How much turnitin cost

The plagiarism checker can compare the content against a vast database of online and offline content. This helps in developing original thinking among students. Turnitin provides actionable feedback on assignments. It provides tools to grade the assessments on various parameters like writing skills, paraphrasing tips, etc. Go to Assignment tool and click on the name of the assignment. Click on the Turnitin Menu Icon. Select Originality Report generation and resubmissions. Choose sources for comparison.

Select if students will receive the report. Decide if you want to store the submitted paper. Click on Submit. Different text will be color coded to show plagiarism. Blue means that no match duplicity was found.

Green denotes one match and up to 24 percent similarity index. Yellow means 25 to 49 percent of similarities were present. Turnitin helps institutes in monitoring plagiarized content with its advanced filters, appropriate plagiarism based color-coding.

Turnitin offers data-based insights, helping you to detect contract cheating. It conducts detailed searches and sends instant. With unique assignment grading features and an extensive feedback policy, Turnitin promotes instructional interventions and.

Turnitin enhances academic skills among the students by promoting original work. It also ensures zero plagiarism in coding, text. The plagiarism checking software, with its enhanced duplicate detection tools, points out contract cheating and other piracy. Compare against the industry-leading database of content for comprehensive results. Unlimited Software Choices. It's as simple as that. Compare unlimited software options and make the right choice for your business.

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If you have used the solution already, why not share your thoughts and help others make the right purchase. It compares the text to sources in its database ad performs originality check.

Accurate and Easy to use feature is commendable. Sometimes getting support takes a long time. Pros : The feedback studio and grade mark tools within Turnitin Plagiarism are industry standard and can be used to simplify workflows. They can be easily implemented within the Moodle Learning Management System.

What makes me like Turnitin the most is its reliability. Cons : Customer care is not very consistent with timelines. Pros : Being associated with the research work, it becomes really hectic when you need to make sure that the writings are authentic. Turnitin Plagiarism has been really helpful in that field. The software offers detailed reports for all the submitted papers, including individual links of the sources with matching sentences. The plagiarized words are shown in a highlighted form, making it easy for me to identify the particulars and make necessary changes in real-time.

Cons : Occasionally, it takes a very long time to generate similar reports. Pros : The product efficiency and effectivness, reports accuracy and ease to use factor makes it a complete plagiarism tool. Cons : Sometimes after implementing the suggestion, the meaning of the sentence gets completely changed.

Reason for choosing the software : Turnitin Originality Checker. Reviews 5 reviews. What type of files are supported by Turnitin? What is the maximum and minimum file size supported by Turnitin? What type of databases are covered by Turnitin?

Does Turnitin have any Mobile App? How to check percentage of plagiarism in Turnitin? How to use Turnitin? How to reduce similarity on Turnitin? How does Turnitin algorithm work? How much similarity is allowed in Turnitin? How to cheat Turnitin software? How to upload file in Turnitin? How to use Turnitin in Moodle? What is class id in Turnitin?

Can I buy Turnitin software? Does Essay Typer show up on Turnitin? How to check Turnitin score? How to delete Turnitin submission? How to submit assignment in Turnitin? How to use Turnitin on blackboard? Can Turnitin detect paraphrasing? How to check your plagiarism percentage on Turnitin? How to create a new account on Turnitin? How to create a class in Turnitin? It is adding significant value to the experience of writing to learn. The plagiarism scanner improves the quality of papers students submit and also lowers the levels of cheating in schools, colleges, and universities.

However, Turnitin cannot flag papers written by hired writers who are professional enough to avoid plagiarism. If you used our ghostwriters, or you hired someone to write the paper for you, you are safe. Universities, colleges, and other educational institutions worldwide, including those in the United States, Australia, Canada, and the United Kingdom, recommend and only use Turnitin to check for plagiarism. You have to make your paper submissions to an assignment that is set by your tutor.

If your tutor does not allow paper resubmissions, you cannot check your paper before sending it in. However, you can test your essay, if the tutor allows paper resubmissions through permitting students to overwrite or by revision assignments. Yes, Turnitin never forgets! Once a paper is checked for plagiarism, it is stored in the database and cannot be erased.

