. The data in the table show where the problem is – first three and last three qualitatively different cases are identical after quantification. Here we know that the cases are not identical; we do not know it when solving usual scientific problems. In any case, quantification of data into variables where the possibility of qualitatively different relationships between variables is ignored ends up with non-sense if qualitatively different wholes emerge from the same attributes encoded as variables.

Now it can be objected that such phenomena perhaps are not common. Nothing would be further from truth – the world around us provides massive amount of examples where the same elements in different relationships “cause” the emergence of qualitatively different wholes. The structure of DNA and its relationship to protein synthesis in a cell is an example; all chemical substances that are composed from the same elements in different relationships would be examples; different tools that can be made from the same material; different houses that can be built from the same stones; money that is earned and money that is stolen is also not the same, etc.

Conditions that are not satisfied

Over the last decade or two, an increasing number of substantial problems with statistical data analysis have been revealed. Some of them I have already mentioned above. But the list is definitely not complete with this. For instance, there are fundamental problems of interpreting variables that encode behavioral data (Toomela, 2008 ). The problems with interpretations emerge when (1) variables contain information about events at different levels of analysis; (2) wrong attribute from many that characterize the observed event is chosen for encoding into a variable; (3) measurement tool is not sufficiently sensitive (i.e., certain behaviors and mental phenomena underlying it exist but are not represented in the tools that are supposed to “measure” this mental phenomenon); (4) the studied phenomenon is absolutely necessary and therefore it does not vary; (5) variables represent variability that emerges because of the properties of the test or questionnaire rather than because the phenomenon really varies; and (6) the variable does not encode variability at the causally relevant range.

Results of the statistical data analyses cannot be interpreted in terms of the processes that underlie observed behaviors unless the meaning of the variables is clear. This condition is not satisfied in psychology. If the meaning of a variable is not clear then statistical data analysis may end up with demonstrating misleading dependencies or misleading independencies. Common textbooks of statistical data analysis all agree that discovery of a dependency between variables cannot be interpreted causally; these textbooks usually do not mention that absence of dependence also cannot be unequivocally interpreted – statistical independence of variables does not demonstrate absence of causal connections. If neither dependence nor independence can be unequivocally interpreted, the results of statistical data analyses cannot be taken as evidence for or against causal connections.

Remark on other kind of questions – questions that cannot be answered statistically

Quantitative psychology asks questions about patterns of relationships between variables; the main question to be answered by such analyses is whether it is possible to identify some faculty, some ability, some cause that underlies observed behavior. In the discussion above I brought again and again examples from biology and chemistry, where the format of questions is different. In addition (not instead!) to asking whether a certain cause can be identified, questions are asked about the structure of the studied phenomena – what elements in which particular relationships underlie the emergence of a whole phenomenon that is aimed to understand. Quantitative data manipulations cannot reveal structure because in structures qualities of elements and qualities of relationships between elements determine the whole.

Altogether, there is not one epistemology that underlies science but two; one is looking for identification of cause → effect relationships and the other is aiming at structural-systemic description of the phenomena under study (see more on these two epistemologies, e.g., Toomela, 2009 , 2010a , in press-a ). These two epistemologies are rooted in philosophy. Next a very short description of the philosophical roots of these epistemologies is provided. It turns out that modern quantitative psychology is based on Cartesian–Humean epistemology whereas modern biology, chemistry, and several other sciences are based on Aristotelian epistemology. Furthermore, psychology pretends to be like other sciences and superficially aims at understanding reality that underlies appearances. This, however, is impossible. We will see that in psychology there is a fundamental mismatch between questions asked and methods used to answer these questions.

Two Epistemologies

Two epistemologies that underlie different views on science are first of all distinct in their understanding of what is cause and causality. History of the notion of causality is complex; philosophers, and scientists have formulated a wide variety of theories of causation, each substantively different from the others. A nice summary of different definitions of causality can be found in Chambers’ Cyclopaedia (Chambers, 1728a , b ). Under the entry “CAUSE” there is First Cause and Second Causes and many more. Under the “Causes in the School Philosophy,” there are: (1) Efficient causes; (2) Material causes; (3) Formal causes; (4) Final causes; (5) Exemplary causes. In the other way, again, “Causes” are distinguished into Physical, Natural, and Moral. Or yet another way, “Causes” are considered as Universal or Particular; Principal or Instrumental; Total or Partial; Univocal or Equivocal, etc. Two prominent views on causes and causality are relevant in the context of this paper.

Aristotle suggested that to know causes means to explain, to know “why” (e.g., Aristotle, 1941c , p. 240, Bk.II, 194 b ). This knowledge of causes is not just knowledge, it is scientific knowledge: “We think we have scientific knowledge when we know the cause (Aristotle, 1941b , p. 170, Bk.II, 94 a ). So, we can say that the aim of sciences is understanding what the causes of the studied phenomena are.

Aristotle distinguished four kinds of causes. In different works he described them from different perspectives. I am suggesting that Aristotelian philosophy of causality rooted structural-systemic epistemology that is followed by many sciences today. Shortly, according to this epistemology, scientific understanding implies description of the distinguishable elements, their specific relationships, the qualities that characterize the novel whole that emerges in the synthesis of those elements, and dynamic processes of the emergence of the whole (Toomela, 2009 , 2010a ). The connection of this kind of epistemology to Aristotelian becomes evident with the following quote:

All the causes now mentioned fall under four senses […] some are cause as the substratum (e.g., the parts ), others as the essence ( the whole, the synthesis , and the form). The semen, the physician, the adviser, and in general the agent, are all sources of change or of rest. The remainder are causes as the end […] (Aristotle, 1941a , p. 753, Bk.V, 1013 b , my emphasis)

So, here we find concepts of parts, relationships or synthesis, and whole or form. We also find here another important notion for structural-systemic epistemology – emergence or causes of change. These four causes are called by tradition that was established long after Aristotle's time, material , formal , efficient , and final cause , respectively.

Descartes and hume

Two thousand years after Aristotle, we find considerably more limited views on causality. Instead of four complementary kinds of causes only one – efficient causality – is taken.

Descartes and efficient causality

Descartes’ view on causality is fundamentally different from the Aristotelian. First of all, he accepts only efficient causes and second, these efficient causes are very different from Aristotelian. For Descartes, cause is: independent, simple, universal, single, equal, similar, straight, etc.; effect , in turn, is: relative, dependent, composite, particular, many, unequal, dissimilar, oblique, etc. (cf. Descartes, 1985c ). According to Descartes, effects can be deduced from causes in a series of steps. The cause–effect relationship, therefore, is unidirectional.

Another noteworthy idea in Cartesian epistemology was that “cause and effect are correlatives” (Descartes, 1985c , p. 22). In most cases, cause–effect relationships are essentially correlations, just covariations of events; there is, however, the First Cause – God – on whose power all causal relationships depend (cf. Descartes, 1985a , b ). As God's plans cannot be known by less perfect humans (Descartes, 1985b ), humans can know only correlations between appearances.

Cartesian description of cause contains terms and ideas that we also recognize in modern statistical data analysis. Here we find: independent and dependent variables; we find an idea of linear (or at least continuous) relationships – correlations; the idea that effects can be understood by knowing (efficient) causes – dependent variables or variability is statistically “ explained ,” etc. There are two noteworthy ideas more. First, the notion of “relationship” has only one meaning, that between cause and effect; no other kind of relationship is important. And second, there is no suggestion that qualitatively novel wholes emerge from the synthesis of parts. This idea is also similar to quantitative thinking in modern psychology. The overlap between Cartesian philosophy and modern quantitative epistemology, I suggest, is not just a coincidence; it reflects fundamental agreement between Cartesian causality and modern quantitative approaches to science.

Hume and efficient causality

Slightly different approach to causality, even though similar to Cartesian in looking for efficient causality only, was taken by Hume. According to him,

Similar objects are always conjoined with similar. Of this we have experience. Suitably to this experience, therefore, we may define ac cause to be an object, followed by another, and where all the objects, similar to the first, are followed by objects similar to the second . Or in other words, where, if the first object had not been, the second never had existed (Hume, 2000 , pp. 145–146).

So, cause is an object which appearance is related to the appearance of the other object. Space limitations do not allow going into detailed description of Hume's ideas. So I only mention them together with references to specific parts in his works where the corresponding ideas have been expressed by him. First, the relationship between causes and effects is characterized by contiguity (Hume, 2000 , p. 54). Second, causes relative to effects have priority in time; cause must precede the effect (Hume, 2000 , p. 54). Third, the number of causes is smaller than the number of effects; therefore many observations of effects can be reduced to a few identified causes (Hume, 2000 , p. 185). Fourth, the relationship between cause and effect reflects only relationships between appearances; no conclusion about reality that necessarily underlies the connection can be made (Hume, 1999 , p. 136). Therefore conclusions about relations between causes and effects concern only matter of fact; they concern only the existence of objects or of their qualities (Hume, 2000 , p. 65). Finally, according to Hume, the relationship between causes and effects is only probable (Hume, 1999 , p. 115). The more often we observe an effect following the cause and the less often we observe effect not following the cause, the stronger is the impression of causality between the observed events (Hume, 2000 , p. 105).

