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December 7th, 2016

The Career Value of the Humanities & Liberal Arts

In 2013, the Wall Street Journal published an article based on the findings of a Harvard University study stating that “the percentage of humanities degrees (as a share of all undergraduate degrees attained) has dropped by half since the 1960s.” According to the Harvard study, liberal arts and humanities are “attracting fewer undergraduates amid concerns about the degrees’ value in a rapidly changing job market.”

Harvard and the WSJ’s inference was that there are no jobs—and hence no money—for graduates of liberal arts and humanities programs.

Following this study, liberal arts departments and humanities scholars all over the country dove into research with the intent of answering two questions:

  1. Is enrollment in the liberal arts and humanities dropping dramatically?
  2. Do liberal arts and humanities degrees lack value that can be monetized?

The answer they found was slightly more nuanced:

While graduates with humanities and liberal arts degrees may earn less immediately after graduating, in the long run they actually earn more on average than those with professional degrees.

The Value of the Humanities & Liberal Arts

 

True Numbers, but Slanted Implication

The answer to the question about enrollment in humanities and liberal arts programs, researchers found, is that while there has been a drop off, it has been small. Enrollment has dropped since the peak it achieved during the Vietnam War. However, when compared to 1950s numbers, enrollment percentages are only slightly lower.

More importantly, “the percentage of college-age Americans holding degrees in the humanities has increased fairly steadily over the last half-century, from a little over one percent in 1950 to about 2.5 percent today,” according to Harvard graduate and Northeastern University professor Ben Schmidt.

Percentage of Humanities and LA degrees in relation to population

Percentage of Humanities and LA degrees in relation to population. Chart: Ben Schmidt.

Questioning the Realities of a Bachelor of Arts Degree

In an era of rising higher education costs, the debate over the value of obtaining a liberal arts degree is important. Yet just as some seemed ready to write off the value of these programs, many others are finding that the conceptual and analytical skills that study in the humanities and liberal arts promotes are becoming more crucial than ever before.

Ironically, this may be particularly true in the field of tech, which tends to be dominated by coders and engineers. As George Anders writes for Forbes, “The more that audacious coders dream of changing the world, the more they need to fill their companies with social alchemists who can connect with customers–and make progress seem pleasant.”

Humanities, liberal arts and social science majors are often saddled with clichés linking a bachelor of arts degree with the ability to reason and write, but few other skills. Traditionally, the assumption was made that graduates with degrees in other fields of study had larger skill sets and could write and reason.

But that is no longer the case. According to Verlyn Klinkenborg, a professor of nonfiction writing who has taught at Harvard, Yale, Bard, Pomona, Sarah Lawrence and Columbia’s Graduate School of Journalism, “many college students graduate without being able to write clearly.” In technical fields, this skill is simply not as important and thus not practiced and perfected.

While doing research in both science and humanities journals for their book Academically Adrift: Limited Learning on College Campuses, Richard Arum and Josipa Roksa discovered that “Students majoring in liberal arts fields see ‘significantly higher gains in critical thinking, complex reasoning, and writing skills over time than students in other fields of study.'” By contrast, students majoring in business, education, social work and communications showed the smallest gains. Yet critical thinking and complex reasoning are crucially important to success in the marketplace.

Short and Long-Term Employment Realities of Liberal Arts and Humanities Graduates

Gaining employment as a liberal arts or humanities major can be difficult and often means taking a salary that is less than one’s counterparts. Studies indicate, however, that in the end, liberal arts or humanities majors actually make more money than the vast majority of graduates with degrees in other fields. In Inside Higher Ed, Allie Grasgreen reports,

“By their mid-50s, liberal arts majors with an advanced or undergraduate degree are on average making more money than those who studied in professional and pre-professional fields, and are employed at similar rates”

According to a joint report conducted by AAC&U and the National Center for Higher Education Management Systems, “while in the years following graduation they earn $5,000 less than people with professional or pre-professional degrees, liberal arts majors earn $2,000 more at peak earning ages.”