Read our comprehensive Turnitin guide on how it works and how it detects plagiarism. Turnitin scans its database and those of its partners, the academic institutions.

If your work or part of it had previously been submitted for an anti-plagiarism scan, Turnitin would remember that. Scribbr has an extensive database with academic articles, websites, and books. It detects plagiarism better than other plagiarism checkers, except Turnitin. However, it cannot detect plagiarism or identify a data source if the same is not publicly accessible. We offer free plagiarism to our customers. All you need to do is to be our customer. Order an essay to be written for you or an assignment done.

As a customer, you will access our checking services. Pass without the hassle! Let someone work for you. Alternatively, send your request to our email [email protected]. If your tutor does not allow paper resubmissions, it is a great idea to double-check your paper before submitting it. There are many free plagiarism detectors available that you can use. However, remember that some of these plagiarism checkers provide limited services.

They may require you to subscribe to access the full services. We Respect your Privacy. Let us Write your Essays! No Plagiarism. Order Now. Order an Essay. Each of these sentences expresses one main idea — by listing them in order, we can see the overall structure of the essay at a glance.

Each paragraph will expand on the topic sentence with relevant detail, evidence, and arguments. The topic sentence usually comes at the very start of the paragraph. However, sometimes you might start with a transition sentence to summarize what was discussed in previous paragraphs, followed by the topic sentence that expresses the focus of the current paragraph.

Topic sentences help keep your writing focused and guide the reader through your argument. In an essay or paper , each paragraph should focus on a single idea. By stating the main idea in the topic sentence, you clarify what the paragraph is about for both yourself and your reader.

A topic sentence is a sentence that expresses the main point of a paragraph. Everything else in the paragraph should relate to the topic sentence.

The interquartile range is the best measure of variability for skewed distributions or data sets with outliers. The two most common methods for calculating interquartile range are the exclusive and inclusive methods. The exclusive method excludes the median when identifying Q1 and Q3, while the inclusive method includes the median as a value in the data set in identifying the quartiles.

The exclusive method works best for even-numbered sample sizes, while the inclusive method is often used with odd-numbered sample sizes. While the range gives you the spread of the whole data set, the interquartile range gives you the spread of the middle half of a data set. The way you present your research problem in your introduction varies depending on the nature of your research paper.

A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement. A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research. The introduction of a research paper includes several key elements:. Homoscedasticity, or homogeneity of variances, is an assumption of equal or similar variances in different groups being compared.

This is an important assumption of parametric statistical tests because they are sensitive to any dissimilarities. Uneven variances in samples result in biased and skewed test results. Statistical tests such as variance tests or the analysis of variance ANOVA use sample variance to assess group differences of populations. They use the variances of the samples to assess whether the populations they come from significantly differ from each other. 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. 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.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share e. Once divided, each subgroup is randomly sampled using another probability sampling method. The thesis statement is essential in any academic essay or research paper for two main reasons:. Without a clear thesis, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

The thesis statement should be placed at the end of your essay introduction. Follow these three steps to come up with a thesis :. A thesis statement is a sentence that sums up the central point of your paper or essay. Everything else you write should relate to this key idea. Instead, it should be centered on an overarching argument summarized in your thesis statement that every part of the essay relates to.

The way you structure your essay is crucial to presenting your argument coherently. A well-structured essay helps your reader follow the logic of your ideas and understand your overall point.

The structure of an essay is divided into an introduction that presents your topic and thesis statement , a body containing your in-depth analysis and arguments, and a conclusion wrapping up your ideas. The structure of the body is flexible, but you should always spend some time thinking about how you can organize your essay to best serve your ideas. Variance is the average squared deviations from the mean, while standard deviation is the square root of this number.

Both measures reflect variability in a distribution, but their units differ:. Although the units of variance are harder to intuitively understand, variance is important in statistical tests. The empirical rule, or the In a normal distribution , data is symmetrically distributed with no skew. Most values cluster around a central region, with values tapering off as they go further away from the center.

