narrative report research design

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Methods for Conducting and Publishing Narrative Research With Undergraduates

Azriel grysman.

1 Psychology Department, Hamilton College, Clinton, NY, United States

Jennifer Lodi-Smith

2 Department of Psychological Sciences and Institute for Autism Research, Canisius College, Buffalo, NY, United States

Introduction

Narrative research systematically codes individual differences in the ways in which participants story crucial events in their lives to understand the extent to which they create meaning and purpose (McAdams, 2008 ). These narrative descriptions of life events address a diverse array of topics, such as personality (McAdams and Guo, 2015 ), development (Fivush et al., 2006 ), clinical applications (Banks and Salmon, 2013 ), well-being (Adler et al., 2016 ), gender (Grysman et al., 2016 ), and older adult memory decline (Levine et al., 2002 ).

Narrative research is an ideal way to involve undergraduate students as contributors to broader projects and often as co-authors. In narrative or mixed method research, undergraduates have the opportunity to think critically about methodology during study construction and implementation, and then by engaging with questions of construct validity when exploring how different methods yield complementary data on one topic. In narrative research in psychology, students collect data, as in many traditional psychology laboratories, but they collect either typed or spoken narratives and then extensively code narratives before quantitative data analysis can occur. Narrative research thus provides a unique opportunity to blend the psychological realities captured by qualitative data with the rigors of quantitative methods.

Narrative researchers start by establishing the construct of interest, deciding when coding narratives for this construct is the most effective form of measurement, rather than a questionnaire or some other form of assessment. A coding manual is developed or adopted, and all coders study the manual, practice implementing it, and discuss the process and any disagreements until the team is confident that all coders are implementing the rules in a similar way. A reliability set is then initiated, such that coders assess a group of narratives from the data of interest independently, compare their codes, and conduct reliability statistics (e.g., Intraclass coefficient, Cohen's kappa). When a predetermined threshold of agreement has been reached and a sufficient percentage of the narrative data has been coded, the two raters are deemed sufficiently similar, disagreements are resolved (by conversation or vote), and one coder completes the remainder of the narrative data. Readers are directed to Syed and Nelson ( 2015 ) and to Adler et al. ( 2017 ) for further details regarding this process, as these papers provide greater depth regarding best practices coding.

Narrative Coding in an Undergraduate Laboratory: Common Challenges and Best Practices

When are students co-authors.

Narrative coding requires heavy investment of time and energy from the student, but time and energy are not the only qualities that matter when deciding on authorship. Because students are often shielded from hypotheses for the duration of coding in order to maintain objectivity and to not bias them in their coding decisions, researchers may be in a bind when data finally arrive; they want to move toward writing but students are not yet sufficiently knowledgeable to act as co-authors. Kosslyn ( 2002 ) outlines six criteria for establishing authorship (see also Fine and Kurdek, 1993 ), and includes a scoring system for the idea, design, implementation (i.e., creation of materials), conducting the experiment, data analysis, and writing. A student who puts countless hours into narrative coding has still only contributed to conducting the experiment or data analysis. If the goal is including students as authors, researchers should consider these many stages as entry points into the research process. After coding has completed, students should read background literature while data are analyzed and be included in the writing process, as detailed below (see “the route to publishing”). In addition, explicit conversations with students about their roles and expectations in a project are always advised.

Roadblocks to Student Education

One concern of a researcher managing a narrative lab is communicating the goals and methods of the interrater process to student research assistants, who have likely never encountered a process like this before. Adding to this challenge is the fact that often researchers shield undergraduates from the study's hypotheses to reduce bias and maintain their objectivity, which can serve as a roadblock both for students' education and involvement in the project and for their ability to make decisions in borderline cases. Clearly communicating the goals and methods involved in a coding project are essential, as is planning for the time needed to orient students to the hypotheses after coding if they are to be included in the later steps of data analysis and writing. In the following two sections, we expand on challenges that arise in this vein and how we have addressed them.

Interpersonal Dynamics

A critical challenge in the interrater process addresses students' experience of power relationships, self-esteem, and internalization of the coding process. In the early stages, students often disagree on how to code a given narrative. Especially when the professor mediates these early disagreements, students might feel intimidated by a professor who sides with one student more consistently than another. Furthermore, disagreeing with a fellow student may be perceived as putting them down; students often hedge explanations with statements like “I was on the fence between those two,” and “you're probably right.” These interpersonal concerns must be addressed early in the coding process, with the goal of translating a theoretical construct into guidelines for making difficult decisions with idiosyncratic data. In the course of this process, students make the most progress by explaining their assumptions and decision process, to help identify points of divergence. Rules-of-thumb that are established in this process will be essential for future cases, increasing agreement but also creating a shared sense of coding goals so that it can be implemented consistently in new circumstances. Thus, interpersonal concerns and intimidation undermine the interrater process by introducing motivations for picking a particular code, ultimately creating a bias in the name of saving face and achieving agreement rather than leading toward agreement because of a shared representation of micro-level decisions that support the coding system.

Clearly communicating the goal of the interrater process is key to establishing a productive coding environment, mitigating the pitfalls described above. One of us (AG) begins coding meetings by discussing the goals of the interrater process, emphasizing that disagreeing ultimately helps us clarify assumptions and prevents future disagreements. If the professor agrees with one person more than another, it is not a sign of favoritism or greater intelligence. Given the novelty of the coding task and undergraduate students' developmental stage, students sometimes need reassurance emphasizing that some people are better at some coding systems than others, or even that some are better coders, and that these skills should not be connected to overall worth.

The next set of challenges pertains to students' own life settings. Depending on the structure of research opportunities in a given department, students work limited hours per week on a project, are commonly only available during the academic semester, and are often pulled by competing commitments. Researchers should establish a framework to help students stay focused on the coding project and complete a meaningful unit of coding before various vacations, semesters abroad, or leaving the laboratory to pursue other interests. This paper discusses best practices that help circumvent these pitfalls, but we recommend designing projects with them in mind. Some coding systems are better suited to semester-long commitments of 3 h per week whereas others need larger time commitments, such as from students completing summer research. It is helpful to identify RAs' long-term plans across semesters, knowing who is going abroad, who expects to stay in the lab, and assigning projects accordingly.