This long-run advantage takes hold, in particular, when liberal arts and humanities graduates go on to obtain graduate degrees.

 

Humanities is the Future

As technology continues to expand and the number of people with high levels of technical proficiency grows, a greater number of the available jobs will require the multidisciplinary skill set provided by a bachelor of arts degree. Technical jobs are often easier to mechanize, particularly as our computing abilities continue to grow and as our societies become increasingly networked.

The humanities are a good bet because the things that are hardest to computerize or outsource are going to be all about skills that emphasize human interaction. Empathy, sociability, writing, analyzing, and reacting to people — all things more likely to come from the humanities than hard sciences.”

The liberal arts and humanities teach students to think at an abstract level and to make connections that technology, so far, simply cannot emulate. In this sense, these degrees teach more than just skills, but rather a way of thinking that will always have an important, strategic value—even in a marketplace that is based on scientific and technological development. 

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November 23rd, 2016

What is the Secret to Success?

At hundreds of colleges and universities across the country, thousands of students are in the midst of the fall semester, trying to manage the academic tasks of studying, exams, papers and lectures. A lot is riding on their academic performance – earning (or just keeping) scholarships, landing summer internships, gaining employment and of course acquiring new skills and knowledge.

The vast majority of students will tell you they intend to do well, that they know it takes hard work to succeed. But some students will end up hitting more bars and parties than books. That is, not everyone ends up putting in that hard work.

In our own work, we have found that asking college students questions like, “How important do you think it is to do well at college?” gives us essentially no information about who will do well in terms of grades.

College students are hardly unique in not following through on their intentions and goals. Frustrated parents might do well to look to their own unused gym memberships or perennial weight-loss resolutions to realize that intentions are not always sufficient to ensure steady progress toward one’s goals.

Why is there such a disconnect between our intentions and our actions? And, how can we predict who has the grit to succeed, if we can’t depend on what people tell us?

The Secret to Success in School

 

Explicit or Implicit Beliefs?

When people are directly asked how important they think it is to succeed at some goal, they are reporting their “explicit beliefs.” Such beliefs may largely reflect people’s aspirations, such as their sincere intentions to buckle down and study hard this semester, but these may not always map onto their subsequent inclination to persist.

Rather than depend on people’s explicit beliefs, in our research we looked instead to people’s implicit beliefs.

Implicit beliefs are mental associations that are measured indirectly. Rather than asking the person to state what they think about some topic, implicit measures use computerized reaction-time tasks to infer the strength of someone’s implicit associations. For instance, a great deal of research by psychologists Brian Nosek, Tony Greenwald and Mahzarin Banaji over the last two decades has shown that people often hold negative implicit associations about members of stigmatized racial and ethnic groups.

Even though many participants in these studies explicitly stated they believed in fairness and equality among racial groups, they nevertheless showed implicit biases toward racial and ethnic groups. In other words, whereas people “said” they were egalitarian, they in fact possessed strong negative associations in their mind when it came to certain racial groups.

Implicit associations are critical to understand because they can predict a range of everyday behaviors, from the mundane (what foods people eat) to the monumental (how people vote).

But do implicit associations predict who has the grit to succeed at life’s difficult goals?

 

Here’s What We did

To find out, instead of measuring people’s explicit beliefs about the importance of their goals, we measured people’s implicit beliefs about the importance of an area (e.g., schoolwork, exercise) and then measured their success and persistence at relevant tasks (e.g., grades, gym regimens).

We used a computer-based test called the “Implicit Association Test (IAT)” to measure our participants’ implicit beliefs. The test takes about seven minutes to complete. Participants have to don noise-canceling headphones and sit in a distraction-free cubicle.

In five of our studies, we used this test to measure students’ cognitive association between “importance” and “schoolwork.” Student participants were asked to indicate, as quickly as they could, using computer keys, whether each of a series of words was related to “schoolwork,” was a synonym of “importance” or was a synonym of “unimportance.” Examples of such words included “exam,” “critical” and “trivial.”

