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Personal Statement Science Examples Of Decay

Brian Rybarczyk has written two previous articles on how to write your personal statement for a graduate school application; you will find his earlier articles here and here.

As I review drafts of personal statements from prospective graduate school applicants, some issues arise over and over. The drafts often seem like resumes in narrative form, lists of activities without much context or meaning. This article highlights the points of feedback I most frequently provide, focusing on ways to add substance to your personal statement. 

Why do you want to go to graduate school? Why do you like this particular graduate program?

Ask yourself “why?”

The competition for entry into graduate programs is increasing. Your graduate education requires a large investment of other people’s time and money. It’s up to you to convince the admissions committee that if they let you in, that time and money won’t be wasted.

The best way to achieve that is to convince the committee that you have a vision—that admission to their graduate program is an obvious and useful next step in your career trajectory. But before you can convince the admissions committee, you need to figure it out yourself: Why do you want to go to graduate school? Why do you like this particular graduate program? If you don’t have a convincing answer, maybe you should wait a while—maybe look for a job in a research lab and apply for admission in a year or two, when you have a clearer vision of your future. A clear conviction that this graduate degree will move you toward your career goals is essential.     

Intentionality

We’ve all had experiences that sparked interest in a new area of research or changed how we think about science. Such experiences are important for conveying your basic science story—for convincing the admissions committee that you have a vision of your future that has emerged over time. You need to show that your record of success in college and research isn’t random but, rather, a record of opportunities exploited as you work toward a desired end: the particular career in science that you’re pursuing.

Even much older scientists spend some time exploring, and that’s OK; there’s no need to try and hide your explorations. But your application will be much stronger if you can convince the graduate admissions committee that you have an increasingly clear vision for your future and a plan for how to get there. So place your experiences in context: Why did you decide to participate in that summer research program? Why did you choose your undergraduate research mentor? Why did you spend extra time in the lab despite a heavy course load? Why did you attend and present at a national conference, and what did you learn from that experience? How did your engagement with mentors shape your scientific identity? How does graduate school—this particular graduate program—fit that bigger picture?

Enhancing a description of your research

I use the metaphor of the hourglass to help writers shape a description of a research experience: big, small, and big. Start with the big-picture background, move toward the specifics of your project, and then connect the two together: How do the results of your research contribute to the field? Your ability to explain this clearly and succinctly—to place your particular research in context—demonstrates your command of the big picture. You need to communicate the rationale for pursuing a particular question and choosing a specific experimental approach, and you need to explain why the results matter. Describe your role in the project and what you learned about science from experience. A strong personal statement may also include a proposal for next steps in the project, which demonstrates that you are forward thinking, an important skill as a future graduate student. 

More skills

Graduate studies are expected to develop advanced cognitive skills. When asked, “What skills do you bring to the table?” many young scientists respond with a list of laboratory techniques they used in their undergraduate research projects. Those skills are valuable, but that list isn’t what the admissions committee is looking for. Analytical thinking, problem solving, and synthesizing and evaluating information are among the higher-level skills needed to be successful in graduate school. Your essay should convey your progress toward mastering such skills. Here are some questions that may help you to achieve this, along with some skills that your responses should demonstrate: 

  • What experience do you have working in a collaborative environment? How do you contribute to the effectiveness of a team? (Skills: team science, collaboration, communication)
  • How have you demonstrated your commitment to seeing a project through to completion? (Skills: project management, initiative, leadership)
  • Have you encountered opportunities to solve problems? What strategies have you employed? How did it turn out? (Skill: problemsolving)
  • What alternatives have you proposed to address a research question? Were your alternative approaches successful? (Skill: criticalthinking)

Key intangibles

Motivation, maturity, independence, and enthusiasm for and commitment to science are crucial to success as a graduate student, so they should come across in your personal statement. There’s no formula for conveying these intangible traits, but providing examples of your character, work ethic, and professionalism will help highlight them. 