Building a robust collaborative environment can shape an invested team who will be engaged in the sustained efforts needed for successful narrative research. In one of our labs (JLS), general lab meetings are conducted to discuss coding protocols and do collaborative practice. Then an experienced coder is paired with a new lab member. The experienced coder codes while walking the new coder through the decision process for a week's worth of assigned coding. The new coder practices on a standard set of practice narratives under the supervision of the experienced coder, discussing the process throughout. The new coder's work is checked for agreement with published codes and years of other practice coders. The new coder then codes new narratives under the supervision of the experienced coder for 2 weeks or until comfortable coding independently. The most experienced and conscientious junior applies for an internal grant each year to be the lab manager during senior year. This lab manager assigns weekly coding and assists with practical concerns. Coding challenges are discussed at weekly lab meetings. More experienced coders also lead weekly “discrepancy meetings” where two or three trained coders review discrepancies in a coded data set and come to a consensus rating. Such meetings give the students further learning and leadership opportunities. These meetings are done in small teams to accommodate the students' differing schedules and help build understanding of the constructs and a good dynamic in the team.

The Route to Publishing With Undergraduates in Narrative Psychology

When coding has successfully been completed, researchers then have the opportunity to publish their work with undergraduates. When talented students are involved on projects, the transition to writing completes their research experience. A timeline should be established and a process clearly identified: who is the lead author? Is that person writing the whole manuscript and the second author editing or are different sections being written? We have considered all these approaches depending on the abilities and circumstances of the undergraduate. In one example Grysman and Denney ( 2017 ), AG sent successive sections to the student for editing throughout the writing process. In another, because of the student's ability in quantitative analysis and figure creation (Grysman and Dimakis, 2018 ), the undergraduate took the lead on results, and edited the researcher's writing for the introduction and discussion. In a third (Meisels and Grysman, submitted), the undergraduate more centrally designed the study as an honors thesis, and is writing up the manuscript while the researcher edits and writes the heavier statistics and methodological pieces. In another example, Lodi-Smith et al. ( 2009 ) archival open-ended responses were available to code for new constructs, allowing for a shorter project time frame than collecting new narrative data. The undergraduate student's three-semester honors thesis provided the time, scope, and opportunity to code and analyze archival narratives of personality change during college. As narrative labs often have a rich pool of archival data from which new studies can emerge, they can be a rich source of novel data for undergraduate projects.

In sum, there isn't one model of how to yield publishable work, but once the core of a narrative lab has been established, the researcher can flexibly include undergraduates in the writing process to differing degrees. As in other programs of research, students have the opportunity to learn best practices in data collection and analysis in projects they are not actively coding. Because of the need to keep coders blind to study hypotheses it is often helpful to maintain multiple projects in different points of development. Students can gain experience across the research process helping collect new data, coding existing narratives, and analyzing and writing up the coding of previous cohorts of students.

Most importantly, narrative research gives students an opportunity to learn about individuals beyond what they learn in the systematic research process and outcomes of their research. The majority of undergraduate research assistants are not going on to careers as psychologists conducting academic research on narrative identity. Many undergraduate psychology students will work in clinical/counseling settings, in social work, or in related mental health fields. The skills learned in a narrative research lab can generalize far beyond the specific goals of the research team. By reading individual narratives, students and faculty have the opportunity to learn about the lived life, hearing the reality in how people story trauma, success, challenges, and change. They can begin to see subtlety and nuance beyond their own experience and come to appreciate the importance of asking questions and learning from the answers.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

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

Funding. Funding for this article is supported by an internal grant from Hamilton College.

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Narrative Analysis 101

Everything you need to know to get started

By: Ethar Al-Saraf (PhD)| Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to research, the host of qualitative analysis methods available to you can be a little overwhelming. In this post, we’ll  unpack the sometimes slippery topic of narrative analysis . We’ll explain what it is, consider its strengths and weaknesses , and look at when and when not to use this analysis method. 

Overview: Narrative Analysis

• What is narrative analysis (simple definition)
• The two overarching approaches  
• The strengths & weaknesses of narrative analysis
• When (and when not) to use it
• Key takeaways

What Is Narrative Analysis?

Simply put, narrative analysis is a qualitative analysis method focused on interpreting human experiences and motivations by looking closely at the stories (the narratives) people tell in a particular context.

In other words, a narrative analysis interprets long-form participant responses or written stories as data, to uncover themes and meanings . That data could be taken from interviews, monologues, written stories, or even recordings. In other words, narrative analysis can be used on both primary and secondary data to provide evidence from the experiences described.

That’s all quite conceptual, so let’s look at an example of how narrative analysis could be used.

Let’s say you’re interested in researching the beliefs of a particular author on popular culture. In that case, you might identify the characters , plotlines , symbols and motifs used in their stories. You could then use narrative analysis to analyse these in combination and against the backdrop of the relevant context.

This would allow you to interpret the underlying meanings and implications in their writing, and what they reveal about the beliefs of the author. In other words, you’d look to understand the views of the author by analysing the narratives that run through their work.

The Two Overarching Approaches

Generally speaking, there are two approaches that one can take to narrative analysis. Specifically, an inductive approach or a deductive approach. Each one will have a meaningful impact on how you interpret your data and the conclusions you can draw, so it’s important that you understand the difference.

First up is the inductive approach to narrative analysis.

The inductive approach takes a bottom-up view , allowing the data to speak for itself, without the influence of any preconceived notions . With this approach, you begin by looking at the data and deriving patterns and themes that can be used to explain the story, as opposed to viewing the data through the lens of pre-existing hypotheses, theories or frameworks. In other words, the analysis is led by the data.

For example, with an inductive approach, you might notice patterns or themes in the way an author presents their characters or develops their plot. You’d then observe these patterns, develop an interpretation of what they might reveal in the context of the story, and draw conclusions relative to the aims of your research.

Contrasted to this is the deductive approach.

With the deductive approach to narrative analysis, you begin by using existing theories that a narrative can be tested against . Here, the analysis adopts particular theoretical assumptions and/or provides hypotheses, and then looks for evidence in a story that will either verify or disprove them.

For example, your analysis might begin with a theory that wealthy authors only tell stories to get the sympathy of their readers. A deductive analysis might then look at the narratives of wealthy authors for evidence that will substantiate (or refute) the theory and then draw conclusions about its accuracy, and suggest explanations for why that might or might not be the case.

