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Frequently, students are assigned papers that require using Oxford style of writing.For this reason, one has to be aware of all details and pitfalls, which can be hidden within the frames of this peculiar style.
Forewarned is forearmed! With this in mind, it will be quite useful for you to find out more about Oxford style in the sphere of academic writing As with everything I waited until the last minute to write my essay. Writing my essay was easy for them. They sent my essay to me in 8 hours!” -5 page essay 9/25/08 “Having kids and then writing a 15 page term paper just doesn't go together. Especially doing all that research would be impossible. Essaywritingcompany.com .Forewarned is forearmed! With this in mind, it will be quite useful for you to find out more about Oxford style in the sphere of academic writing.
Where to Start? The very first thing you have to do before composing an academic paper is to consider the topic you are going to work on.After you have understood the main arguments on the topic, gather those facts that will back up your reasoning Need to buy a ethnicity term paper British 24 pages / 6600 words US Letter Size Business.After you have understood the main arguments on the topic, gather those facts that will back up your reasoning.Your essay topic should include terms, which will define the way of arguing Need to buy a ethnicity term paper British 24 pages / 6600 words US Letter Size Business.Your essay topic should include terms, which will define the way of arguing.For instance, the topic, “Compare and Contrast Chevrolet and Nissan” suggests offering some observations targeted at showing differences and similarities between the given automobile brands.
The above-mentioned example shows the presence of keywords you need to pay attention to when processing topics for your Oxford student paper.Consequently, first, it is worthy to learn how you have to define keywords, which will then produce the arguments' general line.The topic can evaluate, argue, analyze, compare, and apply key terms in order to set up arguments required by the paper.In case the topic comprises keywords to compare, you will be required to present arguments, which distinctly demonstrate differences and similarities between the items required.
Therefore, when you are engaged in writing paper Oxford style, it is important to refer to facts concerning the items at issue.
At the same time, the topic may suggest evaluating data, results, and a set of arguments.When this occurs, you should clearly comprehend the procedures, which build up the good reason behind the observation.In such a way, the paper needs to cover your viewpoints of whether the current results demonstrate a particular trend.After you have clarified all details on the topic, it is high time to decide upon a type of writing.The different effects Oxford essay writers would like to have on their future readers (to entertain, to persuade, to inform, etc.
) lead to creating different types of papers.In general, the most widespread ones are known as the narration, description, or exposition.The sense of narration lies in the depiction of a story – a series of related events.Its task is to reveal the significance of events arranged in a sequence of time.Description has to deal with visual perceptions.
Here, the task is to organize everything we perceive visually in a clear and understandable way so that it can be put on the paper and make sense.When working on your Oxford research proposal, keep in mind that exposition tends to explain everything: History – why William the Conqueror conquered England; everyday life facts – how many people get married; ideas – a theory of politics; how things work – GPS in a car.However, despite its subject, the phenomenon of exposition discovers what a particular person believes in, knows, or thinks is true.Therefore, exposition is organized in a logical way.It is centered on the denial/assertion, particular/general, negative/positive, more/less, false/true, and effect/cause.
At that, the way one explanation flows into another one is marked by such words and phrases as “for example”, “more importantly”, “in fact”, “not only”, “but”, “besides”, “and so”, “however”, and “therefore”.Seeking for a good Oxford University essay prompt, remember that persuasion is aimed at altering how readers think and what they believe in.Hence, it is crucial to back up the claims and statements with solid evidence taken from reliable sources of information.Satire is one more form of persuasion, which laughs at evil or folly, sometimes coarsely and crudely, and sometimes subtly.Lastly, persuasion may be depicted by means of eloquence, turning to noble sentiments and ideas.
Essay Writing Tips: Oxford Style Find several topics you will be able to turn into a brief essay.Consider those topics that deal with your beliefs and opinions, but not much with how-to-do projects, places, or things.Try to choose themes within your experience and interest, and keep in mind that they should be challenging to a certain extent.You need to be specific even when being engaged in Oxford University creative writing: write “what I do not like most about my position” instead of “I do not like my position.” Pick one of the topics and then write a few sentences about your potential readers.
Consider their biases, attitudes, values, general knowledge, and whether they come from a different or similar background.Also, determine whether they are younger or older than you are, and how you want them to look upon you.Oxford Writing Standards Writing, apart from putting words and sentences on a piece of paper, includes the process of thinking.According to Oxford study guide, the first "thinking" step supposes picking a topic, examining possibilities of elaborating it and coming up with the strategy of presenting the information on it.The second step is mainly known as "drafting," and the third "one should always be "revising.