Addressing challenges and deficiencies

Many students have faced personal and professional challenges. Personal or family health issues, child care issues, financial crises, and so on may have affected your academic progress or state of mind, contributing to deficiencies in your academic record or productivity. An applicant can write about these challenges in the personal statement, but it’s important not to dwell on them too much. The best approach is to describe how these challenges were addressed and what you learned from the process. Emphasize how you managed them and continued to make progress.  

Tailoring

A generically written personal statement won’t get you far in the application process. It won’t sound authentic, and it won’t be convincing. Just like a cover letter for a job application, graduate school applicants should tailor their personal statements for the programs they are applying to. Here are a few suggestions.

  • Highlight an area of research that the program is strong in, and describe how it matches your scientific interests.
  • Identify faculty members, collaborative groups, institutes, initiatives, projects, and resources that fit your research goals.
  • Explain how a program’s structure fits your expectations and needs. You may choose to emphasize options for course selection or sequence, the interdisciplinary nature of the program, flexibility for arranging lab rotations, the program’s length, support for academic and professional development, or the presence in the program of particular researchers.

Get critical feedback

Obtaining feedback on your personal statement (or any piece of writing) can be intimidating, but feedback is essential for creating a polished and readable document. Asking a best friend for feedback may result in a canned response—“sounds good,” or “I like it”—which isn’t helpful.  Instead, seek feedback from trusted scientific peers, advisers, and mentors. Reading critiques of your writing can be disheartening and frustrating, but such feedback will continue throughout your career and is important for improving your communication skills—so get used to it.

You may find that comments on your personal statement vary widely and even contradict each other. Pay attention to all of them, and decide for yourself whether they make sense—but if there are consistent patterns in the critiques, i.e. the same suggestion made by all (or most) reviewers, that is certainly an area to revise.

To receive more meaningful constructive feedback, it may be helpful to ask your reviewers questions, such as these:

  • Is my personal statement convincing? Do you believe I really want to go to graduate school—to this graduate school—and that I understand why I want to go?
  • Are the examples appropriate? Does the statement hook the reader in and make them want to read more?
  • Does it answer the essay prompt?
  • Are the explanations of the research experiences clearly understandable for a nonexpert?
  • Does it convey the skills that I’m developing as a future scientist?
  • What about the writing? Is it well organized? Does it make sense? Are the transitions effective?

Proofread

Precision is an important part of science, and no graduate program is interested in candidates that don’t take (or appear to take) their admissions process seriously. An error-riddled essay sends precisely that message: Either you aren’t precise or you don’t care. Even a single typo can be a turnoff. So try to eliminate all obvious errors.

*          *          *

Even if you follow all this advice, I still can’t guarantee that you’ll get accepted to all of your dream graduate programs—that depends on the quality of all the work you’ve done up to now—but I can guarantee that your personal statement will improve and that you will look like a more authentic and substantial candidate. Good luck.

doi:10.1126/science.caredit.a1400252

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Brian Rybarczyk

Brian Rybarczyk is director of academic and professional development at UNC Chapel Hill's graduate school. He has a Ph.D. in pathology and laboratory medicine from the University of Rochester.

Participants

A total of 1557 healthy adults between the ages of 21 and 80 participated in this study (784 women and 773 men). Approximately the same number of men and women were distributed among the six decades in the age range (Supplementary Table S1). The complete sample included 1657 participants; 100 individuals were excluded because their data from the episodic memory task were lost due to technical difficulties. Participants were recruited in Mexico City through advertisements, appeals to community groups, flyers, and word of mouth. To be eligible for the study, participants had to have at least eight years of education, not have been addicted to drugs or alcohol, not have taken any medication that acted on the nervous system for the previous six months, not have neurological or psychiatric diseases, not have experienced head trauma and have normal or corrected-to-normal vision. Additionally, participants were required to obtain specific performance scores on psychological tests to ensure that they were not suffering from depression, dementia, or intellectual difficulties. The performance required was a score ≤ 20 on the Beck Depression Inventory (BDI), a score ≥ 24 on the Mini-Mental State Exam (MMSE), and a score ≥ 26 on the vocabulary subtest of the Wechsler Adult Intelligence Scale-Revised (WAIS-R). Supplementary Table S1 displays the participants’ scores on these tests by decade. All participants provided informed consent and received a monetary reward for his/her participation. The study was approved by the Bioethics Committee of the School of Medicine at the National Autonomous University of Mexico. All experiments were performed in accordance with relevant guidelines and regulations.


