Preparing for graduate school interview questions

Grad school interviews are the last step of the application process, so congratulations for making it to this stage! Getting this far is a big accomplishment—graduate schools only conduct interviews with those applicants they are seriously considering accepting.

Grad schools conduct interviews to assess your “fit” with their program and faculty, as well as your interpersonal skills. In many cases, they may also be attempting to match you with a supervisor.

Before the interview, you should prepare by doing your research and reflecting on how you’ll answer these common questions.

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When to apply for graduate school

Once you’ve decided to apply for graduate school, you need to carefully plan out the application process, leaving yourself enough time to:

In general, you’ll need to start preparing your application at least 6 months in advance of the deadline. Most application deadlines are about 7–9 months before the program’s start date.

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Master’s vs PhD: The key differences

The two most common types of graduate degrees are master’s and doctoral degrees:

  • A master’s is a 1–2 year degree that can prepare you for a multitude of careers.
  • A PhD, or doctoral degree, takes 3–7 years to complete (depending on the country) and prepares you for a career in academic research.

A master’s is also the necessary first step to a PhD. In the US, the master’s is built into PhD programs, while in most other countries, a separate master’s degree is required before applying for PhDs.

Master’s are far more common than PhDs. In the US, 24 million people have master’s or professional degrees, whereas only 4.5 million have doctorates.

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How to ask for a letter of recommendation for graduate school

Letters of recommendation often make or break a graduate school application. It’s important to think carefully about who to ask and how to do it.

Ideally, you should approach former supervisors who know you and your work well. Different programs require different types of recommendation letters, but the process of requesting them is similar.

Follow these five steps to guarantee a great recommendation, including program-specific tips and email examples.

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How to use stratified sampling

In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e.g., race, gender identity, location, etc.). Every member of the population studied should be in exactly one stratum.

Each stratum is then sampled using another probability sampling method, such as cluster or simple random sampling, allowing researchers to estimate statistical measures for each sub-population.

Researchers rely on stratified sampling when a population’s characteristics are diverse and they want to ensure that every characteristic is properly represented in the sample.

The procedure of stratified sampling.

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An introduction to cluster sampling

In cluster sampling, researchers divide a population into smaller groups known as clusters.  They then randomly select among these clusters to form a sample.

Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed. Researchers usually use pre-existing units such as schools or cities as their clusters.

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An introduction to simple random sampling

A simple random sample is a randomly selected subset of a population. In this sampling method, each member of the population has an exactly equal chance of being selected.

This method is the most straightforward of all the probability sampling methods, since it only involves a single random selection and requires little advance knowledge about the population. Because it uses randomization, any research performed on this sample should have high internal and external validity.

The American Community Survey (ACS) uses simple random sampling. Officials from the United States Census Bureau follow a random selection of individual inhabitants of the United States for a year, asking detailed questions about their lives in order to draw conclusions about the whole population of the US.

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An introduction to quasi-experimental designs

Like a true experiment, a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable.

However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria.

Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons.

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What is a double-blind study?

In experimental research, subjects are randomly assigned to either a treatment or control group. A double-blind study withholds each subject’s group assignment from both the participant and the researcher performing the experiment.

If participants know which group they are assigned to, there is a risk that they might change their behavior in a way that would influence the results. If researchers know which group a participant is assigned to, they might act in a way that reveals the assignment or directly influences the results.

Double blinding guards against these risks, ensuring that any difference between the groups can be attributed to the treatment.

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