The test was set up in such a way so that even a slight difference in the speed of response (at the level of milliseconds) could reveal differences in the strength of the association between schoolwork and importance.

In short, it allowed us to measure the extent to which people implicitly believed that schoolwork was important.

 

Multiple Studies to Corroborate

Could millisecond differences in reaction times meaningfully capture people’s beliefs and predict success in their goals? For instance, could this seven-minute-long measure of milliseconds predict who would earn straight A’s in their college classes?

We found that they did. And we didn’t observe this relationship just once. We found that again and again – across seven different studies, run in different labs, with different populations and predicting different types of persistence and success. Across five studies, we found that college students’ implicit belief in the importance of schoolwork predicted who got higher grades.

We didn’t limit our study to college performance. We also tested other goals, such as going to the gym. We found that those who had a stronger association between importance and exercise were significantly more likely to exercise more often and more intensely.

Then we conducted a test to find out how implicit beliefs predicted test-taking abilities. We tested college students’ implicit beliefs about the importance of the GRE (Graduate Record Examination), a widely used exam that helps determine graduate school admissions and scholarships. Those who showed a stronger association between importance and the GRE scored significantly better on a practice GRE test.

 

A Unique Measure of Likelihood of Success

Like any measure, ours wasn’t perfect. We couldn’t always predict in every instance who would succeed or fail. But our brief computerized test provided new insight into who was likely to succeed – an insight not captured by more traditional measures.

For example, higher SAT scores are taken to be a measure of who will likely do better at college and better on the GRE. Our data did show that SAT scores are a good predictor of both. However, knowing participants’ implicit beliefs in the importance of school or the GRE predicted success over and above what SAT scores could tell us. In other words, even when two people scored the same on the SAT, the one with the stronger implicit belief about the importance of the GRE tended to score better on the practice exam.

One interesting finding in our studies was that implicit beliefs predicted some people’s success more than others. Closer examination showed that those for whom exerting self-control was difficult – those who said they have trouble completing assignments on time, who could be easily dissuaded from making it to spin class or who have difficulty maintaining focus during long reading comprehension passages – were those who most benefited from having a strong implicit belief that the goal was important.

In other words, it was those individuals in need of a boost who most clearly benefited from the implicit nudge that their pursuits were important.

 

What Exactly is the Role of Implicit Beliefs?

Our work adds to a growing body of evidence that the ordinarily hidden-from-view, implicit associations in our mind offer new insights about many everyday decisions and behaviors.

Implicit associations can predict success at some of life’s most challenging tasks.

For example, just as implicit associations can predict intergroup behavior, first impressions of other people and voting behavior, our new findings show that they also predict success at some of life’s most challenging tasks.

However, there are still some questions that remain. For example, do implicit beliefs in the importance of working hard actually cause people to do better, or do they simply identify who is likely to succeed? Could changing people’s implicit beliefs have real effects on their prospects for success?

To be clear: It is certainly not the case that what people say about how much they care about something does not matter at all. Indeed, we would guess that people who say they care nothing about exercising will not be heading to the gym, regardless of their implicit associations between exercise and importance.

But, especially among those who say they do care about something – such as the vast majority of college students caring about their performance at school – a measure of their implicit beliefs may give us a better idea about how likely they are to succeed.

Melissa J. Ferguson, Professor of Psychology, Cornell University and Clayton R. Critcher, Associate Professor of Marketing, Cognitive Science, & Psychology, University of California, Berkeley

This article was originally published on The Conversation. Read the original article.

The Conversation

November 6th, 2015

How to Use Regression Analysis Effectively

So, you want to use regression analysis in your paper? While statistical modeling can add great authority to your paper and to the conclusions you draw, it is also easy to use incorrectly.

The worst case scenario can occur when you think you’ve done everything right and therefore reach a strong conclusion based on an improperly conceived model. This guide presents a series of suggestions and considerations that you should take into account before you decide to use regression analysis in your paper.