Measures

Metamemory in Adulthood38 is a scale composed of 108 questions and statements measuring knowledge, affects and beliefs about our own memory. A 5-point Likert scale was used to assess the degree of agreement with various sentences or the frequency of some behaviors. The scale measures seven dimensions: use of memory strategies, knowledge of memory tasks, knowledge of one’s own memory capacities, perception of memory change, relationship between anxiety and memory performance, achievement on memory tasks, and locus of control in memory abilities. The scale was translated into Spanish, and then the translated version was reviewed independently by 10 judges for linguistic and cultural validation consisting of assessing the equivalence of concepts in the questionnaire and adapting the concepts to the Spanish culture.

Food Frequency Questionnaire39 is a semi-quantitative instrument composed of 116 food items designed to assess dietary intake. Frequency of consumption of each food item over the previous year is measured using 10 frequency categories ranging from never, less than once a month, 1–3 times per month, once a week, 2–4 times per week, 5–6 times per week, once a day, 2–3 times per day, 4–5 times per day and 6 times per day. Nutrient intake was estimated using the Evaluation System of Nutritional Habits and Nutrient Consumption (SNUT) software developed by the National Institute of Public Health40. The validity and reproducibility of the Food Frequency Questionnaire have been confirmed for individuals living in Mexico City (e.g.,39). In addition, we asked participants how often they consumed canned food and processed food using the same frequency scale.

Social Readjustment Rating Scale (SRRS)41 is comprised of 43 positive and negative stressful life events. Participants are requested to respond if they had experienced each of the events in the last 12 months. The scale estimates the magnitude of readjustment to accommodate to a life event. The ratings assigned to each event were obtained from a sample of 394 persons that estimated the amount of social readjustment required for each event. Because the rates assigned to each event have been challenged, we scored the scale as the number of stressful events endorsed by each participant.

Lifestyle Questionnaire was created on purpose for the current study to examine education, occupation, income, health status, medication intake, tobacco, drug and alcohol consumption, and cultural, social, mental and physical activities. The questionnaire was applied as a semi-structured interview by psychologists who were carefully trained before collaborating in the study. Income was classified into nine categories ranging from less than 1000 Mexican pesos per month to between 1000–2000, 2000–4000, 4000–7000, 7000–10000, 10000–15000, 15000–20000, 20000–30000 or 30000 or more Mexican pesos per month. The retirement variable measured the time elapsed between retirement and the interview date. Health status was examined for the following systems: nervous, respiratory, cardiovascular, immune, sensory, digestive, renal, reproductive, endocrine, musculoskeletal, hepatic portal, and sleep. Participants were requested to report the diseases they have had formally diagnosed by a physician in each of the above systems during their lifetime.

Medications for antidepressants, neuroleptics, nootropics, hypnotics, anxiolytics, analgesics, amphetamines and hormonal therapy were assessed. The consumption of pharmaceutical drugs from each category was evaluated by asking participants if they had taken the medicine for the typical purpose or by asking for the reasons they are usually prescribed; the classification names listed above were not used in the interview. Participants were asked to provide the medication name, age of initial consumption and intake frequency and duration. The medicines were categorized afterwards by pharmaceutical specialists.

We investigated consumption of drugs (cannabis, hallucinogens, and cocaine), tobacco and alcohol by asking participants to communicate age of onset of consumption, frequency and duration of consumption and time since last consuming the drug. Additionally, for tobacco and alcohol, participants indicated the number of cigarettes and glasses of alcohol they usually have when they smoke and drink. Likewise, participants reported with which frequency they drink different alcoholic beverages (beer, wine, liqueur and spirits). Frequency was classified into 10 categories (never, once a year, three times per year, six times per year, once per month, two or three times per month, one or two times per week, three or four times per week, almost every day and daily). The amount of alcohol consumption was calculated as total grams per week based on the following equivalences: 6 gr of alcohol/200 mL beer, 9.6 gr of alcohol/100 mL wine, 25 gr of alcohol/50 mL liqueur, and 42 gr of alcohol/50 mL spirit.