Which approach you should take depends on your research aims, objectives and research questions . If these are more exploratory in nature, you’ll likely take an inductive approach. Conversely, if they are more confirmatory in nature, you’ll likely opt for the deductive approach.

Need a helping hand?

Strengths & Weaknesses

Now that we have a clearer view of what narrative analysis is and the two approaches to it, it’s important to understand its strengths and weaknesses , so that you can make the right choices in your research project.

A primary strength of narrative analysis is the rich insight it can generate by uncovering the underlying meanings and interpretations of human experience. The focus on an individual narrative highlights the nuances and complexities of their experience, revealing details that might be missed or considered insignificant by other methods.

Another strength of narrative analysis is the range of topics it can be used for. The focus on human experience means that a narrative analysis can democratise your data analysis, by revealing the value of individuals’ own interpretation of their experience in contrast to broader social, cultural, and political factors.

All that said, just like all analysis methods, narrative analysis has its weaknesses. It’s important to understand these so that you can choose the most appropriate method for your particular research project.

The first drawback of narrative analysis is the problem of subjectivity and interpretation . In other words, a drawback of the focus on stories and their details is that they’re open to being understood differently depending on who’s reading them. This means that a strong understanding of the author’s cultural context is crucial to developing your interpretation of the data. At the same time, it’s important that you remain open-minded in how you interpret your chosen narrative and avoid making any assumptions .

A second weakness of narrative analysis is the issue of reliability and generalisation . Since narrative analysis depends almost entirely on a subjective narrative and your interpretation, the findings and conclusions can’t usually be generalised or empirically verified. Although some conclusions can be drawn about the cultural context, they’re still based on what will almost always be anecdotal data and not suitable for the basis of a theory, for example.

Last but not least, the focus on long-form data expressed as stories means that narrative analysis can be very time-consuming . In addition to the source data itself, you will have to be well informed on the author’s cultural context as well as other interpretations of the narrative, where possible, to ensure you have a holistic view. So, if you’re going to undertake narrative analysis, make sure that you allocate a generous amount of time to work through the data.

When To Use Narrative Analysis

As a qualitative method focused on analysing and interpreting narratives describing human experiences, narrative analysis is usually most appropriate for research topics focused on social, personal, cultural , or even ideological events or phenomena and how they’re understood at an individual level.

For example, if you were interested in understanding the experiences and beliefs of individuals suffering social marginalisation, you could use narrative analysis to look at the narratives and stories told by people in marginalised groups to identify patterns , symbols , or motifs that shed light on how they rationalise their experiences.

In this example, narrative analysis presents a good natural fit as it’s focused on analysing people’s stories to understand their views and beliefs at an individual level. Conversely, if your research was geared towards understanding broader themes and patterns regarding an event or phenomena, analysis methods such as content analysis or thematic analysis may be better suited, depending on your research aim .

Let’s recap

In this post, we’ve explored the basics of narrative analysis in qualitative research. The key takeaways are:

• Narrative analysis is a qualitative analysis method focused on interpreting human experience in the form of stories or narratives .
• There are two overarching approaches to narrative analysis: the inductive (exploratory) approach and the deductive (confirmatory) approach.
• Like all analysis methods, narrative analysis has a particular set of strengths and weaknesses .
• Narrative analysis is generally most appropriate for research focused on interpreting individual, human experiences as expressed in detailed , long-form accounts.

If you’d like to learn more about narrative analysis and qualitative analysis methods in general, be sure to check out the rest of the Grad Coach blog here . Alternatively, if you’re looking for hands-on help with your project, take a look at our 1-on-1 private coaching service .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

Thanks. I need examples of narrative analysis

Here are some examples of research topics that could utilise narrative analysis:

Personal Narratives of Trauma: Analysing personal stories of individuals who have experienced trauma to understand the impact, coping mechanisms, and healing processes.

Identity Formation in Immigrant Communities: Examining the narratives of immigrants to explore how they construct and negotiate their identities in a new cultural context.

Media Representations of Gender: Analysing narratives in media texts (such as films, television shows, or advertisements) to investigate the portrayal of gender roles, stereotypes, and power dynamics.

Where can I find an example of a narrative analysis table ?

Please i need help with my project,

how can I cite this article in APA 7th style?

please mention the sources as well.

My research is mixed approach. I use interview,key_inforamt interview,FGD and document.so,which qualitative analysis is appropriate to analyze these data.Thanks

Which qualitative analysis methode is appropriate to analyze data obtain from intetview,key informant intetview,Focus group discussion and document.

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Field guide: narrative research methodologies.

The narrative change field is informed by an array of multidisciplinary approaches to craft narratives, test messages, landscape the narrative environment, and measure narrative change efforts. Our field guide presents a map to a number of traditional and emergent research practices in this space.

As part of our Understanding Narrative Research Methodologies project, Narrative Initiative worked with Spitfire Strategies to produce a field guide to narrative research methodologies. Based on nearly 20 interviews with researchers, practitioners and academics, this report explores the landscape of both existing and emergent narrative research methodologies.

We see this guide as a first edition, intended to spark dialogue. We hope researchers and practitioners in the field reach out to exchange learning and help us fill in the gaps. If you’re interested in further conversation, please contact Márquez .

Introduction

There are likely thousands of organizations and movements actively at work to promote fair and inclusive societies, trying to win justice and equity on a grand scale. These groups, including nonprofits, tap into our imaginations by organizing and by using visual and verbal language to open new pathways and possibilities. We understand this nexus of efforts as narrative change work. No entity does this work alone. Success is found when work is done in coalition and collaboration. How then do they uncover concepts that will move their audiences to action, build power and stickiness, and lead to lasting change?

Narrative Initiative commissioned Spitfire Strategies to learn more about the research approaches and methods being used to inform and advance the narrative work of social justice organizations. This Field Guide offers lessons from interviews with some narrative change research leaders. Our interviewees presented a snapshot of the field, identified barriers, and offered a starting point to deepening narrative change research.

Due to its emergent nature and the varied traditions feeding into narrative change research, a set of needs arose that we find noteworthy. Interviewees cited the need for boldly embracing equity and diversity, and for collaboration across organizations and disciplines sharing research tools, data, and insights. They also expressed a need for shared research ethics and standards of practice. Both the challenge and the opportunity in this work lies in drawing from multiple sectors that contribute to narrative change practice.