The best regression model is based on a strong theoretical foundation that demonstrates not just that A and B are related, but why A and B are related.

How to Use Regression

Before you start, ask yourself two important questions: is your research question a good fit for regression analysis? And, do you have access to good data?

1. Is Your Research Question a Good Fit for Regression Analysis?

This depends on many different factors. Are you trying to explain something that is primarily described by numerical values? This is a key question to ask yourself before you decide to use regression. Although there are various ways to use regression analysis to describe non-numerical outcomes (e.g., dichotomous yes/no or probabilistic outcomes), they become more complicated and you will need to have a much deeper understanding of the underlying principles of regression in order to use them effectively.

Before you start, consider whether or not your dependent variable is numerical. Some examples:

  • Number of years a politician serves in Senate
  • Life expectancy
  • Lifetime earnings
  • Age at birth of first child

At the same time, you need to make sure that there is sufficient variation in your dependent variable and that the variation occurs in a normal pattern.

For example, you would have a problem if you tried to predict the likelihood of someone being elected as president because almost no one is elected as president. As a result, there is virtually no variation on the dependent variable.

2. Do You Have Access to Good Data?

Before you can conduct any type of analysis, you need a good data set. Not all data sets are easily suited to regression analysis without considerable manipulation.

Some things to consider before you decide to use regression:

  • Are most of your independent variables numerical in nature? The best data set for regression will have variables that are primarily described by numbers that vary on a continuous scale. On the other hand, if most of your variables are categorical, you might consider using a different method of analysis (e.g., Chi-squared).
  • Are there enough cases (n) in your data set? Particularly if you think you might use multiple regression, where multiple independent variables are used to predict a single dependent variable, you need to have a sufficient number of cases in your sample to obtain significant results. A general rule of thumb is that you need at least 20 cases per independent variable in your model. So if your model includes 5 independent variables, you need a minimum of 100 cases.

Keep in mind that your independent variables need to meet the same criteria for normality and variability as your dependent variable.

 

Once you decide to proceed with a regression model in your analysis, there are a three key concepts to keep in mind as you design your model to avoid making an easily preventable mistake that could send your conclusions way off track.

  • Parsimony
  • Internal Validity
  • Multicollinearity

Each is described in more detail below.

Parsimony

In statistics, the principle of parsimony is based on the idea that when possible, the simplest model with the fewest independent variables should be used when a model with more variables offers only slightly more explanatory value. In other words, one should not add variables to a model that do not increase the ability of the model to explain something.

Only add variables to a model if they significantly increase the ability of the model to explain something.

If you add too many variables to your model, you can unwittingly introduce major problems to your analysis.

In the extreme case, you must consider that your R2 value will always increase with the addition of new variables: so if you examine R2 alone, you can be duped into thinking that you have a great model simply by dumping in more and more predictor variables.

There are two good ways to address this problem: use an Adjusted R2 to compare models with different numbers of predictors, and use stepwise regression to analyze the explanatory impact of each variable as it is added to the model.

  • Adjusted R2 takes into consideration the number of variables used in the model, and only increases when the addition of a new variable explains more than would random chance alone. So although a model with 10 variables might have a very high R2 value, the Adjusted R2 could actually be much lower than a model with fewer variables. Selecting your model based on Adjusted R2 helps you select a more parsimonious model that is less likely to have other problems (e.g., see multicollinearity below).
  • Stepwise Regression is a computational method of assessing the additional explanatory value of each variable as they are added to the model in different orders. It can be used to parse out superfluous variables from a model, however it needs to be used carefully and in concert with theoretical guidance to avoid overfitting your data.

A good rule of thumb as you consider different models is that you should always have a good reason to add a predictor variable to your model, and if you can’t come up with a good theoretical explanation as to why A influences B, then leave out A!

Internal Validity

Internal validity is the degree to which one factor can be said to cause another factor based on three basic criteria:

  1. Temporal precedence, i.e., the “cause” precedes the “effect.”
  2. Covariation, i.e., the “cause” and “effect” are demonstrably related.
  3. Nonspuriousness, i.e., there are no plausible alternative explanations for the observed covariation caused by a confounding variable.