The measures of central tendency mean, mode and median are exactly the same in a normal distribution. The standard deviation is the average amount of variability in your data set. It tells you, on average, how far each score lies from the mean. In normal distributions, a high standard deviation means that values are generally far from the mean, while a low standard deviation indicates that values are clustered close to the mean. In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings. In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter of a thesis or dissertation presents your research results concisely and objectively. In quantitative research , for each question or hypothesis, state:. In qualitative research , for each question or theme, describe:. Because the range formula subtracts the lowest number from the highest number, the range is always zero or a positive number. In statistics, the range is the spread of your data from the lowest to the highest value in the distribution.

It is the simplest measure of variability. Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

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. Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. While central tendency tells you where most of your data points lie, variability summarizes how far apart your points from each other.

Data sets can have the same central tendency but different levels of variability or vice versa. Together, they give you a complete picture of your data. Variability is most commonly measured with the following descriptive statistics :. Variability tells you how far apart points lie from each other and from the center of a distribution or a data set. The vast majority of essays written at university are some sort of argumentative essay. Almost all academic writing involves building up an argument, though other types of essay might be assigned in composition classes.

While interval and ratio data can both be categorized, ranked, and have equal spacing between adjacent values, only ratio scales have a true zero. For example, temperature in Celsius or Fahrenheit is at an interval scale because zero is not the lowest possible temperature.

In the Kelvin scale, a ratio scale, zero represents a total lack of thermal energy. In rhetorical analysis , a claim is something the author wants the audience to believe. A support is the evidence or appeal they use to convince the reader to believe the claim. A warrant is the often implicit assumption that links the support with the claim.

Pathos appeals to the emotions, trying to make the audience feel angry or sympathetic, for example. Collectively, these three appeals are sometimes called the rhetorical triangle. They are central to rhetorical analysis , though a piece of rhetoric might not necessarily use all of them. For example, you could also treat an advertisement or political cartoon as a text. The goal of a rhetorical analysis is to explain the effect a piece of writing or oratory has on its audience, how successful it is, and the devices and appeals it uses to achieve its goals.

A critical value is the value of the test statistic which defines the upper and lower bounds of a confidence interval , or which defines the threshold of statistical significance in a statistical test. It describes how far from the mean of the distribution you have to go to cover a certain amount of the total variation in the data i.

The t -distribution gives more probability to observations in the tails of the distribution than the standard normal distribution a. In this way, the t -distribution is more conservative than the standard normal distribution: to reach the same level of confidence or statistical significance , you will need to include a wider range of the data. A t -score a. The t -score is the test statistic used in t -tests and regression tests. It can also be used to describe how far from the mean an observation is when the data follow a t -distribution.

The t -distribution is a way of describing a set of observations where most observations fall close to the mean , and the rest of the observations make up the tails on either side. It is a type of normal distribution used for smaller sample sizes, where the variance in the data is unknown. The t -distribution forms a bell curve when plotted on a graph.

It can be described mathematically using the mean and the standard deviation. 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. The American Community Survey is an example of simple random sampling. In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population.

Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset. You should try to follow your outline as you write your essay.

If you have to hand in your essay outline , you may be given specific guidelines stating whether you have to use full sentences.

When writing an essay outline for yourself, the choice is yours. Some students find it helpful to write out their ideas in full sentences, while others prefer to summarize them in short phrases. You will sometimes be asked to hand in an essay outline before you start writing your essay. Your supervisor wants to see that you have a clear idea of your structure so that writing will go smoothly. Even when you do not have to hand it in, writing an essay outline is an important part of the writing process.

In statistics, ordinal and nominal variables are both considered categorical variables. Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them. Ordinal data has two characteristics:. To automatically insert a table of contents in Microsoft Word, follow these steps:. Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

All level one and two headings should be included in your table of contents. That means the titles of your chapters and the main sections within them. The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list. Do not include the acknowledgements or abstract in the table of contents.

Nominal and ordinal are two of the four levels of measurement. Nominal level data can only be classified, while ordinal level data can be classified and ordered. Nominal data is data that can be labelled or classified into mutually exclusive categories within a variable.

These categories cannot be ordered in a meaningful way. For example, for the nominal variable of preferred mode of transportation, you may have the categories of car, bus, train, tram or bicycle. If your confidence interval for a difference between groups includes zero, that means that if you run your experiment again you have a good chance of finding no difference between groups.