We see this Field Guide as the first edition of a tool for narrative change researchers and those interested in embarking upon the practices detailed below. We also frame this Field Guide as an invitation to dialogue and learning exchange wherein readers help fill in the gaps and point to strong examples of theory and practice informing their own approaches. Ultimately, we want to learn with you how research methodologies are being used to make justice and equity common sense.

This report was written by Inga Skippings, Mark Dessaury, and Alexander (Bob) Boykin at Spitfire Strategies ; in conversation with Márquez Rhyne and Rachel Weidinger at Narrative Initiative.  We want to thank the following for helping to shape the thinking in this Field Guide:

• Meg Bostrom, Topos Partnership
• Jeff Chang, Race Forward
• Brett Davidson, Open Society Foundations
• Kristen Grimm, Spitfire Strategies
• Hahrie Han, The P3 Lab
• Doug Hattaway, Hattaway Communications
• David Karpf, George Washington University
• Nat Kendall-Taylor, FrameWorks Institute
• Martin Kirk, /The Rules
• Richard Kirsch, Our Story – The Hub for American Narratives
• Liz Manne, Liz Manne Strategy
• Felicia Perez, Center for Story-based Strategy
• Rashid Shabazz, Color of Change
• Micah Sifry, Civic Hall and Personal Democracy Media
• Anat Shenker-Osorio, ASO Communications
• Tracy Van Slyke, Pop Culture Collaborative
• Brian Waniewski, Harmony Labs
• Rachel Weidinger, Upwell (closed)

Download the full report to continue reading.

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• Observing Narrative Together

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• Narrative Research
• Field of Narrative Change

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• Narrative Initiative

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Handbook of Research Methods in Health Social Sciences pp 411–423 Cite as

Narrative Research

• Kayi Ntinda 2  
• Reference work entry
• First Online: 13 January 2019

2575 Accesses

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Narrative research aims to unravel consequential stories of people’s lives as told by them in their own words and worlds. In the context of the health, social sciences, and education, narrative research is both a data gathering and interpretive or analytical framework. It meets these twin goals admirably by having people make sense of their lived health and well-being in their social context as they understand it, including their self-belief-oriented stories. Narrative research falls within the realm of social constructivism or the philosophy that people’s lived stories capture the complexities and nuanced understanding of their significant experiences. This chapter presents a brief overview of the narrative research approaches as forms of inquiry based on storytelling and premised on the truth value of the stories to best represent the teller’s life world. The chapter also discusses data collection, analysis, and presentation utilizing narrative analysis. In doing so, this chapter provides illustrative examples applying narrative-oriented approaches to research in the health and social sciences. The chapter concludes by outlining the importance of narrative research to person-centric investigations in which the teller-informant view matters to the resulting body of knowledge.

• Collaboration
• Lived experience
• Intersubjectivity
• Narrative inquiry
• Storytelling
• Meaning-making

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Ntinda, K. (2019). Narrative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_79

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Encyclopedia of Research Design

• Edited by: Neil J. Salkind
• Publisher: SAGE Publications, Inc.
• Publication year: 2010
• Online pub date: December 27, 2012
• Discipline: Anthropology
• Methods: Confidence intervals , Dependent variables , Sample size , Correlation , Independent variables , Null hypothesis
• DOI: https:// doi. org/10.4135/9781412961288
• Keywords: equations , errors , estimates , treatment Show all Show less
• Print ISBN: 9781412961271
• Online ISBN: 9781412961288
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Reader's guide

Entries a-z, subject index.

Research design can be daunting for all types of researchers. At its heart it might be described as a formalized approach toward problem solving, thinking, and acquiring knowledge—the success of which depends upon clearly defined objectives and appropriate choice of statistical tools, tests, and analysis to meet a project's objectives.

Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results.

Key Features

Covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; Addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; Provides summaries of advantages and disadvantages of often-used strategies; Uses hundreds of sample tables, figures, and equations based on real-life cases

Descriptive Statistics; Distributions; Graphical Displays of Data; Hypothesis Testing; Important Publications; Inferential Statistics; Item Response Theory; Mathematical Concepts; Measurement Concepts; Organizations; Publishing; Qualitative Research; Reliability of Scores; Research Design Concepts; Research Designs; Research Ethics; Research Process; Research Validity Issues; Sampling; Scaling; Software Applications; Statistical Assumptions; Statistical Concepts; Statistical Procedures; Statistical Tests; Theories, Laws, and Principles; Types of Variables; Validity of Scores

The Encyclopedia of Research Design is the perfect instrument for new learners as well as experienced researchers to explore both the original and newest branches of the field.

Front Matter

• Editorial Board
• List of Entries
• Reader's Guide
• About the Editors
• Contributors
• Introduction