" Nevertheless, despite the information mentioned above, it is important to understand that these are not the steps in the usual sense.Nobody writes Oxford research paper by means of thinking, completing a rough copy, and making a revision.Basically, you accomplish these things simultaneously.In case it seems strange to you, think about the fact that writing is not an easy work.When you simply consider a topic you already start choosing words and building up sentences, making a draft either in your head or in the notes.
During the processes of drafting and revising, the thinking process takes place – you develop new ideas, realize you have come to the deadlock with some of them, and detect implications you have not noticed before.More often than not, it is quite useful to perceive writing as the combination of these three steps.Despite this fact, try to comprehend that the whole process never moves from one step to another in the steady and smooth way.It is always about going back and forth.During the working process, one way or another, you will focus on one writing phase.
Oxford Paper Format Guidelines An academic paper has to demonstrate arguments in a well-defined structure.The given structure will make it easy for your readers and yourself to know the place where to look for separate parts of your arguments.Pay a close heed to the formatting of your paper not to seek for Oxford paper for sale later.As a rule, a well-formatted academic paper consists of introductory part, which contains a clear thesis statement, the body that introduces your arguments in accordance with the thesis statement, and a conclusion part that summarizes your viewpoint and contemplates arguments regarding the main topic.If you are not completely sure how to format your paper properly, you can always look for Oxford style sample paper on the Internet.
Formatting an academic paper requires the observations of pre-defined requirements concerning margins, font style, and indentations.You can find the necessary formatting info in the Oxford University essay writing guide, and seek the professor’s assistance in case you have hard times understanding certain rules.By meeting the set requirements, you can gain credibility in the eyes of the readers.
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Assure yourself that the assignment criteria are clear and easy to understand.
Quite often, apart from the fact that you have a pre-established format to consider (for instance, it may be Oxford University dissertation format) your scientific supervisor can also have his/her own criteria for the paper, which are crucial to consider while working on the paper.
One of the secrets of efficient academic paper writing is meeting all the assignment requirements, as well as the demands of the supervisor, and answering all possible questions readers may want to ask concerning the chosen topic IEEE Citation Guidelines pdf IEEE DataPort.One of the secrets of efficient academic paper writing is meeting all the assignment requirements, as well as the demands of the supervisor, and answering all possible questions readers may want to ask concerning the chosen topic.
You need to keep in mind that academic paper is a kind of work that gives you a possibility to introduce your arguments in a comprehensible manner.If you polish the formatting skill, you can provide readers with a chance to establish your line of reasoning without obstacles caused by a chaotic analysis or unclear format.In addition, you can even save your money because there will be no need to address such organizations as dissertation writing services Oxford.When working on your paper, an important condition is to cite all sources you have used.
It is important that you make sure to do it according to the Oxford University essay format for citing.To perform this task correctly, pay attention to the following information and facts.Oxford Style Referencing Guide Oxford referencing style is applied mainly in research papers in certain philosophy and history departments.In addition, the style can be used when dealing with law courses.Papers written in Oxford style require indicating page numbers, but they can be put according to the writer’s judgment.
Usually, all margins are one inch on every side, except the top where margin should be two inches.The paper itself has to be double-spaced along with the reference page.A title page needs to be formatted very specifically according to the Oxford student guide.The paper’s title is placed at the top of the page; the elements that follow are the type of paper (coursework, essay, dissertation, etc.), the date, word count, author’s name, and the name of the institution.
For example: World War I History Johns Hopkins University Oxford referencing format can be characterized as a documentary-note style, which includes two parts: footnote citation and reference list.Footnote Citation Guidelines As a rule, a superscript number is included in the text where the source is cited.After you have done it, put the superscript number at the page’s bottom where the footnote details are recorded.State initials or the given name of the author before the family name (for instance, Peter Oldridge).Do not forget to cite a separate page (e.
Short title/family name: In case you cite the same source more than once in footnotes of your Oxford economic paper, apply only the page number and the family name of the author for all subsequent references.