In many cases, internal validity becomes an issue in the form of a “chicken and egg” problem.

For example, let’s say you are considering the relationship between obesity and depression (a common example). If you want to include depression as an independent variable to explain obesity in your model, you first need to consider the question:

Does depression lead to obesity, or does obesity lead to depression?

If you have no clear theoretical guidance to show that, in fact, depression usually precedes obesity (temporal precedence), you could introduce a significant problem to your model if the relationship is in fact the other way around: depression being the result of obesity.

Therefore, as you craft your model it is important to have a theoretical basis for the inclusion of each variable.

Multicollinearity

Multicollinearity occurs when the independent variables in a multiple regression model are highly correlated with one another. This can be a problem in several ways:

  • It reduces the parsimony of your model if the two variables are highly similar (e.g., two different variables that effectively measure the same thing);
  • Multicollinearity can lead to erratic changes in the coefficients (measured effect) of predictor variables;
  • As a result, it can be difficult to interpret the results of a model with high multicollinearity among predictors. Specifically, it becomes impossible to discern the individual effect of different regressors.

An example of variables that are going to be highly multicollinear are any variables that effectively measure the same thing. One way to show this, for the purposes of an example, is to imagine converting categorical data into a series of binary variables.

Any variables that effectively measure the same concept are likely to have high collinearity.

For example, let’s say that we have a variable measuring memory where respondents are able to choose very good, average, or poor as a response.

One way to use this data in a regression model would be to convert the data into three dichotomous (yes/no) variables indicating a person’s response.

However, if you then include all of these dichotomous variables in your model, you will have a big problem because they will become perfectly multicollinear. This is because anyone who indicated that they had a very good memory, by default, also indicated that they do not have a poor memory. The two variables measure the same thing: a person’s memory.

Another common example can be found in the use of height and weight variables. Although the two variables measure different things, broadly speaking they can both be said to measure a person’s body size, and they will almost always be highly correlated.

As a result, if both variables are included as predictors in a model, it can be difficult to discern the effect that each variable has individually on the outcome (measured by the coefficient).

Thus, as you build your model, you need to be aware of the potentially confounding impact of using highly similar predictor variables. In an ideal model, all independent variables will have no or very low correlation to each other, but a high correlation with the dependent variable.

 

Conclusion: Use Regression Effectively by Keeping it Simple

Regression analysis can be a powerful explanatory tool and a highly persuasive way of demonstrating relationships between complex phenomena, but it is also easy to misuse if you are not an expert statistician.

If you decide to use regression analysis, you shouldn’t ask it to do too much: don’t force your data to explain something that you otherwise can’t explain!

Moreover, regression should only be used where it is appropriate and when their is sufficient quantity and quality of data to give the analysis meaning beyond your sample. If you can’t generalize beyond your sample, you really haven’t explained anything at all.

Lastly, always keep in mind that the best regression model is based on a strong theoretical foundation that demonstrates not just that A and B are related, but why A and B are related.

If you keep all of these things in mind, you will be on your way to crafting a powerful and persuasive argument.

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March 30th, 2015

How to Manage a Group Project (Video)

Group work is an inevitable part of most university courses and the ability to work well with other people is something all employers care about. While working on a group project can be incredibly rewarding, it can also present real challenges if you don’t go in with the right mindset. Here are a few tips to make group work just a little bit easier!

Be Prepared to Compromise

Something we must learn early in life is that different people have different working styles. While some people like to have an essay planned out and written weeks in advance, others thrive on the pressure of leaving it until the last minute. Be open about how you work from the start – if you talk about the ways in which each person works best right away, you can come up with a compromise that suits everyone.

If everyone compromises a little – for example, by agreeing to pre-planned deadlines – this can help avoid leaving some group members stressed or upset by discovering that their expectations were out of line with the rest of the group.