Reader’s Guide

• Central Tendency, Measures of
• Cohen's d Statistic
• Cohen's f Statistic
• Correspondence Analysis
• Descriptive Statistics
• Effect Size, Measures of
• Eta-Squared
• Factor Loadings
• Krippendorff's Alpha
• Partial Eta-Squared
• Standard Deviation
• Trimmed Mean
• Variability, Measure of
• z Distribution
• Bernoulli Distribution
• Copula Functions
• Cumulative Frequency Distribution
• Distribution
• Frequency Distribution
• Law of Large Numbers
• Normal Distribution
• Normalizing Data
• Poisson Distribution
• Quetelet's Index
• Sampling Distributions
• Weibull Distribution
• Box-and-Whisker Plot
• Column Graph
• Frequency Table
• Graphical Display of Data
• Growth Curve
• L'Abbé Plot
• Radial Plot
• Residual Plot
• Scatterplot
• U-Shaped Curve
• Alternative Hypotheses
• Critical Value
• Decision Rule
• Nondirectional Hypotheses
• Nonsignificance
• Null Hypothesis
• One-Tailed Test
• Power Analysis
• Significance Level, Concept of
• Significance Level, Interpretation and Construction
• Significance, Statistical
• Two-Tailed Test
• Type I Error
• Type II Error
• Type III Error
• “Coefficient Alpha and the Internal Structure of Tests”
• “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
• “Meta-Analysis of Psychotherapy Outcome Studies”
• “On the Theory of Scales of Measurement”
• “Probable Error of a Mean, The”
• “Psychometric Experiments”
• “Sequential Tests of Statistical Hypotheses”
• “Technique for the Measurement of Attitudes, A”
• “Validity”
• Aptitudes and Instructional Methods
• Doctrine of Chances, The
• Logic of Scientific Discovery, The
• Nonparametric Statistics for the Behavioral Sciences
• Probabilistic Models for Some Intelligence and Attainment Tests
• Statistical Power Analysis for the Behavioral Sciences
• Teoria Statistica Delle Classi e Calcolo Delle Probabilità
• Q -Statistic
• Association, Measures of
• Coefficient of Concordance
• Coefficient of Variation
• Coefficients of Correlation, Alienation, and Determination
• Confidence Intervals
• Margin of Error
• Nonparametric Statistics
• Parametric Statistics
• Partial Correlation
• Pearson Product-Moment Correlation Coefficient
• Polychoric Correlation Coefficient
• Randomization Tests
• Regression Coefficient
• Semipartial Correlation Coefficient
• Spearman Rank Order Correlation
• Standard Error of Estimate
• Standard Error of the Mean
• Student's t Test
• Unbiased Estimator
• b Parameter
• Computerized Adaptive Testing
• Differential Item Functioning
• Guessing Parameter
• General Linear Model
• Matrix Algebra
• Polynomials
• Sensitivity Analysis
• Yates's Notation
• Ceiling Effect
• Change Scores
• False Positive
• Gain Scores, Analysis of
• Instrumentation
• Item Analysis
• Item-Test Correlation
• Observations
• Percentile Rank
• Psychometrics
• Random Error
• Response Bias
• Sensitivity
• Social Desirability
• Specificity
• Standardized Score
• True Positive
• American Educational Research Association
• American Statistical Association
• National Council on Measurement in Education
• American Psychological Association Style
• Discussion Section
• Dissertation
• Literature Review
• Methods Section
• Purpose Statement
• Results Section
• Content Analysis
• Discourse Analysis
• Ethnography
• Focus Group
• Interviewing
• Narrative Research
• Naturalistic Inquiry
• Naturalistic Observation
• Qualitative Research
• Think-Aloud Methods
• Coefficient Alpha
• Correction for Attenuation
• Internal Consistency Reliability
• Interrater Reliability
• Parallel Forms Reliability
• Reliability
• Spearman–Brown Prophecy Formula
• Split-Half Reliability
• Standard Error of Measurement
• Test–Retest Reliability
• Aptitude-Treatment Interaction
• Cause and Effect
• Concomitant Variable
• Confounding
• Control Group
• Interaction
• Internet-Based Research Method
• Intervention
• Natural Experiments
• Network Analysis
• Replication
• Research Design Principles
• Treatment(s)
• Triangulation
• Unit of Analysis
• Yoked Control Procedure
• A Priori Monte Carlo Simulation
• Action Research
• Adaptive Designs in Clinical Trials
• Applied Research
• Behavior Analysis Design
• Block Design
• Case-Only Design
• Causal-Comparative Design
• Cohort Design
• Completely Randomized Design
• Cross-Sectional Design
• Crossover Design
• Double-Blind Procedure
• Ex Post Facto Study
• Experimental Design
• Factorial Design
• Field Study
• Group-Sequential Designs in Clinical Trials
• Laboratory Experiments
• Latin Square Design
• Longitudinal Design
• Meta-Analysis
• Mixed Methods Design
• Mixed Model Design
• Monte Carlo Simulation
• Nested Factor Design
• Nonexperimental Design
• Observational Research
• Panel Design
• Partially Randomized Preference Trial Design
• Pilot Study
• Pragmatic Study
• Pre-Experimental Designs
• Pretest–Posttest Design
• Prospective Study
• Quantitative Research
• Quasi-Experimental Design
• Randomized Block Design
• Repeated Measures Design
• Response Surface Design
• Retrospective Study
• Sequential Design
• Single-Blind Study
• Single-Subject Design
• Split-Plot Factorial Design
• Thought Experiments
• Time Studies
• Time-Lag Study
• Time-Series Study
• Triple-Blind Study
• True Experimental Design
• Wennberg Design
• Within-Subjects Design
• Zelen's Randomized Consent Design
• Animal Research
• Declaration of Helsinki
• Ethics in the Research Process
• Informed Consent
• Nuremberg Code
• Participants
• Recruitment
• Clinical Significance
• Clinical Trial
• Cross-Validation
• Data Cleaning
• Delphi Technique
• Evidence-Based Decision Making
• Exploratory Data Analysis
• Inference: Deductive and Inductive
• Last Observation Carried Forward
• Planning Research
• Primary Data Source
• Q Methodology
• Research Hypothesis
• Research Question
• Scientific Method
• Secondary Data Source
• Standardization
• Statistical Control
• Critical Thinking
• Ecological Validity
• Experimenter Expectancy Effect
• External Validity
• File Drawer Problem
• Hawthorne Effect
• Heisenberg Effect
• Internal Validity
• John Henry Effect
• Multiple Treatment Interference
• Multivalued Treatment Effects
• Nonclassical Experimenter Effects
• Order Effects
• Placebo Effect
• Pretest Sensitization
• Random Assignment
• Reactive Arrangements
• Regression to the Mean
• Sequence Effects
• Threats to Validity
• Validity of Research Conclusions
• Volunteer Bias
• White Noise
• Cluster Sampling
• Convenience Sampling
• Demographics
• Exclusion Criteria
• Experience Sampling Method
• Nonprobability Sampling
• Probability Sampling
• Proportional Sampling
• Quota Sampling
• Random Sampling
• Random Selection
• Sample Size
• Sample Size Planning
• Sampling and Retention of Underrepresented Groups
• Sampling Error
• Stratified Sampling
• Systematic Sampling
• Categorical Variable
• Guttman Scaling
• Interval Scale
• Levels of Measurement
• Likert Scaling
• Nominal Scale
• Ordinal Scale
• Ratio Scale
• Thurstone Scaling
• Software, Free
• Homogeneity of Variance
• Homoscedasticity
• Multivariate Normal Distribution
• Normality Assumption
• Autocorrelation
• Biased Estimator
• Cohen's Kappa
• Collinearity
• Correlation
• Criterion Problem
• Critical Difference
• Data Mining
• Data Snooping
• Degrees of Freedom
• Directional Hypothesis
• Disturbance Terms
• Error Rates
• Expected Value
• Fixed-Effects Model
• Inclusion Criteria
• Influence Statistics
• Influential Data Points
• Intraclass Correlation
• Latent Variable
• Likelihood Ratio Statistic
• Loglinear Models
• Main Effects
• Markov Chains
• Method Variance
• Mixed- and Random-Effects Models
• Multilevel Modeling
• Omega Squared
• Orthogonal Comparisons
• Overfitting
• Pooled Variance
• Quality Effects Model
• Random-Effects Models
• Regression Artifacts
• Regression Discontinuity
• Restriction of Range
• Root Mean Square Error
• Rosenthal Effect
• Serial Correlation
• Simple Main Effects
• Simpson's Paradox
• Sums of Squares
• Accuracy in Parameter Estimation
• Analysis of Covariance (ANCOVA)
• Analysis of Variance (ANOVA)
• Barycentric Discriminant Analysis
• Bivariate Regression
• Bonferroni Procedure
• Bootstrapping
• Canonical Correlation Analysis
• Categorical Data Analysis
• Confirmatory Factor Analysis
• Contrast Analysis
• Descriptive Discriminant Analysis
• Discriminant Analysis
• Dummy Coding
• Effect Coding
• Exploratory Factor Analysis
• Greenhouse–Geisser Correction
• Hierarchical Linear Modeling
• Holm's Sequential Bonferroni Procedure
• Latent Growth Modeling
• Least Squares, Methods of
• Logistic Regression
• Mean Comparisons
• Missing Data, Imputation of
• Multiple Regression
• Multivariate Analysis of Variance (MANOVA)
• Pairwise Comparisons
• Path Analysis
• Post Hoc Analysis
• Post Hoc Comparisons
• Principal Components Analysis
• Propensity Score Analysis
• Sequential Analysis
• Stepwise Regression
• Structural Equation Modeling
• Survival Analysis
• Trend Analysis
• Yates's Correction
• t Test, Independent Samples
• t Test, One Sample
• t Test, Paired Samples
• Bartlett's Test
• Behrens–Fisher t′ Statistic
• Chi-Square Test
• Duncan's Multiple Range Test
• Dunnett's Test
• Fisher's Least Significant Difference Test
• Friedman Test
• Honestly Significant Difference (HSD) Test
• Kolmogorov-Smirnov Test
• Kruskal–Wallis Test
• Mann–Whitney U Test
• Mauchly Test
• McNemar's Test
• Multiple Comparison Tests
• Newman–Keuls Test and Tukey Test
• Omnibus Tests
• Scheffé Test
• Tukey's Honestly Significant Difference (HSD)
• Welch's t Test
• Wilcoxon Rank Sum Test
• Bayes's Theorem
• Central Limit Theorem
• Classical Test Theory
• Correspondence Principle
• Critical Theory
• Falsifiability
• Game Theory
• Gauss–Markov Theorem
• Generalizability Theory
• Grounded Theory
• Item Response Theory
• Occam's Razor
• Probability, Laws of
• Theory of Attitude Measurement
• Weber–Fechner Law
• Control Variables
• Criterion Variable
• Dependent Variable
• Dichotomous Variable
• Endogenous Variables
• Exogenous Variables
• Independent Variable
• Nuisance Variable
• Predictor Variable
• Random Variable
• Concurrent Validity
• Construct Validity
• Content Validity
• Criterion Validity
• Face Validity
• Multitrait–Multimethod Matrix
• Predictive Validity
• Systematic Error
• Validity of Measurement

