What’s the difference between random selection and random assignment?
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Methodology refers to the overarching strategy and rationale of your research project. It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section.
In a longer or more complex research project, such as a thesis or dissertation, you will probably include a methodology section, where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.
There are various approaches to qualitative data analysis, but they all share five steps in common:
There are five common approaches to qualitative research:
Operationalisation means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
These are four of the most common mixed methods designs:
An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.
The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.
Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.
To design a successful experiment, first identify:
When designing the experiment, first decide:
There are four main types of triangulation:
Triangulation can help:
But triangulation can also pose problems:
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design, it’s important to identify potential confounding variables and plan how you will reduce their impact.
In a between-subjects design, every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design, each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.
The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.
In a factorial design, multiple independent variables are tested.
If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.
Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.
If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity. However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,
If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.
There are three types of cluster sampling: single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling, systematic sampling, or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.
But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples.
You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.
Using stratified sampling will allow you to obtain more precise (with lower variance) statistical estimates of whatever you are trying to measure.
For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.
Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.
For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.
There are three key steps in systematic sampling:
A statistic refers to measures about the sample, while a parameter refers to measures about the population.
A sampling error is the difference between a population parameter and a sample statistic.
There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction, and attrition.
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
This bias can affect the relationship between your independent and dependent variables. It can make variables appear to be correlated when they are not, or vice versa.
The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.
The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).
There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment, and situation effect.
With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity, which includes construct validity, face validity, and criterion validity.
There are two subtypes of construct validity.
When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.
Construct validity is often considered the overarching type of measurement validity, because it covers all of the other types. You need to have face validity, content validity, and criterion validity to achieve construct validity.
Statistical analyses are often applied to test validity with data from your measures. You test convergent and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity.
Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.
Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.
It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.
While experts have a deep understanding of research methods, the people you’re studying can provide you with valuable insights you may have missed otherwise.
There are many different types of inductive reasoning that people use formally or informally.
Here are a few common types:
In inductive research, you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
In research, you might have come across something called the hypothetico-deductive method. It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.
A dependent variable is what changes as a result of the independent variable manipulation in experiments. It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.
In statistics, dependent variables are also called:
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.
Independent variables are also called:
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.
The term ‘explanatory variable‘ is sometimes preferred over ‘independent variable‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.
Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.
The difference between explanatory and response variables is simple:
There are 4 main types of extraneous variables:
‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.
Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. That way, you can isolate the control variable’s effects from the relationship between the variables of interest.
If you don’t control relevant extraneous variables, they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable.
A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.
In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).
The process of turning abstract concepts into measurable variables and indicators is called operationalisation.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.
In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
In matching, you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable.
In statistical control, you include potential confounders as variables in your regression.
In randomisation, you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause, while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables.
Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
Yes, but including more than one of either type requires multiple research questions.
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.
You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment.
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
Discrete and continuous variables are two types of quantitative variables:
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design.
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.
Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.
When conducting research, collecting original data has significant advantages:
However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.
A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.
There is a risk of an interviewer effect in all types of interviews, but it can be mitigated by writing really high-quality interview questions.
An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.
Unstructured interviews are best used when:
The four most common types of interviews are:
As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions, which can bias your responses.
Overall, your focus group questions should be:
The third variable and directionality problems are two main reasons why correlation isn’t causation.
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.
A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.
Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions. The Pearson product-moment correlation coefficient (Pearson’s r) is commonly used to assess a linear relationship between two quantitative variables.
A correlation reflects the strength and/or direction of the association between two or more variables.
Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.
Longitudinal studies and cross-sectional studies are two different types of research design. In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.
|Longitudinal study||Cross-sectional study|
|Repeated observations||Observations at a single point in time|
|Observes the same group multiple times||Observes different groups (a ‘cross-section’) in the population|
|Follows changes in participants over time||Provides snapshot of society at a given point|
Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.
Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘x affects y because …’).
A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study, the statistical hypotheses correspond logically to the research hypothesis.
Individual Likert-type questions are generally considered ordinal data, because the items have clear rank order, but don’t have an even distribution.
Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.
The type of data determines what statistical tests you should use to analyse your data.
A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.
To use a Likert scale in a survey, you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.
A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.
However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).
For strong internal validity, it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
Depending on your study topic, there are various other methods of controlling variables.
Questionnaires can be self-administered or researcher-administered.
Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.
Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.
You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.
Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.
Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.
You can use several tactics to minimise observer bias.
Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. Observer bias is also called detection bias or ascertainment bias.
Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.
Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.
In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.
For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.
After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.
Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.
You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.
Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.
If participants know whether they are in a control or treatment group, they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.
However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.
Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.
Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.
It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.
Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.
In general, the peer review process follows the following steps:
Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.
For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.
Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations.
You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.
You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.
Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.
These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.
Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when collecting data from others.
These considerations protect the rights of research participants, enhance research validity, and maintain scientific integrity.
We are here to help you daily via chat, WhatsApp, email or phone between 9:00 to 23:00 CET.
Our APA experts default to APA 7 for editing and formatting. For the Citation Editing Service you are able to choose between APA 6 and 7.
Yes, if your document is longer than 30,000 words, you will get a sample of approximately 2,000 words. This sample edit gives you a first impression of the editor’s editing style and a chance to ask questions and give feedback.
You will receive the sample edit within 24 hours after placing your order. You then have 24 hours to let us know if you’re happy with the sample or if there’s something you would like the editor to do differently.
Yes, you can upload your document in sections.
We try our best to ensure that the same editor checks all the different sections of your document. When you upload a new file, our system recognizes you as a returning customer, and we immediately contact the editor who helped you before.
However, we cannot guarantee that the same editor will be available. Your chances are higher if
Please note that the shorter your deadline is, the lower the chance that your previous editor is not available.
If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the Scribbr Improvement Model and will deliver high-quality work.
Yes, our editors also work during the weekends and holidays.
Because we have many editors available, we can check your document 24 hours per day and 7 days per week, all year round.
If you choose a 72 hour deadline and upload your document on a Thursday evening, you’ll have your thesis back by Sunday evening!
Yes! Our editors are all native speakers, and they have lots of experience editing texts written by ESL students. They will make sure your grammar is perfect and point out any sentences that are difficult to understand. They’ll also notice your most common mistakes, and give you personal feedback to improve your writing in English.
Every Scribbr order comes with our award-winning Proofreading & Editing service, which combines two important stages of the revision process.
You might be familiar with a different set of editing terms. To help you understand what you can expect at Scribbr, we created this table:
|Types of editing||Available at Scribbr?|
Correction of superficial mistakes, such as typos, misspellings, punctuation errors and consistency errors.
This is the “proofreading” in Scribbr’s standard service. It can only be selected in combination with editing.
Focus on grammar, syntax, style, tone and the conventions of the field. The editor also considers the internal logic of the text and flags any obvious contradictions.
This is the “editing” in Scribbr’s standard service. It can only be selected in combination with proofreading.
Focus on language, style, concision and choices. The editor helps you strengthen your story, polish your sentences and ensure that your use of language drives home your ideas.
Select the Structure Check and Clarity Check to receive a comprehensive edit equivalent to a line edit.
|Developmental editing (i.e. content editing, substantive editing)
This is the first step of the editing process and applies to very early drafts. The editor helps you structure your ideas, decide what story to tell and find direction for your writing.
This kind of editing involves heavy rewriting and restructuring. Our editors cannot help with this.
When you place an order, you can specify your field of study and we’ll match you with an editor who has familiarity with this area.
However, our editors are language specialists, not academic experts in your field. Your editor’s job is not to comment on the content of your dissertation, but to improve your language and help you express your ideas as clearly and fluently as possible.
This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content.
Good academic writing should be understandable to a non-expert reader, and we believe that academic editing is a discipline in itself. The research, ideas and arguments are all yours – we’re here to make sure they shine!
After your document has been edited, you will receive an email with a link to download the document.
The editor has made changes to your document using ‘Track Changes’ in Word. This means that you only have to accept or ignore the changes that are made in the text one by one.
It is also possible to accept all changes at once. However, we strongly advise you not to do so for the following reasons:
You choose the turnaround time when ordering. We can return your dissertation within 24 hours, 3 days or 1 week. These timescales include weekends and holidays. As soon as you’ve paid, the deadline is set, and we guarantee to meet it! We’ll notify you by text and email when your editor has completed the job.
Very large orders might not be possible to complete in 24 hours. On average, our editors can complete around 13,000 words in a day while maintaining our high quality standards. If your order is longer than this and urgent, contact us to discuss possibilities.
Always leave yourself enough time to check through the document and accept the changes before your submission deadline.
Scribbr is specialised in editing study related documents. We check:
The fastest turnaround time is 24 hours.
You can upload your document at any time and choose between three deadlines:
At Scribbr, we promise to make every customer 100% happy with the service we offer. Our philosophy: Your complaint is always justified – no denial, no doubts.
Our customer support team is here to find the solution that helps you the most, whether that’s a free new edit or a refund for the service.
Yes, in the order process you can indicate your preference for American, British, or Australian English.
If you don’t choose one, your editor will follow the style of English you currently use. If your editor has any questions about this, we will contact you.