 

Maximize Each Member’s Strengths

Do you love public speaking? If so, great – tell your group members that from the start! Break down everything that has to be done, from conducting the research to preparing the slideshow and giving the presentation in front of the class, and assign tasks to each person based on their strengths.

While it can be difficult to please everyone, having an honest discussion about strengths/weaknesses early on and and attempting to give everyone tasks that they’re comfortable with will benefit the entire group in the end.

 

Stand Up for Yourself and Do the Work

People have different personalities, so if you are naturally shy and are put in a group with someone more confident, it can be tempting to shrink up and not say or do anything, even when you think that the group might be headed in the wrong direction – this is a mistake!

As scary as it is, make sure you stand up for yourself and speak up. This is the only effective antidote to groupthink and conversations where not everyone immediately agrees can be incredibly fruitful.

Of course it goes without saying, always put in the work. Don’t be the person that shows up with the job half done. It is common for group projects to include peer assessments and if you don’t put in the effort, your classmates won’t be shy.

 

Choose Your Group Wisely

If you are given the opportunity to choose your group members, the temptation is often to work with your friends. Sometimes that is for the best because you know each other well and it can make working on the project more fun and less stressful. However, it can also lead to even more tension, particularly if you aren’t diligent about assigning tasks and preparing some deadlines from the get go.

Someone who you have a lot of fun with on a night out might not make the best partner for a group project. (For one thing, this can make it much easier to get distracted!)

There are also a lot of benefits to working with people you don’t know – it can give your project a wider range of perspectives and help you capitalize on differentiated skills as a group. Moreover, you might even end up making a great new friend.

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March 23rd, 2015

Presentation Tips 101 (Video)

Presentations have become an integral part of most university and college courses. While some students won’t think twice before getting up to speak in front of a room full of people, for others, the thought of being in the spotlight can become overwhelming. It’s natural to be nervous, but for many students, those nerves can spiral out of control, making you feel anxious for days leading up to the event.

Here are a few tips which will hopefully help to make you feel as comfortable as possible before giving your next group or solo presentation!

Perfect Your Slides

If you are required to make a visual background for your presentation on something like PowerPoint, make a really good job of it! Rather than cobbling together some blank slides with a couple of paragraphs on them the night before, take some time to make them look amazing!

A well-structured, nicely designed slideshow will show your teacher and classmates that you put a lot of work into the presentation. This tells your audience that any visible nerves are purely due to public speaking and not from a lack of preparation.

Having your key points outlined briefly on your slides also means that if your nerves get the better of you and you lose track of where you are, your slides will quickly guide you back to where you were.

 

Practice, Practice, Practice!

It sounds obvious, but the worst thing you can do if public speaking worries you is not run through your presentation a good few times in advance! Start off alone, speaking aloud in your bedroom or an empty classroom. Then ask some friends to act as a test audience for you.

Not only will this likely lead to useful feedback on your content, but it will make you feel more comfortable in front of a crowd, too! It allows you to practice key strategies — such as eye contact — which will improve your presentation. Your tutor wants to see that you are engaging with the class, so getting used to being in front of others is really helpful.

 

Make Use of Note Cards

Note cards can be a useful tool to take advantage of, but do make sure you check that you are allowed to use them first! Having cards with brief summaries (not the full script of your presentation) can help to keep you on track and, much like the slideshow, can give you the confidence of knowing exactly what’s coming next.

However, they can also be a useful tool to stop you from fidgeting, something which is ever so tempting when you’re nervous! If you have cards to hold, you won’t be as likely to touch your hair, fidget with a pen or fiddle with your jacket!

 

Plan Something Nice for Later!

Depending on your schedule, if you can afford to take a little time out to go out for dinner with friends, go to the cinema, or even just go for a walk round the shops – do it! Knowing you have something fun planned for after the big event can make it so much easier to get through the stress of a presentation. You might still be nervous, but knowing that no matter what, the rest of your day is only going to get better, is a great feeling! That alone might be enough to make you feel more settled.