Otherwise, when your references are not successive, mention the page number, the short title of the work, and the author’s family name for subsequent references.Avoid putting the publication date, publisher, and place of publication.Along the same line, when you cite several works by one author, you may apply the short title and family name in all subsequent references to differentiate these works.According to Oxford style citation guide, indirect and direct paraphrasing should be acknowledged.At the same time, footnotes are used to find informational sources, interpretations, or ideas even when they are less paraphrased than described.
Failure to format sources properly may result in the high level of plagiarism in your paper.Direct quotations have to be put in single quotation marks.In case the quotation is too long and contains more than 40 words, single out the whole quote from the body of text indenting it.Remember that the given indentation has to be single-spaced, whether or not the rest of the text has the same spacing.Check any sample Oxford paper to see how cited and quoted info looks in the text.
Example of Footnote: In 1950, Adorno, Frenkel-Brunswick, Levinson and Sanford proposed the concept of the authoritarian personality – a type of person who is prejudiced by virtue of specific personality traits which predispose him or her to be hostile towards ethnic, racial, and other minority groups.1 Subsequent Footnotes According to Oxford University paper reference form, all subsequent references do not have to be too detailed in comparison with the very first footnote.Such references require a minimum information to point out which source is being cited.With One Author Introduce all important details in the first footnote.In case you would like to cite the same source several times, a simple way out is to provide a page number, year of publication, and the name of the author.
For instance: 1 A Bryman, 2 … 3 Bryman, p.As follows from Oxford style guide online, if you refer to more than two works by the same author in the text, simply add the title of work: 1 J M Gibson and R L Green, The Unknown Conan Doyle: Essays on Photography, Secker & Warburg, London, 1982, p.2 J M Gibson and R L Green, Letters to Press, Secker & Warburg, London, 1986, p.
3 Gibson and Green, The Unknown Conan Doyle: Essays on Photography, p.One more way to reduce subsequent references is to use Latin abbreviations, such as ibid (same as last entry) and .
You can apply ibid when two references at a stretch originate from the same source., it may be applied in case you have already provided all details regarding the particular source in an earlier footnote., introduce such details as the name of the author in order to make the source more recognizable.
Oxford citation format requires the given abbreviations to be put in the lowercase.Oxford Style Paper Format: Reference List Guidelines Your list of references has to be titled as "Reference List" and put on a separate page at the end of your work.Such list includes all details regarding footnotes, which are arranged in alphabetical order by author's family name.
Two notions "Reference List" and "Bibliography" are used interchangeably in the majority of cases, however, a Bibliography, in accordance with Oxford University bibliography format, contains all sources applied to fulfill your assignment whereas a Reference List comprises only those sources you have referred to in your paper.Taking this into account, do not forget to check with your instructor on the format required.Other points to consider: The given name of the author always precedes the family name (A.Parker) in your footnotes, while in the list of references the family name goes first (Parker, A.Apply the first substantive title’s word (except articles a and the, according to Oxford grammar guide) if the work has no author(s) to add to the list of references in alphabetical order.In case you have referred to several works by the same author, arrange them by date.When dates are identical, insert a lower case letter after the date in order to be able to differentiate between the given works, e.Bibliography format Oxford style may demand your list of references be divided into primary and secondary informational sources.Now, it is high time to learn how to indicate references for different kinds of sources.Below, you will find the examples of the most widespread documents used when dealing with paper writing.Books with a single author: Add (if any): last and first names of an author; the title of work; edition; publisher and place of publication, publication year.
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, Books (two or more authors): FitzSimons, T., Australian Documentary: History, Practices and Genres, 2nd edn.
, Port Melbourne, VIC, Cambridge University Press, 2011 Best website to write a custom ethnicity term paper Academic double spaced A4 (British/European) Bluebook., Port Melbourne, VIC, Cambridge University Press, 2011.
Edited Books: In accordance with Oxford bibliography format, put editor (s) in brackets right after editor’s name (s).), Our Great Game: The Photographic History of Australian Football, Docklands, VIC, Slattery Media Group, 2010.
When the book has more than one editor, you need to observe multiple authors format, putting eds About. HelpingPaper is an economical Essay Writing Service that provide quality content and help the students who're stuck with their research work and looking for guidance. Helping Paper..When the book has more than one editor, you need to observe multiple authors format, putting eds.E-books: References for e-books will be the same as for printed publications.For those books that have been downloaded or read on a bookshop or library websites, it is necessary to include details about the e-book., Gated Communities?: Regulating Migration in Early Modern Cities, Farnham, UK, Ashgate Publishing, 2012.Sometimes, some books with expired copyright can be available online.When this occurs, you have to include the full URL together with the access date.