Taken together, it turns out that Humean epistemology is practically identical with the modern quantitative science – in both the succession of continuous events ground impressions about cause–effect relationships that can be observed with some probability; in both the impression of causal connection is perceived stronger when the proportion of observations that agree with one direction of events (from the supposed cause to the supposed effect) is higher than the proportion of observations that disagrees with this assumed direction of relationship; in both there can be no evidence that absolutely disagrees with some hypothetical causal relationship because cause–effect relationships can be observed only in degrees and not in necessary all-or-none relationship; and in both it is assumed that large number of observations can be “explained” by knowing small number of causes.

There is one interesting correspondence more between Humean epistemology and modern quantitative psychology. According to Hume, discovery of cause–effect relationships is based not on deductions or thinking but on “some instinct or mechanical tendency” (Hume, 1999 , p. 130). The same can be said about quantitative science – the ways by which man-made causes (if to use Thurstone's words) are discovered, are highly mechanical. There are algorithms that are strictly followed in calculations of probabilities, effect sizes, and all other statistical descriptors of the variables in the analyses; there is no adjustment of each particular case of study to particular statistical calculations, for instance. In scientific inquiry, mechanization leads to dead end because it puts constraints on what can be understood in principle.

There is yet one point where the scholars who introduced statistical data analysis into sciences agreed with Hume but the modern researchers tend (at least implicitly) to disagree. It was already discussed above that both Pearson and Thurstone were fully aware that statistical theories are about appearances, about relationships between observed events; no conclusion can be made about the essence of the reasons why the statistical relationships between variables emerge. Hume had identical understanding of the state of affairs – efficient causality is about appearances and not about what he called “secret powers” that underlie the observed relationships:

It must certainly be allowed, that nature has kept us at a great distance from all her secrets, and has afforded us only the knowledge of a few superficial qualities of objects; while she conceals from us those powers and principles, on which the influence of these objects entirely depends. […] there is no known connection between the sensible qualities and the secret powers (Hume, 1999 , pp. 113–114).

Here modern quantitative psychology seems to disagree – on the basis of different kinds of statistical data analyses often conclusions are made about exactly those “secret powers.” Psychologists today attribute often the statistically “discovered” causes not just to man-made generalizations that leave an impression of causality but rather directly to “secret powers,” to mental attributes that are supposed to underlie the behaviors. It is ignored that behavior is not in one-to-one correspondence with psychic reality that underlies the behavior; externally identical behaviors may emerge from mentally qualitatively different operations and vice versa. So, all quantitative theories are only about appearances and not about underlying mechanisms because quantification of data into variables already excludes the information that is necessary for discovering the mechanisms that underlie observed covariations.

Why only efficient causality?

Aristotelian causality distinguished four complementary causes; Descartes and Hume, nevertheless, proposed only one. It is also important that neither Descartes nor Hume proposed entirely new concepts of causality; they took one Aristotelian cause out of his four. The reasons why they treated causality only in terms of efficient causes are relevant here.

It was already discussed above that, according to Descartes, understanding of causality is about correlations between observed events; correlations do not imply necessity – every appearance can be correlated with every other appearance in principle. For Aristotle, causes were essentially constraints – in order to make a statue, bronze is used; there are many substances out of which it is not possible to make statues. If things have been made according to plan, then plan constrained the possible course of events; the result did not come out by accident or by chance but was constrained by plan before the event took place. Descartes, in order to be coherent with his philosophy, could not accept any kind of cause as a constraint.

Descartes believed in God, and not just some God, but God who is “infinite, eternal, immutable, omniscient, omnipotent […] all the perfections which I could observe to be in God.” (Descartes, 1985a , p. 128). Therefore, logically, there can be no causes that are constraints because God has no constraints; God is omnipotent. God is the First Cause of everything that is. Effects follow from cause by necessity in principle because effects follow from God's omnipotence. Humans, however, cannot know necessity that relates causes to effects; for them only knowledge about correlations is available:

When dealing with natural things we will, then, never derive any explanations from the purposes which God or nature may have had in view when creating them. For we should not be so arrogant as to suppose that we can share in God's plans. We should, instead, consider him as the efficient cause of all things, and starting from the divine attributes which by God's will we have some knowledge of, we shall see, with the aid of our God-given natural light, what conclusions should be drawn concerning those effects which are apparent to our senses (Descartes, 1985b , p. 202).

Taken together, humans can only know what is given for them through senses – appearances and correlations of them; they cannot know reasons that connect causes to effects because they are imperfect. Correlations do not allow going beyond observations of events; there is no way to know what are the reasons for observed correlations but one – God's will.

For Hume, too, efficient causality was not related to necessity: “tis possible for all objects to become causes or effects to each other […]” (Hume, 2000 , p. 116). If there is no necessary relationship between events then it is not possible to know, why the events are related because it is actually not even possible to prove that the events are related essentially and not by accidents or by mistakes of observation. But his reasons for this view were different from Descartes’. Hume suggested that God is not knowable in principle and therefore the idea of God should not be taken into account in philosophy.

Hume suggested, similarly with Descartes, that humans have no access to knowledge beyond appearances; they cannot know why observed causes are related to observed effects. Human knowledge is actually even more limited – it is also not possible to be sure in discovered laws; the laws of nature can change and what we thought to be a cause may turn out to be the effect, or no connection between events would be discovered eventually (Hume, 1999 , p. 115). The reasons of human limitations of understanding the world lied for Hume in limitations of the human (and animal) mind; world is not knowable beyond appearances because the mind is unable to go beyond appearances. Here Hume's psychology becomes central for understanding his views. According to him, the mind works only on the principles of association:

[…] principles of association […] To me, there appear to be only three principles of connection among ideas, namely, Resemblance, Contiguity in time or place, and Cause or Effect . […] But the most usual species of connection among the different events, which enter into any narrative composition, is that of cause and effect; (Hume, 1999 , pp. 101–103).

So, the only operation available for mind is to form associations between observed events as they appear to us. If this would be the case, then Humean rejection of the possibility to have knowledge beyond senses – his proposition that only efficient causes as they appear to us can be known – would be well grounded. Humean psychology, however, was acceptable in his time, but not any more. The inability of associationism to be sufficient for explaining the human mind was established and grounded with empirical studies almost a century ago. Not only humans, but even apes were demonstrated to be able to think in a way that is not based on associations alone (Köhler, 1925 ; see also Koffka, 1935 ; Köhler, 1959 ; Vygotsky, 1982b ). The idea that animal mind is based only on reflexes and conditioned reflexes, discovered by Pavlov ( 1927 , 1951 ) was actually rejected by scholars from his own laboratory (Anokhin, 1975 ; Konstantinov et al., 1978 ).

Modern quantitative psychology – mix of two incompatible epistemologies

Psychology today often aims at understanding structures that underlie observed behaviors. This aim is borrowed from that Aristotelian structural-systemic epistemology. Methods chosen for studies, however, are based on Cartesian–Humean cause–effect epistemology. Both philosophers who limited understanding of causality to efficient causality – Descartes and Hume – and scholars who introduced quantitative methods into sciences – Pearson and Thurstone – agreed that method of associating events by contiguity and covariation cannot ground interpretations in terms of underlying necessary reasons that connect observed causes to observed effects. They all also agreed that what is represented in observed associations between events or variables is subject to doubt. Interpretation of those associations can be only weaker or stronger depending on the relative frequency of events that correspond to certain idea of causality to the frequency of observations that contradict it. Laws discovered by such procedures are therefore not absolute but relative; laws cannot be refuted by observations that contradict it – in psychology effect sizes 1.0 are practically never observed; it is actually conveniently accepted that far-going conclusions can be made when 10–30% of data variability is statistically “explained.” It is ignored that in such situations substantial number of cases disagrees completely with the conclusions of the study. After conducting some “meta-analysis” it often turns out that the laws of association discovered in different studies contradict; and a new law can be proposed to replace those from the analyzed studies. It would not become a surprise when some meta-meta-analysis would yet lead to different generalization. Laws, in this epistemology, are not absolute; they can change without destroying the theory that is built from the collection of associative generalizations. Some philosophers would suggest that this kind of activity is not what science should do:

For it is an important postulate of scientific method that we should search for laws with an unlimited realm of validity. If we were to admit laws that are themselves subject to change, change could never be explained by laws. It would be the admission that change is simply miraculous. And it would be the end of scientific progress; for if unexpected observations were made, there would be no need to revise our theories: the ad hoc hypothesis that the laws have changed would “explain” everything (Popper, 2002 , p. 95).