If your confidence interval for a correlation or regression includes zero, that means that if you run your experiment again there is a good chance of finding no correlation in your data. In both of these cases, you will also find a high p -value when you run your statistical test, meaning that your results could have occurred under the null hypothesis of no relationship between variables or no difference between groups.

If you want to calculate a confidence interval around the mean of data that is not normally distributed , you have two choices:. The standard normal distribution , also called the z -distribution, is a special normal distribution where the mean is 0 and the standard deviation is 1. Any normal distribution can be converted into the standard normal distribution by turning the individual values into z -scores. In a z -distribution, z -scores tell you how many standard deviations away from the mean each value lies.

The z -score and t -score aka z -value and t -value show how many standard deviations away from the mean of the distribution you are, assuming your data follow a z -distribution or a t -distribution. These scores are used in statistical tests to show how far from the mean of the predicted distribution your statistical estimate is.

If your test produces a z -score of 2. The predicted mean and distribution of your estimate are generated by the null hypothesis of the statistical test you are using. The more standard deviations away from the predicted mean your estimate is, the less likely it is that the estimate could have occurred under the null hypothesis. To calculate the confidence interval , you need to know:.

Then you can plug these components into the confidence interval formula that corresponds to your data. The formula depends on the type of estimate e. The confidence level is the percentage of times you expect to get close to the same estimate if you run your experiment again or resample the population in the same way.

The confidence interval is the actual upper and lower bounds of the estimate you expect to find at a given level of confidence. These are the upper and lower bounds of the confidence interval. The abstract appears on its own page, after the title page and acknowledgements but before the table of contents. The abstract is the very last thing you write.

You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper. An abstract for a thesis or dissertation is usually around — words. In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length. The acknowledgements are generally included at the very beginning of your thesis, directly after the title page and before the abstract.

In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics. Then you can include personal thanks to friends, family members, or anyone else who supported you during the process. Comparisons in essays are generally structured in one of two ways:.

Comparing and contrasting is also a useful approach in all kinds of academic writing : You might compare different studies in a literature review , weigh up different arguments in an argumentative essay , or consider different theoretical approaches in a theoretical framework. Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment. Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned. The key difference is that a narrative essay is designed to tell a complete story, while a descriptive essay is meant to convey an intense description of a particular place, object, or concept.

Narrative and descriptive essays both allow you to write more personally and creatively than other kinds of essays , and similar writing skills can apply to both. The mean is the most frequently used measure of central tendency because it uses all values in the data set to give you an average. The measures of central tendency you can use depends on the level of measurement of your data.

Measures of central tendency help you find the middle, or the average, of a data set. What kind of story is relevant, interesting, and possible to tell within the word count? The best kind of story for a narrative essay is one you can use to reflect on a particular theme or lesson, or that takes a surprising turn somewhere along the way. The point of a narrative essay is how you tell the story and the point you make with it, not the subject of the story itself.

Narrative essays are usually assigned as writing exercises at high school or in university composition classes. They may also form part of a university application. When you are prompted to tell a story about your own life or experiences, a narrative essay is usually the right response. The majority of the essays written at university are some sort of argumentative essay.

In composition classes you might be given assignments that specifically test your ability to write an argumentative essay. At college level, you must properly cite your sources in all essays , research papers , and other academic texts except exams and in-class exercises.

Add a citation whenever you quote , paraphrase , or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text. The exact format of your citations depends on which citation style you are instructed to use. Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement. For example, income is a variable that can be recorded on an ordinal or a ratio scale:. If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is. The level at which you measure a variable determines how you can analyze your data.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis. Levels of measurement tell you how precisely variables are recorded.

There are 4 levels of measurement, which can be ranked from low to high:. The p -value only tells you how likely the data you have observed is to have occurred under the null hypothesis. The alpha value, or the threshold for statistical significance , is arbitrary — which value you use depends on your field of study. In most cases, researchers use an alpha of 0.

P -values are usually automatically calculated by the program you use to perform your statistical test. They can also be estimated using p -value tables for the relevant test statistic. P -values are calculated from the null distribution of the test statistic. They tell you how often a test statistic is expected to occur under the null hypothesis of the statistical test, based on where it falls in the null distribution. If the test statistic is far from the mean of the null distribution, then the p -value will be small, showing that the test statistic is not likely to have occurred under the null hypothesis.