Participants were requested to report the frequency and time they spent in physical activity (aerobic and anaerobic exercise), mental activity (watching television, listening to the radio, using the computer and reading), attending cultural events (film screenings, theater plays, exhibitions, concerts, conferences or courses), attending social events (parties or reunions) and hobbies. Participants were asked for the type of exercise they performed most frequently. Additionally, participants indicated the genres of television, radio and literature they most often chose. Likewise, the kind of activity they most frequently performed on the computer was assessed. The same 10 frequency categories described above for drug intake were used for these variables.

The scores obtained on the BDI, MMSE and WAIS-R Vocabulary subtest screening tests were also included in the structural equation modeling analyses.


Stimuli

A total of 110 color images representing natural and artificial common objects (50% from each category) were used in the source memory paradigm, and 2 of them were presented at the beginning of the encoding and retrieval tasks and were not analyzed (Fig. 1a). Additionally, another 12 images were employed in a practice session. From the set of 108 images, 72 of them were randomly selected (equal proportion of natural and artificial objects) for each participant to be presented during the encoding phase, and the complete set of 108 images was randomly presented at the retrieval phase. Each image subtended vertical and horizontal visual angles ranging from 2.9° to 4.3° and were displayed on a white background screen.


Procedure

The study started in 2003 and lasted six and a half years. Approximately the same number of participants from each decade in the age range was evaluated each year. Participants attended two sessions of about two hours each. Graduate psychologist collaborators conducted the experimental sessions and applied the instruments after several months of training. The training consisted of learning how to interview, apply psychological tests, evaluate visual acuity, and administer the memory tasks. To ensure that the collaborators had acquired the necessary skills before being allowed to formally carry out the study, they were observed several times and evaluated through a Gesell chamber while conducting interviews and applying psychological tests. The collaborators were also supervised and evaluated while they administered the memory tasks to guarantee that the procedure was applied consistently to all of the participants. Potential participants were checked through prescreening questions if they fulfilled the inclusion and exclusion criteria prior to being invited to attend the first session. The first session occurred in a silent room in which only the participant and the experimenter were present. At the beginning of this session, participants were further interviewed to confirm that they satisfied the inclusion criteria. Afterwards, participants were tested with the WAIS-R Vocabulary subtest, the MMSE and the BDI, and their vision was tested. Participants that were eligible for the study were asked to provide their informed consent. Afterwards, participants completed the Lifestyle Questionnaire, followed by the Food Frequency Questionnaire and the SRRS, which were completed in a counterbalanced order. Then, the Annett Hand Preference Questionnaire was administered. At the end of the session, participants had the Metamemory in Adulthood questionnaire explained, which was then handed to them to be answered at home. Participants were also asked to record all foods they ate for three days in special formats (data not analyzed here). Finally, participants’ weight and height were measured.

In the second session, participants’ glucose, cholesterol and triglycerides were measured in a non-fasting state with the Accutrend Plus System, Roche Diagnostics, Rotkreuz, Switzerland. These measurements were taken in a counterbalanced order. Afterwards, participants performed a working memory task (data not shown) in addition to the source memory task, which consisted of an encoding and a retrieval phase, in a sound-dampened chamber. Only one room was used for this session. The participants were seated in a high-back armchair 100 cm away from the monitor screen. Blood pressure and heart rate were measured with a digital upper arm sphygmomanometer Hem-712C, Omron, Kyoto, Japan. Mean arterial pressure (MAP) was estimated as [(2 × diastolic blood pressure) + systolic blood pressure]/3. Skin conductance responses were recorded by placing an electrode in the annular and medium fingers of the non-dominant hand (data not presented here). The response panel, adapted for right- or left-handed participants, was located on a platform on the left or right armchair according to the participant’s handedness. The response panel consisted of four keys arranged in two columns of two rows each to be pressed by the index and middle finger, and a fifth key located in the lower portion of the response panel to be pressed by the thumb. The four keys represented the quadrants of the screen where the images were presented during the encoding phase. Only the two keys in the second row were used during the encoding phase, while all five keys were used during the retrieval phase. The participants performed a training session that involved a shorter version of the encoding and retrieval tasks to learn how to use the response panel. The stimulus presentation and response recording were controlled by E-Prime software v1.0, Psychological Software Tools, Pittsburgh, PA, USA.