Statistical methods in psychology are useless

Taken together, there are reasons to suggest that quantitative methods are useless for psychology – IF the aim of psychology is to develop knowledge about mind, about “secret powers” that underlie observed behaviors. Such understanding would require qualitative approaches that allow distinguishing between externally similar behaviors based on internally different mental processes; and between externally different behaviors that are based on similar mechanisms.

Modern quantitative psychology is based on the epistemology where the questions are asked about efficient causality; explanation is reduced to identification of cause–effect relationships between events. Such approach could be fully consistent if it would be accepted – as did Thurstone and Pearson – that discovery of such relationships cannot be connected to underlying structures in principle. Modern quantitative psychology, however, takes methods from Cartesian–Humean efficient causality epistemology and aims from incompatible with it Aristotelian-structural epistemology. Structural-systemic description of the studied phenomena cannot be based on quantitative methodology. The histories of biology or chemistry which are based on systemic-structural epistemology, also shows that majority of discoveries in these sciences have been made without statistical methods.

Statistical methods in psychology are inevitable

The suggestion to reject quantitative methodology, I made, is conditional – IF the aims of studies would correspond to methods, quantitative methodology would turn out to be extremely valuable, almost inevitable … for applied psychology. Now we need to turn the discussion upside-down. Instead of asking what cannot be accomplished with quantitative methods we ask, what it can bring to us? The world around us is constantly changing and always unique. How to live in the world of unique events? This would be impossible – in order to live, all life-forms must be able to react to future changes of the environment before these changes actually take place (Anokhin, 1978 ; Toomela, 2010a ); foresight must be based on generalization and abstraction.

Coherent systemic-structural theories, as modern applications of physics, chemistry, and biology amply demonstrate, are extremely practical. But how to behave if the theory about underlying processes has not been created yet? Here quantitative methods become valuable: it is possible to create useful generalizations without knowing the processes that underlie the events. This was exactly what Thurstone, for instance, aimed at:

It is the faith of all science that an unlimited number of phenomena can be comprehended in terms of a limited number of concepts or ideal constructs. Without this faith no science could ever have any motivation. To deny this faith to affirm the primary chaos of nature and the consequent futility of scientific effort (Thurstone, 1935 , p. 44).

Thurstone, as we saw above, aimed explicitly and only at discovering ways to comprehend nature by describing regularities among observed events; these discoveries would be just man-made schemes, and yet they would help to manage otherwise unmanageable amount of information. A lot could be learned in this way – it would be possible to discover behaviors that should be avoided and behaviors that should be repeated in appropriate conditions – and all this without necessarily knowing, why. Until systemic-structural theories replace associative quantitative theories, psychology can create increasingly strong ground to applied uses of it. Quantitative science is inevitable for applied purposes until a theory about structures that underlie behavior is sufficiently developed for grounding applied uses. If, however, quantitative science continues to look for what it cannot find – the “secret powers” – then it ends up where Hume warned us not to go:

We are got into a fairy land, long ere we have reached the last steps of our theory […] (Hume, 1999 , p. 142).

Some notes on mathematical psychology in general

Mathematical psychology is not based exclusively on statistical methods. Perhaps non-statistical mathematical psychology is better suited for discovering the structure of mind? Indeed, from a certain perspective, it seems that mathematical psychology is doing well – there are fields of studies where mathematical psychology is prospering: foundational measurement theory, signal detection theory, decision theory, psychophysics, neural modeling, information processing approach, and learning theory (Townsend, 2008 ). Sometimes it almost seems that the only true science is based on mathematics; so Townsend suggests that psychology undergraduate training should change toward “solid-science” education and in order to do that, “The only practical solution I can espy is for psychology departments to offer a true scientific psychology track , with mandatory courses in the sciences, mathematics and statistics ” (Townsend, 2008 , p. 275, my emphasis).

A small problem can be that achievements of mathematics, such as axiomatic measurement theory and computer-based, non-metric model fitting techniques, do not have an impact on psychology these “revolutions” deserve (Cliff, 1992 ). It might be that many problems will be solved with some developments in mathematics which, for instance, would explicate relationships between ways of describing randomness and ways of describing structure (Narens and Luce, 1993 ; Luce, 1999 ). It might be, however, that mathematics as such is inappropriate for answering questions psychology aims at answering. The most fundamental issue is not how mathematics should be applied in psychology but rather whether it can be applied for answering the core question of the science of psyche – what is mind? No development in any kind of measurement theory, for instance, will be helpful if psychological attributes cannot be measured in principle; there are strong reasons to suggest that they are not indeed (Valsiner, 2005 ; Trendler, 2009 ; Michell, 2010 ).

In order to proceed, a definition of mathematics is needed. According to Luce ( 1995 , p. 2):

Mathematics studies structures and patterns described by systems of propositions relating aspects of entities in question. Deriving logically true statements from sets of assumed statements (often called axioms), uncovering symmetries and patterns, and evolving and understanding general structures are the concerns of mathematicians.

It is noteworthy that the term “structure” does not apply directly to the things and phenomena studied by physics, biology, psychology, or any other science. Rather, mathematics studies descriptions of objects and phenomena – systems of propositions – and “structure” refers to the system of descriptions; in that sense mathematics is an abstract science (Veblen and Young, 1910 ); it is a body of theorems deduced from a set of axioms (Veblen and Whitehead, 1932 ).

It is important that, as an abstract science, mathematics is based on assumptions, its “starting point” is

a set of undefined elements and relations , and a set of unproven propositions involving them ; and from these all other propositions (theorems) are to be derived by the methods of formal logic (Veblen and Young, 1910 , p. 1, my emphasis).

So, mathematics is a system of propositions that begins with a set of undefined assumptions, called axioms or postulates; and there are rules of deduction or a system of logic. Thus, mathematics defines a priori certain principles which are not derived from the studies of the world but attributed to it before studies. Mathematical description of the concrete real-world phenomena is successful only if the concrete system of things satisfies the fundamental assumptions of mathematics (Veblen and Young, 1910 ). Even though axioms can be postulated on the basis of scientific studies of the world and added to the basic set of abstract axioms, the abstract basis of mathematics is nevertheless determined before the studies. Taken together, it can be said that mathematics does not study the world but rather searches for events where the world corresponds to abstract mathematical principles – principles that cannot be proven or even defined.

Mathematics studies only formal aspects of the world. For Poincare, “mathematics is the art of giving the same name to different things” (Poincare, 1914 , p. 34; also: “Mathematics teaches us, in fact, to combine like with like,” Poincare, 1905 , p. 159). Similarities of things can be discovered by studying relationships:

Mathematicians do not study objects, but the relations between objects; to them it is a matter of indifference if these objects are replaced by others, provided that the relations do not change. Matter does not engage their attention, they are interested by form alone (Poincare, 1905 , p. 20).

Thus, similarities of things discovered by mathematical studies are purely mathematical – things are similar if the mathematical relationships that describe them formally are similar. And the essence of these mathematical relationships, we saw above, is defined a priori and not derived from scientific studies.

Here are the reasons why not only statistics but any mathematical approach – if mathematics is defined as described above – is unable to reveal the structure of the things and phenomena studied; mathematics cannot in principle answer the questions structural-systemic science aims to answer – what is the studied thing or phenomenon? First, in the world, externally similar things and phenomena can be based on different underlying structures; for mathematics these structural differences do not exist. If, for instance, in a similar threatening situation one person reacts aggressively because he has made a conscious choice and the other impulsively, then for mathematics these two reactions are the same even though in the first case psychological structure included rational processes and in the other it did not. Mathematical prediction of future in such case cannot be very accurate because a person who chooses rationally to react aggressively in certain situations is also able to control his reactions whereas impulsive behavior is directed by the situation. This problem of mathematics has been recognized by mathematicians themselves; Poincare, for instance, suggested:

It is not enough that each elementary phenomenon should obey simple laws: all those that we have to combine must obey the same law ; then only is the intervention of mathematics of any use. […] It is therefore, thanks to the approximate homogeneity of the matter studied by physicists, that mathematical physics came into existence. In the natural sciences the following conditions are no longer to be found: – homogeneity, relative independence of remote parts, simplicity of the elementary fact; and that is why the student of natural science is compelled to have recourse to other modes of generalisation (Poincare, 1905 , pp. 158–159, my emphasis).