In case URL is too long, it is possible to apply website URL e.
Journal Articles: To make a reference when dealing with Oxford writing articles example, you need to add: author’s first and last names; article’s title; issue and volume; publication year; numbers of pages., ‘Food Enigmas, Colonial and Postcolonial,’ Gastronomica, vol.
, ‘When Gifts Become Commodities: Pawnshops, Valuables, and Shame in Tonga and the Tongan Diaspora,’ Journal of the Royal Anthropological Institute, vol.Articles from Electronic Journals: Often, in order to define an electronic article, DOI is used.
Such DOIs are permanent, which is why it is quite easy to find the article when the article's URL has been changed.As usual, major academic publishers assign DOI-numbers to articles.As stated in Oxford college guide for citing works, in case there is no DOI, you have to provide the article’s URL and access date., ‘Was Pythagoras Ever Really in Sparta?’, Rosetta, no., ‘The City of Sordid Splendour,’ Australian, 26 August 1964.
Internet Sources (Web Pages): Author, organization, company or authority; (year); title of page or document; website’s name; webpage’s last update; date of access and full URL.Encyclopedias: For online encyclopedias’ entries, when being engaged in Oxford creative writing online, add: article’s author, article’s title, encyclopedia name, publication year; full URL together with the access date./EBchecked/topic/142824/Creutzfeldt-Jakob-disease (Accessed 2010-10-30).When you have received your Master’s creative writing Oxford Style assignment to complete, but cannot find a particular example of reference in our short list, it is better to address the official Oxford style guide.Oxford University CV Writing Guidelines Imagine that you have already graduated from university and are now looking for a decent job.
The first thing you know you need to do is to write your CV.For instance, the most commonly used type of CV is a reverse chronological one.Often, it covers information about your work experience, additional activities, and education.Traditional CV sections are as follows: work experience; references (‘Available on Request’).The given Oxford CV format makes it possible for employers to notice important details quickly and provides clear info about the candidate for a job position.
Another type of CV that can be used while applying is the skills-based one.In such document, all details are organized to demonstrate relevant skills.In order to provide context, you have to put a concise summary of your work history before or after the section with relevant skills.Usually, the given CV type is applied to show the adaptability of your skills in case you are going to apply to a position without relevant experience.Taking it into account, this CV is used by people who are targeted at changing their career direction or transitioning to other sectors.
When you have chosen a correct CV format, be sure to organize all information in an appropriate order.In any way, it will be useful to look for Oxford University CV template on the Internet.At the same time, remember to attach a well thought-out cover letter, which will back up your application.For your convenience, Pro-Papers has prepared some main guidelines for business letter writing Oxford style: A couple of pages is the maximum – demonstrate your ability to prioritize.
You should sound professional and confident because your letter is a piece of formal writing.
Be sure to indicate the purpose clearly when writing a cover letter Oxford style.Show your insight into those matters, which are important for the potential employer.Did the guide leave you with more questions than answers? Have you lost all the patience trying to get right into the nuts and bolts of the given formatting style? Do not be upset because you still have a chance to receive a professional support regarding your academic writing assignment at Pro-Papers.We are the company that can provide every client with a wide spectrum of Oxford essay writing service, starting from free consultations and finishing with original papers composed by our writers.By the way, if you are not satisfied with the final version of the paper, you may send it for revision, which is also free of charge.
Therefore, if you go through tough times when composing papers, leave this writing nightmare to Pro-Papers specialists who will cope with the task perfectly.Best Practices for Measuring Students’ Attitudes toward Learning Science *Department of Educational Psychology, University of Georgia, Athens, GA 30602 †Department of Plant Biology, University of Georgia, Athens, GA 30602 Address correspondence to: Peggy Brickman ( @namkcirB).CBE—Life Sciences Education © 2013 The American Society for Cell Biology.This article is distributed by The American Society for Cell Biology under license from the author(s).It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License ( /licenses/by-nc-sa/3.“ASCB®” and “The American Society for Cell Biology®” are registered trademarks of The American Society of Cell Biology.This article has been cited by other articles in PMC.Abstract Science educators often characterize the degree to which tests measure different facets of college students’ learning, such as knowing, applying, and problem solving.A casual survey of scholarship of teaching and learning research studies reveals that many educators also measure how students’ attitudes influence their learning.Students’ science attitudes refer to their positive or negative feelings and predispositions to learn science.