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How To Write a Narrative Report

Published by Boni on May 18, 2021 May 18, 2021

A narrative report is one of the most common assignments in the academic world. Students face this type of task more often in school. That is why it is vital to understand how to handle such for your success in school. The primary purpose of a narrative report is to curate a compelling story. For students looking to pass their TEAS exam, we also offer free TEAS practice tests to help students prepare for their exam.

Elevate Your Writing with Our Free Writing Tools!

Did you know that we provide a free essay and speech generator, plagiarism checker, summarizer, paraphraser, and other writing tools for free?

Here are some few hacks to enable you write exciting and thrilling reports that will get top grades.

How Long Should a Narrative Report Be?

Before getting into writing the report, consider the length of the report. In the preparation to write a narrative report, you ought to follow two rules regarding the length. One is that you should make your narrative report as informative and as concise as possible, and two, the report should follow your professor’s requirements. Make the narrative report unfold in a way that will exhaustively cover the topic and create a story that your intended audience will enjoy.

Have you ever experienced the struggle of trying to write your narrative report? You are not alone since many students, especially those who strive to combine work and studies, always seek research papers for sale from Gudwriter which is offered at an affordable price.

Narrative Report Format

A narrative report should contain three primary sections; the introduction, body, and conclusion.

• Introduction- the introductory paragraph should be about three to five sentences. Take this chance to write a narrative report introduction that warm-up your audience and ensure they read the entire report.
• Body- the body of the narrative report entails supportive arguments that relate to the main idea explained in the introduction. Ensure that every new concept you introduce is described in its paragraph with paragraphs of three to five sentences.
• Conclusion- the conclusion should be a summary of your report concerning the main idea indicated in the introduction.

Tips on Writing an Excellent Narrative Report

• Choose a good topic

Topic selection is aided by how good you are in writing. To select a good topic for your narrative report, ensure you read many narrative essays for inspiration. Broad reading provides you with ideas on how to organize your work and put across your points meticulously. Look for reports that your professors have assigned in the past and check out narrative essays on the internet.

• Put across a story that illustrates a specific topic

Having a theme confines you to the scope of information that you write about in your narrative report. To write a narrative report that makes sense to your audience, have a story, and analyze that story. A narrative report is about a theme where you use a personal account to illustrate that idea to the audience in an exciting manner.

• Your narrative report must fit the requirements

Narrative reports are mainly required for college admission or assignment, which means you are given a prompt to follow by the institution or the lecturer. Even though you have fascinating stories, you must ensure that you follow the requirements outlined in the prompt to avoid straying from the main point.

The common topics include your personality that was transformed. Still, some events, adversities you had to overcome, or how you dealt with consequences of failure in a particular juncture in your life, the topics are not cast in stone. Hence, you have to go through the prompt to understand the specific topic you must handle.