In fact, Trendler ( 2009 ) proposed essentially the same reason why psychological attributes cannot be measured – in case of psychological attributes there are too many sources of systematic errors that cannot be controlled experimentally; in other words – psychological attributes are not independent but depend on each other. Therefore they cannot be measured.

Next, mathematics is a secondary science; the successful application of mathematics to the phenomena of the world depends on the experiments conducted in other sciences:

Experiment is the sole source of truth. It alone can teach us something new; it alone can give us certainty (Poincare, 1905 , p. 140).

Mathematics can help to organize the results of experiments; it can direct generalization; but it does not provide any new knowledge (Poincare, 1905 ). There are two different problems here. One problem is related to the essence of generalization. In mathematics, generalization is related to relationships between events, but in order to understand what a thing is , it is not sufficient to know with what else it can be related. In principle, there is no constraint on the number of other things with which any given thing in the world can be related; but what the thing is, is defined qualitatively and is fully constrained. A thing is what it is. Mathematical generalizations are not useful for discovering what things are. Another problem with mathematical generalizations can also be related to Poincare's discussion of the role of mathematics in sciences. According to him,

It is clear that any fact can be generalised in an infinite number of ways, and it is a question of choice. The choice can only be guided by considerations of simplicity (Poincare, 1905 , p. 146).

In case of phenomena that can be – in psychology they are – externally similar but yet based on qualitatively different psychic structures, the assumption of simplicity is not just wrong – it is fundamentally misleading. The assumption of simplicity forces scientists who rely on mathematics to ignore what should be studied and understood – complexity. Parenthetically, it should be mentioned here that mathematical models can be extremely complex; but they are fundamentally oversimplified if there is an assumption that externally similar events are all based on similar structures.

Third, mathematics can model only what is given by experiments and observations conducted in other sciences. It follows that mathematics is not able to provide any understanding of becoming, of emergence of qualitatively novel things. If these other sciences have described the emergence of novel qualities, this emergence can be modeled mathematically in principle. But again, what is modeled is not the novel thing or phenomenon itself – that model would be structural description of the thing or phenomenon – but relationships between events, i.e., what is modeled mathematically is always external to the thing itself. Here mathematicians perhaps could object and suggest that structural theory is also mathematical model. This suggestion, however, would require fundamental redefinition of what mathematics is; because structural model based on studies of things, such as a model of atom, gene, wristwatch, or mind, is not based on a set of a priori given assumptions-axioms but rather on studies of the world. As Poincare suggested, science can be based on different kinds of generalizations and mathematical generalization that fits so well into physics, is considerably less useful in other sciences. I will discuss briefly the methods of scientific generalization in the next section.

Altogether, there are reasons to suggest that mathematics is not appropriate tool if the aim of science is to understand what the studied thing or phenomenon is. For mathematical psychologist, naturally, mathematics is almost the most important tool for science:

Mathematical psychology has arguably accelerated the evolution of psychology and allied disciplines into rigorous sciences many times over their likely progress in its absence. Let's nurture and strengthen it (Townsend, 2008 , p. 279).

I suggest that mathematics has actually had the opposite role for psychology – it has oversimplified theories, blinded scientists, and directed their attention to the study of relationships between things and phenomena instead of guiding them to study what these things and phenomena are. Physics has been most successful not where something can be exactly calculated but where the theory has defined what the things are, atoms, for instance. Yes, mathematics is useful tool here and there – as it can be also for psychology for grounding practical decisions – but no machine has been ever built on the basis of mathematical formulas alone whereas many of them have been constructed completely without any aid from mathematics. The same can be said about biology – it is very powerful science not because of applications of mathematics in some peripheral matters of biology but because of the theories about what are cells, components of cells, organs, organisms, etc. Perhaps what should be “nurtured and strengthened” in psychology is not mathematical psychology but studies that aim to understand what mind is.

If Not Mathematics, Then What?

So, mathematics is a useful tool for generalizations about relationships between events. The value of mathematics, though, is not the same in all sciences. Poincare, for instance, suggested that mathematics is very useful tool for generalization of the results of experiments in physics:

It might be asked, why in physical science generalisation so readily takes the mathematical form. The reason is now easy to see. It is not only because we have to express numerical laws; it is because the observable phenomenon is due to the superposition of a large number of elementary phenomena which are all similar to each other; and in this way differential equations are quite naturally introduced (Poincare, 1905 , p. 158).

He also suggested, as was discussed above, that mathematical generalization is appropriate only in cases when the matter studied by the scientists is homogeneous; when parts are relatively independent and elementary facts are simple. These conditions are not met in psychology. Therefore another way for generalization must be found. Another way for scientific generalization is a special kind of experiment.

Usually it is thought that there is one kind of experiments. This kind is Cartesian–Humean; the question answered in the experiment is whether certain event is or is not an efficient cause of another event. In order to answer this question, the artificial situation is created where, ideally, all conditions are kept equal but one that is manipulated or “controlled.” If the expected effect follows when manipulated event is present and does not follow when the manipulated event is absent, then it is concluded that the cause of the event has been identified.

There is, however, another kind of experiment that, to the best of my knowledge, was first brought into the theory of scientific experimentation by Engels (even though in several respects similar idea can be found already in Aristotle’ s works; cf. Aristotle, 1941a , p. 690, Bk.I, 981 a –981 b ). Engels discussed the role of induction in scientific discoveries and proposed that there is much more powerful way for scientific proofs than induction:

A striking example of how little induction can claim to be the sole or even the predominant form of scientific discovery occurs in thermodynamics: the steam-engine provided the most striking proof that one can impart heat and obtain mechanical motion. 100,000 steam-engines did not prove this more than one , but only more and more forced the physicists into the necessity of explaining it. […] The empiricism of observation alone can never adequately prove necessity. Post hoc but not propter hoc . […] But the proof of necessity lies in human activity, in experiment, in work: if I am able to make the post hoc , it becomes identical with the propter hoc . (Engels, 1987 , pp. 509–510)

This kind of experiment that follows from the principles outlined by Engels, I have called “constructive” (Toomela, in press-a ). In constructive experiments it is attempted to create the thing or phenomenon that is studied. If the phenomenon or thing can be constructed on the basis of knowledge about hypothetical elements and specific relationships between them, the experiment has provided corroborating evidence for the theory. Here the result of the experiment – constructed thing or phenomenon – follows from theory. It is important that there is no logical necessity that a whole with certain emergent properties must emerge when theoretically defined elements are put into certain relationships. Instead of logical deduction, the necessity is proven in the real construction of the phenomenon that is attempted to understand. Mathematics derives logically true statements from assumptions that cannot be proven. It is important that the truth of logical derivations depends fully on the truth of the assumptions. If even one assumption cannot be proven, there is no proof possible for the scientific theory as a whole as well. In constructive experiments, on the other hand, the proof is obtained by actual construction of the studied thing. Such actual construction does not contain any assumptions that cannot be proven – these assumptions are proven by the result of the experiment.

Constructive experiments can be found in different fields of science. Atomic theory is well corroborated by the construction of the nuclear bombs and reactors; chemical theories are corroborated with the synthesis of new molecules. Just now biologists have reported a major breakthrough in constructive experiments on the cells; a bacterial cell with chemically synthesized genome has been created (Gibson et al., 2010 ). There are also examples of constructive experiments in psychology. Neuropsychological rehabilitation based on Luria's theory is grounded on structural-systemic theory; numerous psychological functions have been artificially created with special educational programs that were designed on the basis of theories about the elements and relationships of elementary psychological processes of those complex functions (Luria, 1948 ; Tsvetkova, 1985 ).

Taken together, mathematics is not useful for discovering what things are. For such discoveries, observations and analytic experiments should be combined with constructive experiments.

Conclusions

Science begins with the question, what do I want to know? Science becomes science, however, only when this question is justified and the appropriate methodology is chosen for answering the research question. Research question should precede the other questions; methods should be chosen according to the research question and not vice versa. Modern quantitative psychology, though, has accepted method as primary and research questions are adjusted to the methods. It would not be a problem if methods would fit the questions about the studied phenomena; but they do not. The crucial question that needs to be asked, is – do the answers to the questions what, why, and how I want to know, make a coherent theoretically justified whole? All psychology that aims at understanding the structure of mind with any kind of mathematical tools has to admit that the methods do not correspond to the study questions.

For understanding thinking in modern quantitative psychology, two epistemologies should be distinguished: structural-systemic that is based on Aristotelian thinking; and associative-quantitative that is based on Cartesian–Humean thinking. The first aims at understanding the structure that underlies the processes leading to events observed in the world, the second looks for identification of apparent cause–effect relationships between the events with no claim made about processes that underlie the appearances.