Science educators use attitude measures, in conjunction with learning measures, to inform the conclusions they draw about the efficacy of their instructional interventions.The measurement of students’ attitudes poses similar but distinct challenges as compared with measurement of learning, such as determining validity and reliability of instruments and selecting appropriate methods for conducting statistical analyses.In this review, we will describe techniques commonly used to quantify students’ attitudes toward science.
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We will also discuss best practices for the analysis and interpretation of attitude data.Science, technology, engineering, and math (STEM) education has received renewed interest, investment, and scrutiny over the past few years (American Association for the Advancement of Science AAAS , 2010 ).
government funded 209 STEM education programs costing more than $3 Best Practices for Measuring Students Attitudes toward Learning nbsp.government funded 209 STEM education programs costing more than $3.
4 billion (National Science and Technology Council, 2011 ).At the college level, education researchers have predominantly focused greater effort on demonstrating the results of classroom interventions on students’ intellectual development rather than on their development of “habits of mind, values and attitudes” toward learning science (National Research Council, 2012 ).
However, students’ perceptions of courses and attitudes toward learning play a significant role in retention and enrollment (Seymour and Hewitt, 1997 ).) and discomfort with the ambiguity, lack of a “right” response, and multiplicity of views found in these methods (Cossom, 1991 ).For all these reasons, many researchers have increased their focus on measuring students’ engagement, perceived learning gains, motivation, attitudes, or self-efficacy toward learning science.There are a wide variety of excellent tools available to gather data on student perceptions.Qualitative analysis tools, such as student interviews, provide rich data that can reveal new insights and allow for flexibility and clarification of students’ ideas (Slater ).
However, analyzing written comments or transcripts can be very labor intensive.Quantitative analysis tools, such as survey instruments, can allow for easier compilation of student responses that attach numerical scores to students’ opinions about different aspects of a curriculum along a continuum, say, 1–5, with 1 being “not useful” to 5 being “very useful” for each aspect.The familiar end-of-semester student evaluations of courses and teachers use a combination of quantitative survey items and qualitative open-ended comments.In addition, the Student Assessment of Their Learning Gains Internet site alone has almost 7800 instructors creating surveys that query students’ perceptions of gains in learning ( ).To draw the most valid conclusions possible from data collected through such tools, it is important for faculty to choose analyses most appropriate for the task.
This review is designed to present an overview of some of the common assessment tools available to measure students’ attitudes toward learning science.The review will also provide widely endorsed, straightforward recommendations for analysis methods with theory and empirical evidence to support analysis plans.Our goal is to help education researchers plan attitudinal studies such that they avoid common pitfalls.We would also like to provide advice and references for supporting your approaches to analyzing and displaying attitudinal data.INVENTORIES (SCALES) FOR ASSESSING STUDENTS’ ATTITUDES Pen-and-paper assessments used to gauge psychological characteristics such as attitude are commonly referred to interchangeably as inventories, surveys, instruments, or measurement scales.
Psychologists use such tools to assess phenomena of interest, such as beliefs, motivation, emotions, and perceptions that are theoretical constructs not directly observable and often composed of multiple facets.The more psychologists know about the theoretical underpinnings of a construct, the more likely they are to develop reliable, valid, and useful scales (DeVellis, 2003 ).Psychological constructs are often described as latent, meaning they are not directly observed but are instead inferred from direct measurements of theoretically related variables (Lord and Novick, 1968 ).The most important methodological concern to stress about scales designed to measure a latent construct is that they are not solely a collection of questions of interest to the researcher.
Instead, scales are composed of items that have been subjected to tests of validity to show that they can serve as reasonable proxies for the underlying construct they represent (DeVellis, 2003 use the history of the development of temperature measures as an analogy for better understanding measurement theory in the social sciences.
Although people customarily refer to the reading on a thermometer as “the temperature,” Bond and Fox explain that a thermometer reading is at best an indirect measure.The estimate of temperature is indirect, because it is determined from the known effects of thermal energy on another variable, such as the expansion of mercury or the change in conductivity of an electrical resistor.Similarly, in the social sciences, numerical representations of psychological attributes (e., attitude toward science) are derived from theoretical explanations of their effect on a more readily observable behavior (e.