• Narrate a story with a manageable plot

Articulate narrative reports mostly tell stories with specificity. Since you are not writing a book or a novel, write a narrative report that is concise and contained with a proper limitation of characters, plot, and setting. Too broad narratives make bad narrative reports; thus, you should be specific on characters you involve in your story and ensure they participate in building the theme. Read on descriptive essay about a person and a sample.

• Narrate a story with lively details

To make a good narrative report explain specific details, particular images, and a language that makes the story lively for the audience. Discuss the smells and sights in your narrative with particular details with imagination filling the void. Narrative reports are not fictional; thus, you should stay true to the story while being as creative as possible.

The Writing Process of a Narrative Report

After understanding the structure and the elements of narrative report , the writing process becomes relatively easy. Have a captivating introduction and then outline the major points of the narrative report while describing them in the body. Finally, ensure that your description ends with a delightful punchline and an unpredictable twist.

1. Write the report in the first-person

A narrative report is very personal since it describes events that have occurred to you and relates to your identity. It is, therefore, a requirement to use “I” statements without changing to the favor of other characters in your report.

2. Describe places and characters

Although a narrative report is not descriptive, adorn your story with descriptions of crucial characters and places mentioned in the plot. Vivid description mainly goes for characters who are affected by the outcome of your case whose personalities should be disclosed.

3. Editing and proofreading

After creating the first draft, go through it to point out mistakes and essential ideas you might have left out. To do proper editing of your narrative report, take a rest after you finish drafting it, then read it afresh. Clear all repeated ideas and incorporate the critical ideas that you might have missed. Reread your narrative report to check for any grammatical errors. You could also run your report grammar checking software but do not entirely rely on them.

Finally, you can entrust a friend to go through your narrative report. They may point out a mistake that you might have missed while going through the report and give you their opinion on the report. Feel free to explore a racial profiling essay example with outline.

The Do’s and Don’ts of Narrative Report Writing

To smoothen the process of narrative report writing, have the following points in mind.

• Write your story in the first-person point of view.
• Follow the proper narrative report structure.
• Use straightforward and easy-to-read language.
• Put across your points in chronological order.
• Using all the five senses while writing the report- inform the audience what the characters saw and what they felt, smelt, and heard.

Don’ts

• Don’t use the second-person point of view while writing your narrative report.
• Don’t create fictional stories; narratives should be as natural and true as possible.
• Write a concise and short story that is not too broad.

More resources; How to write an information report.

Free Personal Narrative Essay Sample – With Outline

A personal narrative essay is one that tells a story from a defined point of view, often the author’s, so there is feeling as well as specific and often sensory details provided to get the reader involved in the elements and sequence of the story.

The Loan: A Personal Experience Essay Outline

Introduction.

Thesis:  In spite of my unending struggles, I hoped and believed I would one day make it in life, and that all that I needed was to identify an opportunity and maximally utilize it.

Paragraph 1:

An opportunity presented itself through a conversation with a workmate called John.

• I learnt that John was receiving ten thousand dollars yearly from an uncle of his.
• I faked a mail order business plan and used it to request him for a four thousand dollars loan for half a year.
• He however only afforded me three thousand dollars, as I gave him an old printing machine of mine as interest.
• I used part of the money to acquire a postal box number and then embarked on squandering what had remained of it.

Paragraph 2:  

John demanded back his money by October the same year.

• According to our original plan, I would settle the loan by the 10th of December.
• We however again reached a “mini agreement” and the deadline was revised to the 10th of November.
• I decided to start participating in lottery using the eight hundred dollars I had remained with by then so I could repay the loan.
• I would purchase five tickets bearing similar numbers, which would enable me make 1,500 dollars upon winning the lottery.

Paragraph 3:

I did not win the lottery contrary to my expectations.

• The numbers I had so much trusted had failed me.
• I continued playing but lost every time I tried.
• I was not yet ready to repay the loan even as we reached the 9th of November, and I once again tried my luck in the lottery by picking fifteen numbers.
• I would add what I would win to the $600 I was remaining with at that time.

Paragraph 4:

I decided to use numbers that had been picked by the computer instead of those I had earlier picked.

• I additionally purchased a Super-Cash out of which I would earn $250,000.
• The drawing of my pick would occur at exactly 5.42 p.m. and I would be two thousand five hundred dollars richer if I won.
• I again failed as my numbers did not match the winning “formula.”
• I bought another Super-Cash ticket and banked all my hopes on it.

Paragraph 5:

The drawing was conducted at 11.02 p.m. and I tried my best to have my numbers drawn.

• I once again failed and retired to bed, with depression quickly taking over me.
• I plunged myself into deep thoughts, even wishing I had used my last pick of numbers to play.
• This thought was a bit too little too late.

Paragraph 6:

It was the 10th of November, the deadline for repaying the loan.

• I gave John the six hundred dollars I had remained with and explained to him my situation.
• I received five dollars from my parents, who had empathized with me.
• John lost his job at the end of the month.

Paragraph 7:   

On the 3rd of December, I took a step that would culminate in the end of my financial woes.

• I applied for a bank loan and was awarded the amount I had requested.
• This was a simpler and more workable thought than the lottery idea.
• After passing through another period of financially challenging situations, I fully appreciated that it is never a dependable idea to rely on credit.
• Never have I again asked a friend to loan me any amount of money.

Paragraph 8:

Another lesson I should have learnt is not to depend on lottery.

• I could not learn fast enough at that time as I came close to winning every time I played.
• I now appreciate that nothing comes on a silver platter under the sun.
• I also learnt that problems are solved by facing them, not quitting.
• From the entire experience, I learnt the importance of explaining one’s problem to their benefactors.
• John understood my situation after I explained it to him.
• Had I avoided him, our friendship could probably have crumbled.

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The Loan: A Personal Experience Essay

This was my last chance. I would need three thousand dollars by the following day but unfortunately, only seven hundred was in my possession. I would perhaps need to win a lottery in order to raise the remaining amount. Trouble was beckoning for me. I had a tolerable life five months earlier. I was then twenty years old and was staying with my fiancé and our young baby. The apartment we lived in was cramped. Under normal circumstances, the rent that was charged for the apartment should have been affordable for us. However, our meager income could not grant affordability and continuance. Even as I went through unceasing struggles, I still had hopes and belief that I would one day make it in life. All I needed was to identify an opportunity and make good use of it.