Quantitative methods are useful for generalizations about the relationships between things and events. What the studied things or phenomena are cannot be revealed by such methods. Structural-systemic science, which aims at understanding structures, relies on qualitative methodology that includes, in addition to the observations and analytic experiments, constructive experiments.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1 I am not going here into philosophical question whether such full correspondence can be known in principle. I agree that we can never be sure that out theories are correct (cf., e.g., Engels, 1996 ; Kant, 2007 ). I only suggest that theories can be in full correspondence with the physical reality even though we cannot demonstrate it.

• Anokhin P. K. (1975). Ocherki po fiziologii funktsional'nykh sistem . Moscow: Medicina [ Google Scholar ]
• Anokhin P. K. (1978). “Operezhajuscheje otrazhenije deistvitel'nosti,” (Anticipating reflection of actuality. In Russian. Originally published in 1962.) . in Filosofskije aspekty teorii funktsional'noi sistemy , eds Konstantinov F. V., Lomov B. F., Schvyrkov V. B., Anokhin P. K. Izbrannyje trudy. (Moscow: Nauka; ), pp. 7–26 [ Google Scholar ]
• Aristotle (1941a). “Metaphysics,” (Metaphysica.) . in The Basic Works of Aristotle , ed. McKeon R. (New York: Random House; ), pp. 681–926 [ Google Scholar ]
• Aristotle (1941b). “Organon,” in The Basic Works of Aristotle , ed. McKeon R. (New York: Random House; ), pp. 7–212 [ Google Scholar ]
• Aristotle (1941c). “Physics,” (Physica .). in The Basic Works of Aristotle , ed. McKeon R. (New York: Random House; ), pp. 213–394 [ Google Scholar ]
• Boker S. M., Molenaar P. C. M., Nesselroade J. R. (2009). Issues in intraindividual variability: individual differences in equilibria and dynamics over multiple time scales . Psychol. Aging 24 , 858–862 10.1037/a0017912 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chambers E. (1728a). Cyclopædia, or, An universal dictionary of arts and sciences . In two volumes. Volume the first. London: Ephraim Chambers Retrieved 21 July 2009 from http://digicoll.library.wisc.edu/HistSciTech/subcollections/CyclopaediaAbout.html
• Chambers E. (1728b). Cyclopædia, or, An universal dictionary of arts and sciences . In two volumes. Volume the Second. London: Ephraim Chambers Retrieved 21 July 2009 from http://digicoll.library.wisc.edu/HistSciTech/subcollections/CyclopaediaAbout.html
• Cliff N. (1992). Abstract measurement theory and the revolution that never happened . Psychol. Sci. , 3 , 186–190 10.1111/j.1467-9280.1992.tb00024.x [ CrossRef ] [ Google Scholar ]
• Descartes R. (1985a). “Discourse on the method of rightly conducting one's reason and seeking the truth in the sciences,” (Originally published in 1637) in The Philosophical Writings of Descartes , Vol. 1 , eds Cottingham J., Stoothoff R., Murdoch D. (New York: Cambridge University Press; ), pp. 111–151 [ Google Scholar ]
• Descartes R. (1985b). “Principles of philosophy,” (Originally published in 1644 in Latin and in 1647 in French) in The Philosophical Writings of Descartes , Vol. 1 , eds Cottingham J., Stoothoff R., Murdoch D. (New York: Cambridge University Press; ), pp. 179–291 [ Google Scholar ]
• Descartes R. (1985c). “Rules for the direction of the mind,” (Originally written in about 1628, published in 1684) in The Philosophical Writings of Descartes , Vol. 1 , eds Cottingham J., Stoothoff R., Murdoch D. (New York: Cambridge University Press; ), pp. 9–78 [ Google Scholar ]
• Engels F. (1987). “Dialectics of nature,” in Collected Works , Vol. 25 , eds Marx K., Engels F. (New York: International Publishers; ), pp. 313–590 [ Google Scholar ]
• Engels F. (1996). Ludwig Feuerbach and the End of Classical German Philosophy (Originally published in 1888) . Beijing: Foreign Language Press [ Google Scholar ]
• Epstein S. (1980). The stability of behavior. II. Implications for psychological research . Am. Psychol. 35 , 790–806 10.1037/0003-066X.35.9.790 [ CrossRef ] [ Google Scholar ]
• Essex C., Smythe W. E. (1999). Between numbers and notions. A critique of psychological measurement . Theory Psychol. 9 , 739–767 10.1177/0959354399096002 [ CrossRef ] [ Google Scholar ]
• Gibson D. G., Glass J. I., Lartigue C., Noskov V. N., Chuang R.-Y., Algire M. A., Benders G. A., Montague M. G., Ma L., Moodie M. M., Merryman C., Vashee S., Krishnakumar R., Assad-Garcia N., Andrews-Pfannkoch C., Denisova E. A., Young L., Qi Z.-Q., Segall-Shapiro T. H., Calvey C. H., Parmar P. P., Hutchison C. A., III, Smith H. O., Venter J. C. (2010). Creation of a bacterial cell controlled by a chemically synthesized genome . Science 329 , 52–56 10.1126/science.1190719 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gigerenzer G. (1991). From tools to theories: a heuristic of discovery in cognitive psychology . Psychol. Rev. 98 , 254–267 10.1037/0033-295X.98.2.254 [ CrossRef ] [ Google Scholar ]
• Gigerenzer G. (1993). “The superego, the ego, and the id in statistical reasoning,” in A Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues , eds Keren G., Lewis C. (Hillsdale, NJ: Lawrence Erlbaum Associates; ), pp. 311–339 [ Google Scholar ]
• Hamaker E. L., Dolan C. V., Molenaar P. C. M. (2005). Statistical modeling of the individual: rationale and application of multivariate stationary time series analysis . Multivariate Behav. Res. 40 , 207–233 10.1207/s15327906mbr4002_3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hume D. (1999). “An enquiry concerning human understanding,” (Originally published in 1748) in An Enquiry Concerning Human Understanding , ed. Beauchamp T. L. (Oxford: Oxford University Press; ), pp. 81–211 [ Google Scholar ]
• Hume D. (2000). “A treatise of human nature,” (Originally published in 1739–1740) in A Treatise of Human Nature , eds Norton D. F., Norton M. J. (Oxford: Oxford University Press; ), pp. 1–418 [ Google Scholar ]
• Kant I. (2007). “Critique of pure reason,” (Originally published in 1787) in Critique of Pure Reason . Immanuel Kant. Revised 2nd Edn., ed. Smith N. K. (New York: Palgrave McMillan; ). [ Google Scholar ]
• Koffka K. (1935). Principles of Gestalt Psychology . London: Routledge & Kegan Paul [ Google Scholar ]
• Köhler W. (1925). The Mentality of Apes . New York: Harcourt, Brace & Co. [ Google Scholar ]
• Köhler W. (1959). Gestalt Psychology. An Introduction to New Concepts in Modern Psychology . New York: Mentor Books [ Google Scholar ]
• Konstantinov F. K., Lomov B. F., Shvyrkov B. V. (eds). (1978). P. K. Anokhin. Izbrannyje trudy. Filosofskije aspekty teorii funktsional'noi sistemy . Moscow: Nauka [ Google Scholar ]
• Lewin K. (1935). A Dynamic Theory of Personality. Selected Papers . New York: McGraw-Hill [ Google Scholar ]
• Luce R. D. (1995). Four tensions concerning mathematical modeling in psychology . Annu. Rev. Psychol. 46 , 1–26 10.1146/annurev.ps.46.020195.000245 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luce R. D. (1999). Where is mathematical modeling in psychology headed? Theory Psychol. 9 , 723–737 10.1177/0959354399096001 [ CrossRef ] [ Google Scholar ]
• Luria A. R. (1948). Vosstanovlenije funkcii mozga posle vojennoi travmy . (Restoration of Brain Functions After War Trauma. In Russian). Moscow: Izdatel'stvo Akademii Medicinskih Nauk SSSR [ Google Scholar ]
• Michell J. (2000). Normal science, pathological science and psychometrics . Theory Psychol. 10 , 639–667 10.1177/0959354300105004 [ CrossRef ] [ Google Scholar ]
• Michell J. (2010). “The quantity/quality interchange: a blind spot on the highway of science,” in Methodological Thinking in Psychology: 60 Years Gone Astray? eds Toomela A., Valsiner J. (Charlotte, NC: Information Age Publishing; ). [ Google Scholar ]
• Molenaar P. C. M. (2004). A Manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever . Measurement 2 , 201–218 [ Google Scholar ]
• Molenaar P. C. M., Valsiner J. (2005). How generalization works through the single case: a simple idiographic process analysis of an individual psychotherapy . Int. J. Idiographic Sci. Article 1. Retrieved October 25 2005 from http://www.valsiner.com/ articles/molenvals.htm [ Google Scholar ]
• Narens L., Luce R. D. (1993). Further comments on the “nonrevolution” arising from axiomatic measurement theory . Psychol. Sci. 4 , 127–130 10.1111/j.1467-9280.1993.tb00475.x [ CrossRef ] [ Google Scholar ]
• Nesselroade J. R., Gerstorf D., Hardy S. A., Ram N. (2007). Idiographic filters for psychological constructs . Measurement 5 , 217–235 [ Google Scholar ]
• Pavlov I. P. (1927). Lekcii o rabote bol'shikh polusharii golovnogo mozga . (Lectures on the work of the hemispheres of the brain.). Moscow: Gosudarstvennoje Izdatel'stvo [ Google Scholar ]
• Pavlov I. P. (1951). Dvatcatilet'nii opyt ob'jektivnogo izuchenija vyshei nervnoi dejatel'nosti (povedenija zhivotnykh) . Moscow: Medgiz [ Google Scholar ]
• Pearson K. (1896). Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia . Philos. Trans. R. Soc. Lond. A 187 , 253–318 10.1098/rsta.1896.0007 [ CrossRef ] [ Google Scholar ]
• Pearson K. (1899). Mathematical contributions to the theory of evolution. V. On the reconstruction of the stature of prehistoric races . Philos. Trans. R. Soc. Lond. 192 , 169–244 10.1098/rsta.1899.0004 [ CrossRef ] [ Google Scholar ]
• Pearson K. (1902). On the systematic fitting of curves to observations and measurements . Biometrika , 1 , 265–303 10.1093/biomet/1.3.265 [ CrossRef ] [ Google Scholar ]
• Pearson K. (1903–1904). Mathematical contributions to the theory of evolution. XII. On a generalised theory of alternative inheritance, with special reference to Mendel's laws . Proc. R. Soc. Lond. 72 , 505–509 10.1098/rspl.1903.0081 [ CrossRef ] [ Google Scholar ]
• Pearson K. (1904). Mathematical contributions to the theory of evolution. XII. On a generalised theory of alternative inheritance, with special reference to Mendel's laws . Philos. Trans. R. Soc. Lond. A 203 , 53–86 10.1098/rsta.1904.0015 [ CrossRef ] [ Google Scholar ]
• Pearson K., Lee A. (1900). Mathematical contributions to the theory of evolution. VIII. On the inheritance of characters not capable of exact quantitative measurement . Philos. Trans. R. Soc. Lond. A 195 , 79–150 10.1098/rsta.1900.0024 [ CrossRef ] [ Google Scholar ]
• Pearson K., Lee A., Bramley-Moore L. (1899). Mathematical contributions to the theory of evolution. VI. Genetic (reproductive) selection: Inheritance of fertility in man, and of fecundity in thoroughbred racehorses . Philos. Trans. R. Soc. Lond. A 192 , 257–330 10.1098/rsta.1899.0006 [ CrossRef ] [ Google Scholar ]
• Poincare H. (1905). Science and Hypothesis . London: Walter Scott Publishing [ Google Scholar ]
• Poincare H. (1914). Science and Method . New York: Cosimo [ Google Scholar ]
• Popper K. (2002). The Poverty of Historicism . (First English edition published in 1957). London: Routledge Classics [ Google Scholar ]
• Thurstone L. L. (1935). The Vectors of Mind: Multiple-Factor Analysis for the Isolation of Primary Traits . Chicago: University of Chicago Press; 10.1037/10018-000 [ CrossRef ] [ Google Scholar ]
• Thurstone L. L. (1948). Psychological implications of factor analysis . Am. Psychol. 3 , 402–408 10.1037/h0058069 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Toomela A. (2008). Variables in psychology: a critique of quantitative psychology . Integr. Psychol. Behav. Sci. 42 , 245–265 10.1007/s12124-008-9059-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Toomela A. (2009). “How methodology became a toolbox - and how it escapes from that box,” in Dynamic Process Methodology in the Social and Developmental Sciences , eds Valsiner J., Molenaar P., Lyra M., Chaudhary N. (New York: Springer; ), pp. 45–66 [ Google Scholar ]
• Toomela A. (2010a). Biological roots of foresight and mental time travel . Integr. Psychol. Behav. Sci. 44 , 97–125 10.1007/s12124-010-9120-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Toomela A. (2010b). “Modern mainstream psychology is the best? Noncumulative, historically blind, fragmented, atheoretical,” in Methodological Thinking in Psychology: 60 Years Gone Astray? eds Toomela A., Valsiner J. (Charlotte, NC: Information Age Publishing; ), pp. 1–26 [ Google Scholar ]
• Toomela A. (in press-a). “Guesses on the future of cultural psychology: past, present, and past,” in Oxford Handbook of Culture and Psychology , ed. Valsiner J. (Oxford: Oxford University Press; ). [ Google Scholar ]
• Toomela A. (in press-b). “Methodology of idiographic science: limits of single-case studies and the role of typology,” in Yearbook of Idiographic Science , Vol. 2 , eds Salvatore S., Valsiner J., Gennaro A., Travers Simon J. B. (Rome: Firera & Liuzzo Group; ). [ Google Scholar ]
• Townsend J. T. (2008). Mathematical psychology: prospects for the 21st century: A guest editorial . J. Math. Psychol. 52 , 269–280 10.1016/j.jmp.2008.05.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trendler G. (2009). Measurement theory, psychology and the revolution that cannot happen . Theory Psychol . 19 , 579–599 10.1177/0959354309341926 [ CrossRef ] [ Google Scholar ]
• Tsvetkova L. S. (1985). Neiropsikhologicheskaja reabilitatsija bol'nykh. Rech i intellektual'naja dejatel'nost . (Neuropsychological rehabilitation of a sick person. Speech and intellectual activity. In Russian.). Moscow: Izdatel'stvo Moskovskogo Universiteta [ Google Scholar ]
• Valsiner J. (2005). Transformations and flexible forms: where qualitative psychology begins . Qual. Res. Psychol. 4 , 39–57 [ Google Scholar ]
• Veblen O., Whitehead J. H. C. (1932). The Foundations of Differential Geometry. Cambridge Tracts in Mathematics and Mathematical Physics . Cambridge: Cambridge University Press, 29 [ Google Scholar ]
• Veblen O., Young J. W. (1910). Projective Geometry , Vol. I New York: Ginn & Co. [ Google Scholar ]
• Vygotsky L. S. (1982a). “Istoricheski smysl psikhologicheskogo krizisa. Metodologicheskoje issledovanije,” (Historical meaning of the crisis in psychology. A methodological study. Originally written in 1927; First published in 1982) in Sobranije sochinenii. Tom 1. Voprosy teorii i istorii psikhologii , eds Luria A. R., Jaroshevskii M. G. (Moscow: Pedagogika; ), pp. 291–436 [ Google Scholar ]
• Vygotsky L. S. (1982b). “Problema razvitija v strukturnoi psikhologii. Kriticheskoje issledovanije,” (Problem of development in structural psychology. A critical study. Originally published in 1934) in Sobranije sochinenii. Tom 1. Voprosy teorii i istorii psikhologii , eds Luria A. R., Jaroshevskii M. G. (Moscow: Pedagogika; ), pp. 238–290 [ Google Scholar ]