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March 10th, 2015

Finding Balance in Graduate School

 In the darkest days of the graduate school “doldrums,” as you wade through readings and midterms and papers, it can be hard to recall why exactly you decided to go to grad school in the first place. Even though you might feel like you can hardly find time to breathe, the truth is you can make time for relaxing, catching a movie, spending time with your partner, or whatever else you enjoy — if you try.

How can you balance graduate school with enjoying your personal life? Here are five things you can try:

Finding balance between grad school and your personal life

  1. Schedule School Like A 9-to-5

Grad school can often feel like a 24/7 job where you need to be thinking about your research, coursework, and teaching all the time in order to compete in the academic job market. Not so!

If you discipline yourself, you can work a semi-regular “shift” and still make time for dating, relaxing, and hobbies. Figure out when your most productive daytime hours are, and schedule 8-10 working hours during that time. If you stay on task during these hours, you can feel good about shutting it down to enjoy some personal time.  Of course there will always be emergencies and last-minute deadlines, but by scheduling working shifts you can actually minimize their occurrence and lead a more ‘normal’ day-to-day life.

  1. Make Working Time Productive

Procrastination during your scheduled hours will drag work into your personal time, so you need to find strategies to stay productive and on task. Download an app that blocks time-wasting websites; write from a computer with internet disabled; meditate or go for a walk – whatever you have to do to stay on task.

Schedule short breaks every 60 to 90-minutes so that you stay energized and give your brain some relief.

  1. Set Goals & Reward Yourself

If you’ve ever had a pet, you know how effective small rewards can be. But you are trainable, too! Set realistic goals for yourself and then reward yourself when you meet them. Small rewards for finishing tasks or meeting goals can go a long way toward keeping you motivated. Figure out what you respond to – a Starbucks coffee? A homemade cookie? A night out dancing? – and reward yourself when you meet set goals. The more that you give yourself rewards, the more you will be willing to meet your own goals when you set them.

  1. Schedule Personal Time

Some people dislike the idea of penciling in their partners or setting aside a block of time for pleasure reading – but given how graduate school work tends to expand to fill all of your time, scheduling off chunks of time to take care of your personal needs might be the smartest way to make sure they don’t get constantly sidelined. So schedule yourself some free time, put your school work away, and indulge.  You’ll find that the more you allow yourself to refresh your brain, the more you will actually get done when it’s time to work – because your mind will be focused on work, and not how tired of working you are.

  1. Banish the Guilt

It’s easy to envision yourself working productively around the clock to finish academic obligations or publish one more paper. The flip side is that you often guilt yourself when you aren’t working – you think that any minute you spend relaxing could be spent working! But the truth is, even if you love your research area, it’s easy to get “burned out” in academia.

If you work around the clock, you can get disillusioned and discouraged. The more exhausted you are mentally and spiritually with your work, the harder it is over the long-term for you to produce high-quality scholarship. You have to take breaks in order to produce your best work.

Divest yourself from the guilt that graduate school can bring. Whenever you feel guilty for spending time on non-work things, mentally change the subject and remind yourself that it’s OK to spend time relaxing and recharging – even more, it’s healthy.

So divest yourself from the guilt that graduate school can bring. Whenever you feel guilty for spending time on non-work things, mentally change the subject and remind yourself that it’s OK to spend time relaxing and recharging – even more, it’s healthy.  This is a “fake it ’til you make it” kind of thing – you will have to actively pretend you don’t feel guilty at first. Spend more time focused on producing the highest quality work and less time on berating yourself. Beating yourself up is never productive anyway! So stay positive and learn to focus on a positive reinforcement-based schedule. The more you do this, the less guilt you will eventually learn to feel during time off.

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September 10th, 2014

5 Tips for Publishing Your First Academic Article

Publishing an article in an academic journal can be a frustrating process that demands a substantial commitment of time and hard work. Nevertheless, establishing a record of publication is essential if you intend to pursue a career as an academic or scientific researcher.

These five suggestions will help you turn the odds in your favor and make the publishing process less daunting.

Read the rest of this entry »

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