In this way, an attitudinal scale score serves as a proxy for the latent construct it is purported to measure, and researchers need to be prepared to defend the validity and limitations of their scale in representing it (Clark and Watson, 1995 ).Just as one would not consider a single question adequate to evaluate a student's knowledge about a biology topic, one would not evaluate a complex construct, for example, engagement, with a single item.A scale developed to evaluate engagement would undergo a rigorous, iterative validation process meant to determine the aspects of the underlying construct the scale represents and empirically test the hypothesized relationships between the construct and its observable proxy (Clark and Watson, 1995 ).
A measurement scale is composed of a collection of purposely constructed items backed up by empirical evidence of interrelationship and evidence that they represent the underlying construct (Carifio and Perla, 2007 ).A minimum of six to eight items is recommended to provide for adequate considerations of generalizability, reliability, and validity (Cronbach Inventories for assessing students’ perceptions about biology (college-level) The basic assumption behind attitude scales is that it is possible to uncover a person's internal state of beliefs, motivation, or perceptions by asking them to respond to a series of statements (Fraenkel and Wallen, 1996 ).Individuals indicate their preference through their degree of agreement with statements on the scale.Items containing these statements are constructed with three common response formats: dichotomous agree/disagree, semantic-differential, and Likert formats (Crocker and Algina, 2008 ).In all cases, the items consist of two parts: a question stem and a response option (Figure 1).
Dichotomous items contain just two response options (1 = yes, 2 = no; or 0 = disagree, 1 = agree) following a simple declarative statement.Semantic-differential items use a bipolar adjective (opposite-meaning) list or pair of descriptive statements that examinees use to select the response option out of a range of values that best matches their agreement.These semantic-differential items measure connotations.(Figure 1 contains semantic-differential items from Lopatto 2004 .) As demonstrated in Table 1, Likert items are the most common response formats used in attitude scales.
They offer multiple response categories that usually span a 5-point range of responses, for example, A = “strongly agree” to E = “strongly disagree,” but may span any range.(Figure 1 contains Likert response–format items from Russell and Hollander 1975 ).In addition to the increase in reliability when moving from the dichotomous 2-point range to a 4- or 5-point range, statisticians have demonstrated an increase in type II error rates in 2-point response formats (Cohen, 1983 ).Response options may be delineated by numbers, percentages, or degrees of agreement and disagreement.Response options may also be structured in several equivalent ways: a numbering system, letters to indicate the responses, or just end points indicated (Frisbie and Brandenburg, 1979 .
TYPES OF DATA COLLECTED IN ATTITUDINAL SURVEYS Psychologist Stanley Smith Stevens is credited with developing the theory of data types that are pertinent for pen-and-paper tests used to measure psychological constructs (Stevens, 1946 ).He set forward “basic empirical operations” and “permissible statistics” for the four levels of measurement scales, terms, and rules he developed to describe the properties of different kinds of data: nominal, ordinal, interval, or ratio (Table 2).Data collected in a nominal format describe qualitative traits, categories with no inherent order, such as demographic information like nationality or college major.Responses to dichotomous items are considered nominal when 0 and 1 merely serve as descriptive tags, for example, to indicate whether someone is male or female (Bond and Fox, 2007 ).However, dichotomous items may be used to generate ordinal rather than nominal data.
For example, the disagree/agree or unsatisfied/satisfied responses to dichotomous items generate data for which a value of 1 represents a meaningfully greater value than that represented by 0.Ordinal data are nominal data with an added piece of quantitative information, a meaningful order of the qualities being measured.This means these data can be rank-ordered (first, second, third, …).In addition to these agree/disagree dichotomous items, responses to semantic-differential items ask participants to place themselves in order along a continuum between two adjectives.Likert items ask participants to rank a set of objects or statements with response options over a range of values: “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree.
” These would also commonly be described as ordinal, because the response choices on a particular item are arranged in rank order of, in this case, least amount of agreement to most (Jamieson, 2004 ).Both nominal and ordinal data are described as categorical, whereas the two other levels of measurement—interval and ratio—are quantitative (Agresti, 2007 ).Quantitative data can be further classified as discrete quantitative, only being able to take on certain values, or as continuous quantitative, theoretically able to take on any value within a range (Steinberg, 2011 ).