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50+ Research Topics for Psychology Papers

How to Find Psychology Research Topics for Your Student Paper

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

• Specific Branches of Psychology
• Topics Involving a Disorder or Type of Therapy
• Human Cognition
• Human Development
• Critique of Publications
• Famous Experiments
• Historical Figures
• Specific Careers
• Case Studies
• Literature Reviews
• Your Own Study/Experiment

Are you searching for a great topic for your psychology paper ? Sometimes it seems like coming up with topics of psychology research is more challenging than the actual research and writing. Fortunately, there are plenty of great places to find inspiration and the following list contains just a few ideas to help get you started.

Finding a solid topic is one of the most important steps when writing any type of paper. It can be particularly important when you are writing a psychology research paper or essay. Psychology is such a broad topic, so you want to find a topic that allows you to adequately cover the subject without becoming overwhelmed with information.

I can always tell when a student really cares about the topic they chose; it comes through in the writing. My advice is to choose a topic that genuinely interests you, so you’ll be more motivated to do thorough research.

In some cases, such as in a general psychology class, you might have the option to select any topic from within psychology's broad reach. Other instances, such as in an  abnormal psychology  course, might require you to write your paper on a specific subject such as a psychological disorder.

As you begin your search for a topic for your psychology paper, it is first important to consider the guidelines established by your instructor.

Research Topics Within Specific Branches of Psychology

The key to selecting a good topic for your psychology paper is to select something that is narrow enough to allow you to really focus on the subject, but not so narrow that it is difficult to find sources or information to write about.