Source memory paradigm

Throughout the encoding task, a cross divided the screen into quadrants; the center of the cross was in the middle of the screen. The images were randomly displayed in one of the quadrants with the same probability of appearing in each of them. The images were displayed at a distance ranging from 0.5° to 1.25° away from the axes of the cross dividing the screen. Each trial started with the presentation of an image for 1000 ms followed by a 3000 ms period when only the cross remained on the screen. The task consisted of classifying whether the images represented a natural or an artificial object by pressing one of two keys. Participants were able to respond after the onset of the stimulus during a period of 3500 ms. Participants were instructed to concentrate on the encoding task because they knew that their memory would be tested.

Approximately three minutes after the encoding task was completed, the retrieval task began. The screen was not divided into quadrants, and the images were presented in the center of the screen. The same timing used in the encoding task was used in the retrieval task. Participants were asked to judge whether the image was new or old. If the image was old, they indicated the quadrant where the image was originally presented during the encoding phase by pressing one of the four keys that represented each quadrant of the screen. If the image was new, participants used their thumb to press the lower (fifth) key on the response panel. Participants were instructed to randomly select one of the four quadrants if they were confident that the image was old but were unable to remember its exact position.


Data analysis

Structural equation modeling (SEM) was conducted by using Stata v. 13 Texas, USA with the maximum likelihood estimation procedure. A hypothetical model was developed a priori based on previous empirical findings that demonstrated that the variables included in the model were associated with or had an effect on episodic memory, memory in general or cognition. The model consisted of one exogenous variable: participant’s age. The ultimate outcome variable was source memory accuracy, which was estimated as the percent of recognition hits accompanied by a correct source response. Note that the images that were incorrectly classified during the encoding task were excluded from the analyses. The rest of the variables were considered endogenous mediator variables that intervene between age and source memory in a causal sequence. Thus, we estimated the magnitude of the direct effect of age on source memory not mediated by other variables in the model. Also, we calculated the indirect effect of age mediated by the other variables in the model, and the indirect effect of each mediator variable by multiplying the coefficients for the two linked direct effects. Data were not developed by any reduction technique. Therefore, only observed variables were included in the model because they represent precise evidence of the domains of interest, and they can be directly compared with predictors from other studies.

A total of 120 variables were considered as potential mediator variables, and none of them had missing values. Descriptive analyses were conducted to assess all study variables. Those variables with skewness exceeding ±3 were natural log-transformed. Then, we applied linear regression analyses between all variables and the correct source to select the variables that would be entered in the SEM analyses. Only those variables with a significant (P < 0.05) effect on source memory were selected. Linear regressions were not corrected for multiple comparisons because they were not used to test alternative hypotheses. The development of the model was completed in several steps. First, all the variables that had a significant effect on source memory accuracy were included in the SEM analysis. After the paths were fitted, the model was fine-tuned by eliminating endogenous variables, which paths had t values that were no longer significant (P > 0.05). Then, we repeated the SEM analysis with only the endogenous variables that remained significant. If necessary, we repeated this procedure until all mediator variables’ paths were significant in the model. Goodness-of-fit was assessed with the χ2 likelihood ratio with degrees of freedom, relative or normed χ2 (χ2/degrees of freedom), the comparative fit index (CFI), the root mean square error of approximation (RMSEA) and the standardized root mean squared residual (SRMS). Finally, we performed a bootstrapped test of mediation42 with 20,000 replications to estimate the 95% bias-corrected confidence intervals for each mediator variable and the total indirect effect.


Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.