A p -value , or probability value, is a number describing how likely it is that your data would have occurred under the null hypothesis of your statistical test. You can choose the right statistical test by looking at what type of data you have collected and what type of relationship you want to test.

The test statistic will change based on the number of observations in your data, how variable your observations are, and how strong the underlying patterns in the data are. For example, if one data set has higher variability while another has lower variability, the first data set will produce a test statistic closer to the null hypothesis, even if the true correlation between two variables is the same in either data set.

The formula for the test statistic depends on the statistical test being used. Generally, the test statistic is calculated as the pattern in your data i. An argumentative essay tends to be a longer essay involving independent research, and aims to make an original argument about a topic.

Its thesis statement makes a contentious claim that must be supported in an objective, evidence-based way. Rather, it aims to explain something e.

Expository essays are often shorter assignments and rely less on research. An expository essay is a common assignment in high-school and university composition classes. It might be assigned as coursework, in class, or as part of an exam. Sometimes you might not be told explicitly to write an expository essay.

An expository essay is a broad form that varies in length according to the scope of the assignment. Expository essays are often assigned as a writing exercise or as part of an exam, in which case a five-paragraph essay of around words may be appropriate. If participants know whether they are in a control or treatment group , they may adjust their behavior 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. Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset. Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population. A true experiment a. However, some experiments use a within-subjects design to test treatments without a control group.

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. 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 analyze your data. A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors.

It is made up of 4 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 5 or 7 possible responses, to capture their degree of agreement. In a Chicago style footnote , list up to three authors. In the bibliography , list up to 10 authors.

The same rules apply in Chicago author-date style. In scientific research, concepts are the abstract ideas or phenomena that are being studied e. Variables are properties or characteristics of the concept e. The process of turning abstract concepts into measurable variables and indicators is called operationalization. There are various approaches to qualitative data analysis , but they all share five steps in common:.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis.

There are five common approaches to qualitative research :. Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

When an online source does not list a publication date, replace it with an access date in your in footnote citations and your bibliography :.

In a Chicago footnote citation , when the author of a source is unknown as is often the case with websites , start the citation with the title in a full note. In short notes and bibliography entries, list the organization that published it as the author. In Chicago author-date style , treat the organization as author in your in-text citations and reference list.

Operationalization means turning abstract conceptual ideas into measurable observations. However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive.

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 organizations. There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

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 randomization , 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. To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

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. To ensure the internal validity of an experiment , you should only change one independent variable at a time. 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 soda and regular soda, so you conduct an experiment. Determining cause and effect is one of the most important parts of scientific research. 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. Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling. Using careful research design and sampling procedures can help you avoid sampling bias. Oversampling can be used to correct undercoverage bias. Some common types of sampling bias include self-selection, non-response, undercoverage, survivorship, pre-screening or advertising, and healthy user bias. Sampling bias is a threat to external validity — it limits the generalizability of your findings to a broader group of people.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. A sampling error is the difference between a population parameter and a sample statistic. A statistic refers to measures about the sample , while a parameter refers to measures about the population. Populations are used when a research question requires data from every member of the population.

This is usually only feasible when the population is small and easily accessible. Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient and manageable. Use a shortened version of the title in your in-text citation. If a source has no page numbers, you can use an alternative locator e. If there are three or more authors, name only the first author, followed by et al. You must include an in-text citation every time you quote or paraphrase from a source e.

There are seven threats to external validity : selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment and situation effect. The two types of external validity are population validity whether you can generalize to other groups of people and ecological validity whether you can generalize to other situations and settings.

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures. Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study.

Cross-sectional studies are less expensive and time-consuming than many other types of study. Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long. The British Cohort Study , which has collected data on the lives of 17, Brits since their births in , is one well-known example of a longitudinal study. 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.

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. If you use a lot of long quotes , consider shortening them to just the essentials.

If you need to remove a lot of words, you may have to cut certain passages. Revising, proofreading, and editing are different stages of the writing process. In statistics, model selection is a process researchers use to compare the relative value of different statistical models and determine which one is the best fit for the observed data.