One approach is to narrow your focus down to a subject within a specific branch of psychology. For example, you might start by deciding that you want to write a paper on some sort of social psychology topic. Next, you might narrow your focus down to how persuasion can be used to influence behavior .

Other social psychology topics you might consider include:

• Prejudice and discrimination (i.e., homophobia, sexism, racism)
• Social cognition
• Person perception
• Social control and cults
• Persuasion, propaganda, and marketing
• Attraction, romance, and love
• Nonverbal communication
• Prosocial behavior

Psychology Research Topics Involving a Disorder or Type of Therapy

Exploring a psychological disorder or a specific treatment modality can also be a good topic for a psychology paper. Some potential abnormal psychology topics include specific psychological disorders or particular treatment modalities, including:

• Eating disorders
• Borderline personality disorder
• Seasonal affective disorder
• Schizophrenia
• Antisocial personality disorder
• Profile a  type of therapy  (i.e., cognitive-behavioral therapy, group therapy, psychoanalytic therapy)

Topics of Psychology Research Related to Human Cognition

Some of the possible topics you might explore in this area include thinking, language, intelligence, and decision-making. Other ideas might include:

• False memories
• Speech disorders
• Problem-solving

Topics of Psychology Research Related to Human Development

In this area, you might opt to focus on issues pertinent to  early childhood  such as language development, social learning, or childhood attachment or you might instead opt to concentrate on issues that affect older adults such as dementia or Alzheimer's disease.

Some other topics you might consider include:

• Language acquisition
• Media violence and children
• Learning disabilities
• Gender roles
• Child abuse
• Prenatal development
• Parenting styles
• Aspects of the aging process

Do a Critique of Publications Involving Psychology Research Topics

One option is to consider writing a critique paper of a published psychology book or academic journal article. For example, you might write a critical analysis of Sigmund Freud's Interpretation of Dreams or you might evaluate a more recent book such as Philip Zimbardo's  The Lucifer Effect: Understanding How Good People Turn Evil .

Professional and academic journals are also great places to find materials for a critique paper. Browse through the collection at your university library to find titles devoted to the subject that you are most interested in, then look through recent articles until you find one that grabs your attention.

Topics of Psychology Research Related to Famous Experiments

There have been many fascinating and groundbreaking experiments throughout the history of psychology, providing ample material for students looking for an interesting term paper topic. In your paper, you might choose to summarize the experiment, analyze the ethics of the research, or evaluate the implications of the study. Possible experiments that you might consider include:

• The Milgram Obedience Experiment
• The Stanford Prison Experiment
• The Little Albert Experiment
• Pavlov's Conditioning Experiments
• The Asch Conformity Experiment
• Harlow's Rhesus Monkey Experiments

Topics of Psychology Research About Historical Figures

One of the simplest ways to find a great topic is to choose an interesting person in the  history of psychology  and write a paper about them. Your paper might focus on many different elements of the individual's life, such as their biography, professional history, theories, or influence on psychology.

While this type of paper may be historical in nature, there is no need for this assignment to be dry or boring. Psychology is full of fascinating figures rife with intriguing stories and anecdotes. Consider such famous individuals as Sigmund Freud, B.F. Skinner, Harry Harlow, or one of the many other  eminent psychologists .

Psychology Research Topics About a Specific Career

​Another possible topic, depending on the course in which you are enrolled, is to write about specific career paths within the  field of psychology . This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most.

In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and the different employment options that are available.

Topics of Psychology Research Involving Case Studies

One potentially interesting idea is to write a  psychology case study  of a particular individual or group of people. In this type of paper, you will provide an in-depth analysis of your subject, including a thorough biography.

Generally, you will also assess the person, often using a major psychological theory such as  Piaget's stages of cognitive development  or  Erikson's eight-stage theory of human development . It is also important to note that your paper doesn't necessarily have to be about someone you know personally.

In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films.

Psychology Research Topics Involving Literature Reviews

Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.

Literature reviews are generally found in the  introduction  of journal articles and other  psychology papers , but this type of analysis also works well for a full-scale psychology term paper.

Topics of Psychology Research Based on Your Own Study or Experiment

Many psychology courses require students to design an actual psychological study or perform some type of experiment. In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results.

Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well as subjects you have studied in the past.

Online sources, newspaper articles, books , journal articles, and even your own class textbook are all great places to start searching for topics for your experiments and psychology term papers. Before you begin, learn more about  how to conduct a psychology experiment .

What This Means For You

After looking at this brief list of possible topics for psychology papers, it is easy to see that psychology is a very broad and diverse subject. While this variety makes it possible to find a topic that really catches your interest, it can sometimes make it very difficult for some students to select a good topic.

If you are still stumped by your assignment, ask your instructor for suggestions and consider a few from this list for inspiration.

• Hockenbury, SE & Nolan, SA. Psychology. New York: Worth Publishers; 2014.
• Santrock, JW. A Topical Approach to Lifespan Development. New York: McGraw-Hill Education; 2016.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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Understanding Naturalistic Observation in Research

This essay is about naturalistic observation, a research method used to observe subjects in their natural environment without interference. It discusses the advantages of this method, such as providing rich, qualitative insights into behavior, and the challenges, including observer bias and lack of control over variables. The essay also touches on ethical considerations and the impact of technological advancements on the effectiveness of naturalistic observation. Examples from various fields like anthropology, ecology, and psychology illustrate the method’s versatility and significance in understanding authentic behaviors in real-world settings.

How it works

Naturalistic observation emerges as a method frequently employed in psychology and the social sciences. This methodology entails the observation of subjects in their native habitat devoid of any manipulation or intrusion by the investigator. The primary objective is to amass data on the behavioral patterns of subjects within authentic settings, proffering insights that may elude capture within a more regimented laboratory milieu. By affording behaviors the latitude to manifest organically, researchers can glean genuine reactions and interchanges, rendering this method invaluable for certain types of inquiries.

An eminent advantage of naturalistic observation lies in its capacity to furnish a nuanced, qualitative comprehension of behavior. For instance, through the observation of juveniles at a recreational area, an investigator can discern not only their play dynamics but also their social dynamics, conflict resolution strategies, and the evolution of their play over time. These observations can subsequently underpin deductions regarding social maturation, aggression, collaboration, and other facets of behavior. Such profundity of insight often eludes attainment through alternative methodologies such as surveys or experiments, wherein the contrived nature of the milieu may exert a sway over the behavior under observation.

Nevertheless, naturalistic observation is not devoid of impediments. One of the principal challenges pertains to the specter of observer partiality. Since the investigator is actively monitoring and documenting behaviors, their own presumptions or convictions may inadvertently color their perceptions and interpretations. To counteract this tendency, researchers frequently deploy strategies such as inter-observer concordance, whereby multiple observers independently record the same occurrence and subsequently compare findings to ascertain congruity. Furthermore, meticulous protocols and training can aid observers in preserving objectivity to the fullest extent feasible.

Another hurdle is the paucity of dominion over extraneous variables. Within a natural setting, myriad factors may influence behavior, ranging from meteorological conditions to the presence of bystanders. This renders the establishment of causal relationships a daunting task. For instance, if an investigator is scrutinizing responses to public art installations, discerning whether reactions stem from the art per se or from ancillary factors such as temporal considerations or pedestrian traffic patterns may prove challenging. Despite these constraints, the concession is often warranted for the genuine, ecological validity that naturalistic observation affords.

Ethical considerations likewise loom large in naturalistic observation. Researchers must strike a delicate equilibrium between the exigencies of unobtrusive observation and the entitlements of the subjects under observation. Frequently, this entails safeguarding the anonymity of subjects and refraining from documenting their conduct without their explicit consent, particularly within private domains. Public settings, wherein individuals lack a reasonable expectation of privacy, typically afford greater latitude for naturalistic observation. Nevertheless, ethical precepts must be rigorously adhered to in order to uphold the dignity and rights of all implicated subjects.

Naturalistic observation has made substantial inroads across various domains of inquiry. In anthropology, it has served as a lens through which to explore cultural customs and social configurations across disparate communities. In ecology, scientists engage in the observation of fauna within their native habitats to fathom behaviors germane to survival, procreation, and social dynamics. In psychology, it has proven instrumental in elucidating human behaviors spanning from the genesis of adolescence to social dynamics and psychological well-being. The method’s malleability renders it adaptable to a panoply of research queries and contexts, endowing it with a versatile utility in the researcher’s repertoire.

Technological strides have further augmented the efficacy of naturalistic observation. Contemporary tools such as video recording apparatuses, mobile devices, and even unmanned aerial vehicles can expedite data collection while minimizing interference. These innovations facilitate more granular and precise observations, which can be reviewed iteratively for analysis. Moreover, analytic software can aid in discerning patterns and drawing inferences from voluminous troves of observational data, thereby engendering a more rigorous and methodical analytical process.