It was during a conversation with a workmate that I landed an opportunity. The colleague, John by name, told me that a wealthy uncle of his had been giving him a whopping ten thousand dollars on a yearly basis. Even a small percentage of that amount would change my wanting situation forever. I quickly drafted a fake mail order business plan. I then requested John to give me four thousand dollars in the form of a loan for a period of half a year. I gave him my old printing machine, whose worth was about 300 hundred dollars, as interest. However, John could only afford me three thousand dollars. I could not turn it down. I quickly sprang into action with my business plan. I acquired a postal box number but sadly, that marked my end with the plan. I acted no more on it. Unperturbed, I embarked on squandering the remainder of the three thousand dollars. Whenever John inquired about the progress of my business, I would tell him all was well.

Fast-forward to October the same year, John demanded back his money. Noteworthy, our original agreement indicated that the 10th of December would mark the deadline for settling the loan. At this point, we again reached a “mini-agreement” that I would settle the loan on the 10 th  of November. By then, I had been left with eight hundred dollars out of the three thousand I had received. My decision to start taking part in the lottery at this time was informed by the lack of a source that could help me repay the loan. As per this new plan of mine, I would purchase five tickets bearing similar numbers. Upon winning the lottery, I would make 1,500 dollars given that each ticket would fetch five hundred dollars. Since I would have personally purchased the tickets, each store would issue me with a check as opposed to having my prize given to me by Madison. Additionally, no taxes would reduce the amount I would earn.

Strange enough and contrary to my expectations, I would not win the lottery. I had put immense trust on the numbers I had picked to get me the prize. This is why it came to me as a big surprise that the numbers failed to win! I never threw in the towel. I picked on new numbers the following day but sadly, I once again failed to win. I continued playing with unending hopes even though I lost every time I tried. Before I could get ready, it was already the 9th of November. I had to raise the money on this particular day. Relying on my luck and that of nature, I picked fifteen numbers in the lottery. I would spend $75 in buying the numbers since each would cost $5. I would add what I would win to the $600 I was remaining with at that time.

My playing time came and I decided not to use the numbers I had earlier picked. My luck was not definitely in the numbers. I decided to rely on numbers picked by the computer at least for this last chance. In addition, I purchased a Super-Cash. Out of it, I would earn $250,000. On this particular day, I had to go back home early from work since the drawing of my pick would occur at exactly 5.42 p.m. I was already glued to my television screen by 5.30 p.m. Within me; I knew this was my chance. I was just a few minutes away from becoming two thousand five hundred dollars richer. My heart throbbed in my chest as the drawing got underway. ‘3’ was the first number to be drawn. Looking at my ticket, ‘3’ was the first among the numbers. Again, ‘3’ was the second one. On my ticket, the second number was not ‘3’. The same number was supposed to be the third in my ticket. The winning “formula” was to be 3-3-3 which I did not have. I had once again failed. However, not all my hopes had been dashed as the Super-Cash would give me another drawing chance. My financial woes would be over if I won the Super-Cash even though I would not have the cash the following day. My conviction pushed me into purchasing another ticket of Super-Cash. I had never before in my life concentrated the way I did in picking the numbers. This was my last source of hope.

The drawing was conducted at 11.02 p.m. Again, I tried my best to have my numbers drawn. ‘13’ was the first number to appear. It was not on my ticket. I plunged into desperation. Not even a single number out of the numbers that were drawn was on my ticket. I had no more options left. Where would I turn to next? Of course the next thing was to go to bed given that the day was literally over. My problem was that I could not sleep. I wallowed in miser. I wished the following day would not come. I dreaded facing it. I did not want to face tomorrow. As countless thoughts crisscrossed my mind in my bed, I remembered about the numbers I had arranged for my picks. My last pick had been 3-3-3. Had I used this particular pick for playing, I would have been a winner. However, this thought was a bit too little too late.

Morning had come. It was the 10th of November, the day I was to honor my pledge of fully servicing the loan. I woke up and carried with me all the six hundred dollars I was left with to work. I gave John the entire amount and narrated to him my ordeal. Within the week, my parents sent me some five hundred dollars after empathizing with my situation. John lost his job at the end of the month. The reason for his dismissal was failure to report to work. Apparently, he had got a greener pasture and thus had decided to quit his former employer. He had informed me of this. However, things did not turn out as he had expected and thus he tried to plead with the company to have him back. Unfortunately for him, the company would have none of his pleas. He even went as far as asking me to convince the company’s management on his behalf as a close friend.

On the 3rd of December, I took a step that would culminate in the end of my financial woes. Noteworthy, this was some seven days to the 10th of December, the date that I was originally to resettle John’s loan. I applied for a bank loan and was awarded the amount I had requested. As is now evident, this was a simpler and more workable thought than the lottery idea. The incident should have taught me a host of lessons. However, I did not fully learn from it. I would again have to pass through some financially challenging situations in order for me to fully appreciate that relying on credit is often not a dependable idea. As a result, never have I again asked a friend to loan me any amount of money. If that is what being in debt with a friend’s money would cost me, let the idea of borrowing from a non-financial institution be.

Another lesson that the situation should have taught me is not to be dependent on lottery. However, I could not learn fast enough at that time as my chances of winning came so close. I only missed winning because I did not use all the numbers I had picked. As a matter of fact, winning would have made me a ‘wizard’ at choosing lottery numbers. However, all would not come to pass; I kept on failing! I now have enough and valuable experience about the challenging nature of the world. It is never easy. Nothing comes on a silver platter under the sun, not even the lottery. Another hard lesson I learnt out of the situation is the importance of being in a position to face one’s problems with courage. One’s problems are one’s problems; one can never solve them by quitting!

Finally, I learnt the importance of explaining one’s problem to their benefactors. As already seen, I had only remained with $600 by the time John’s loan was due. However, instead of trying to avoid him, I walked right up to him and faced him with my situation. I explained everything that had transpired to him and pleaded with him to understand my situation. My efforts were not in vain as he understood and took my promise that I would settle the debt as soon as I could. Had I started avoiding him, the issue could have caused our friendship to deteriorate drastically. Thus, one should always communicate with those they owe irrespective of whether or not they are in a position to repay their loans.

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