In summation, naturalistic observation emerges as a potent means of dissecting behavior within its native milieu. Despite its impediments, including observer partiality, variable control constraints, and ethical quandaries, it furnishes unparalleled insights into the interplay between subjects and their surroundings. By abstaining from intervention, researchers can procure data that is both authentic and germane to real-world contexts. As technological progress marches onward, the potential for naturalistic observation to enrich our comprehension of intricate behaviors is poised to burgeon, cementing its status as a cornerstone of research methodologies. Recall, this exposition serves as a springboard for contemplation and further exploration. For bespoke guidance and to ensure adherence to scholarly standards, contemplate engaging the services of professionals at EduBirdie.

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Origin of symmetry breaking in the grasshopper model

David llamas, jaron kent-dobias, kun chen, adrian kent, and olga goulko, phys. rev. research 6 , 023235 – published 3 june 2024.

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Supplemental Material

The planar grasshopper problem, originally introduced by Goulko and Kent [ Proc. R. Soc. A 473 , 20170494 (2017) ], is a striking example of a model with long-range isotropic interactions whose ground states break rotational symmetry. In this paper we analyze and explain the nature of this symmetry breaking with emphasis on the importance of dimensionality. Interestingly, rotational symmetry is recovered in three dimensions for small jumps, which correspond to the nonisotropic cogwheel regime of the two-dimensional problem. We discuss simplified models that reproduce the symmetry properties of the original system in N dimensions. For the full grasshopper model in two dimensions we obtain quantitative predictions for optimal perturbations of the disk. Our analytical results are confirmed by numerical simulations.

• Received 20 November 2023
• Accepted 18 March 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.023235

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

• Research Areas

Authors & Affiliations

• 1 Department of Physics, University of Massachusetts Boston, Boston, Massachusetts 02125, USA
• 2 Istituto Nazionale di Fisica Nucleare, Sezione di Roma I, 00185 Rome, Italy
• 3 Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA
• 4 Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
• 5 Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario N2L 2Y5, Canada
• * [email protected]
• † [email protected]
• ‡ [email protected]

Article Text

Vol. 6, Iss. 2 — June - August 2024

Subject Areas

• Interdisciplinary Physics
• Quantum Physics
• Statistical Physics

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The stability of flat half-space in N dimensions to plane-wave perturbations of wavenumber k . All values are negative except at k = 0 (which corresponds to translation of the interface and is not a probability-conserving perturbation) and for k d that are multiples of 2 π in the case N = 2 . These values are zero, meaning that there is a marginal stability to perturbations with wavenumber commensurate with d in two dimensions but not an instability. For larger dimension N of space, the result becomes increasingly insensitive to the commensurability of k and d .

The coefficient of stability for the disk to small perturbations of n -fold symmetry. A large dot is drawn at the smallest value of d where each curve first becomes positive. The black bar shows the point in d at which the transition to disconnected shapes occurs as measured in [ 1 ].

The scaled coefficient δ p n for increasing n on the disk as a function of k d = π n d , along with the result for the semi-infinite plane ( n = ∞ ) from ( 14 ). As n is increased, the disk result asymptotically approaches that of the semi-infinite plane. Since the finite- n curves tend to the zeros of the limit curve from above, the marginally stable points of the half-plane are destabilized at finite n .

First and second most unstable modes n at a given d for n ≤ 18 . The blue and yellow lines show the location of the first and second peak in δ p n ( d ) , respectively. The location of the first peak is extremely similar to (3.4) of [ 1 ] but not identical. If the cutoff in n is increased, more bands appear at higher n . Black markers denote the corresponding numbers of cogs for the optimal solutions found through numerical simulation in Ref. [ 1 ] (the same data are shown in the left panel of Fig. 5 in Ref. [ 1 ]). The numerical results from [ 1 ] are in very close agreement with the current prediction.

(Left) Grasshopper probability for lawns with fixed cog number for two values of d , obtained numerically for discrete lawns with 10 000 cells. The maxima corresponding to the two leading unstable modes are clearly visible ( n = 9 and n = 17 for d = 0.4 ; n = 7 and n = 15 for d = 0.46 ) and are marked with thin vertical lines. These are indeed (local) maxima of the grasshopper problem. Horizontal-dashed lines denote the corresponding disk probabilities given by Eq. ( 3 ). (Right) The same probability rescaled by the squared amplitude ε of the cogs (symbols with error bars). Solid lines show δ p n ( d ) from ( 25 ), which corresponds roughly with the finite- ε numerical data.

Study of discretization effects. (Left) The exact continuous probability functional p μ ( d ) for the solid 3-ball of unit volume given by Eq. ( 4 ) (red solid line) compared with the corresponding discrete P { s } ( d ) (black dots) as function of the grasshopper jump distance d . For d ≤ R 0 , 3 the 3-ball configuration is the optimal lawn shape. (Right) Relative deviation of P { s } ( d ) for the solid 3-ball configuration from the corresponding p μ ( d ) as function of the lattice spacing h for two representative values of the grasshopper jump: d = 0.2 ≈ 0.3 R 0 , 3 (blue dots and line) and d = 0.5 ≈ 0.8 R 0 , 3 (red dots and line). For the highest resolutions considered ( M ≈ 113 000 ) the discretization error is well below 0.3 % . Lines are to guide the eye.

Study of discretization effects. (Left) The exact continuous probability functional p μ ( d ) for the 3-shell where the inner radius is selected as R i , 3 = d − R 0 , 3 (solid blue line) compared with the corresponding discrete P { s } ( d ) (black dots) as function of the grasshopper jump distance d ≥ R 0 , 3 . The corresponding probability for the solid 3-ball (red line) is also shown for comparison. For d > R 0 , 3 the 3-shell has a higher success probability than the 3-ball. (Right) Relative deviation of P { s } ( d ) for the 3-shell configuration from the corresponding p μ ( d ) as function of the lattice spacing h for two representative values of the grasshopper jump: d = 0.94 ≈ 1.5 R 0 , 3 (blue dots and line) and d = 0.77 ≈ 1.25 R 0 , 3 (red dots and line). The inner radius for each d is R i , 3 = d − R 0 , 3 , as before. For the highest resolutions considered ( M ≈ 162 000 ) the discretization error is below 1 % . Lines are to guide the eye.

(Left) Cross section of the optimal configuration for d = 0.64 R 0 , 3 found numerically for a system with M = 160 000 spins. The configuration has the shape of a solid 3-ball. This configuration was found to be optimal for all d ≤ R 0 , 3 . (Right) Cross section of the optimal configuration for d = 1.32 R 0 , 3 found numerically for a system with M = 160 , 000 spins. If the jump length exceeds R 0 , 3 the configurations remain isotropic (for d ≲ 1.4 R 0 , 3 ) but develop a spherical hole in the center; the radius of the hole grows with increasing d . Note that the outer radius of the configuration is slightly larger than R 0 , 3 to ensure that it has unit volume.

(Left) The optimal inner radius R i , 3 of the 3-shell vs grasshopper jump. The numerically found inner radii (black dots) match very well the analytical result (red solid line). The value d − R 0 , 3 (blue-dashed lines) is shown for comparison. (Right) The corresponding optimal grasshopper probabilities for the optimal inner radius (red-solid line for analytical value and black dots for the numerical value) and for d − R 0 , 3 (blue-dashed lines). The isotropic 3-shell ceases to be optimal for jumps exceeding a critical value of approximately 1.4 R 0 , 3 .

Histograms of the radial coordinates of the configuration boundary points for, from left to right, d = 1.39 R 0 , 3 (isotropic 3-shell), d = 1.42 R 0 , 3 (eightfold perturbation), and d = 1.58 R 0 , 3 (sixfold perturbation). Results shown were obtained for systems with M = 160 000 spins. The vertical-red lines denote the theoretical values for the optimal (for the respective value of d ) inner and outer radii of the corresponding 3-shell configurations. The shaded regions mark the ± h interval around the optimal radii.

Maximal values found numerically for the discrete grasshopper lawn probability P { s } ( d ) . Results shown were obtained for systems with M = 40 000 spins. Vertical lines denote boundaries between the different regimes. From left to right these are: isotropic solid ball, isotropic shells, eightfold shell perturbations, sixfold shell perturbations, ring with caps, nested crescents. Insets show examples of representative configurations. 3d animations displaying in full the features summarized here are given within the Supplemental Material [ 14 ].

The points give the difference in probability between the perturbed disk and the disk as a function of perturbation size ε for n = 2 and d = 0.98 > d 0 . They were computed using numeric integration on the full expression. The solid line gives δ p 2 ( d ) ε 2 . The results agree at small ε , as expected.

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