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CATEGORICAL (NONPARAMETRIC) VERSUS QUANTITATIVE (PARAMETRIC) DATA ANALYSIS PROCEDURES In inferential statistics, tests are conducted to determine the plausibility that data taken from a smaller random sample are representative of the parameters measured were the data to be observed for the entire population (Moore, 2010 ).(See Table 3 for a glossary of statistical terms.
) Researchers commonly refer to the statistical tools developed for analyzing categorical data with a nominal or ordinal dependent variable as nonparametric and include the median test; Mann-Whitney U-test; Kruskal-Wallis one-way analysis of variance (ANOVA) of ranks; and Wilcoxon matched-pairs, signed-ranks test (Huck, 2012 ) 23 Aug 2014 - the study of cultures, gender, sexuality, race, religion, class, ethnicities and languages with special Analyze and write about how meaning is created through both form and content in a work of art. Each member of the group must turn in a one page, 400 words; single-space paper that sums up what..) Researchers commonly refer to the statistical tools developed for analyzing categorical data with a nominal or ordinal dependent variable as nonparametric and include the median test; Mann-Whitney U-test; Kruskal-Wallis one-way analysis of variance (ANOVA) of ranks; and Wilcoxon matched-pairs, signed-ranks test (Huck, 2012 ).
These tests involve fewer assumptions than do the parametric test procedures developed for use with quantitative interval- or ratio-level data (such as the assumptions of normality of the distributions of the means and homogeneity of variance that underlie the t and F tests).Parametric statistics are so named because they require an estimation of at least one parameter, assume that the samples being compared are drawn from a population that is normally distributed, and are designed for situations in which the dependent variable is at least interval (Stevens, 1946 ).Researchers often have a strong incentive to choose parametric over nonparametric tests, because parametric approaches can provide additional power to detect statistical relationships that genuinely exist (Field, 2009 ).In other words, for data that do meet parametric assumptions, a nonparametric approach would likely require a larger sample to arrive at the same statistical conclusions.
Although some parametric techniques have been shown to be quite robust to violations of distribution assumptions and inequality of variance (Glass ).Glossary The assumption that parametric tests should only be used with interval-level dependent variables is central to the ongoing debate about appropriate analyses for attitudinal data.Statistics such as mean and variance—what are commonly the parameters of interest—are only truly valid when data have meaningfully equidistant basic units; otherwise, using these statistics is “in error to the extent that the successive intervals on the scale are unequal in size” (Stevens, 1946, p.Data from well-designed psychological measurement scales, however, can have properties that appear more interval than ordinal in quality, making classification based on Stevens’ guidelines more ambiguous (Steinberg, 2011 ).
This has led to a great deal of conflicting recommendations over whether to use parametric or nonparametric data analysis procedures for scales based on ordinal data from dichotomous and semantic-differential items, but particularly for Likert-type items (Knapp, 1990 ).For example, some sources argue that assigning evenly spaced numbers to ordinal Likert-response categories creates a quantitative representation of the response options that is more interval than ordinal and, therefore, practically speaking, could be analyzed as interval quantitative data.This argument supports computing means and SDs for Likert-response items (Fraenkel and Wallen, 1996 ) and utilizing parametric statistical analysis techniques (e., ANOVA, regression) designed for interval data (Norman, 2010 ).
Others argue that equivalency of distances between ranked responses in a Likert-response format should not be assumed and, thus, treating responses to a Likert item as ordinal would lead to a more meaningful interpretation of results (Kuzon even offered this pragmatic suggestion: “In the strictest propriety, the ordinary statistics involving means and SDs ought not to be used with these ordinal scales, for these statistics imply a knowledge of something more than the relative rank-order of data.On the other hand, for this ‘illegal’ statisticizing there can be invoked a kind of pragmatic sanction: In numerous instances it leads to fruitful results” (p.The reasoning behind varying perspectives on appropriate procedures for analysis of data involving ordinal items has been addressed in further detail elsewhere (Harwell and Gatti, 2001 ).Marcus-Roberts and Roberts (1987) sum it up best by saying that although it may be “appropriate” to calculate means, medians, or other descriptive statistics to analyze ordinal or ranked data, the key point is “whether or not it is appropriate to make certain statements using these statistics” (p.
The decision to analyze ordinal responses as interval quantitative data depends heavily on the purpose of the analysis.In most cases, ordinal-response measurement scales are used to gather data that will allow inferences to be made about unobservable constructs.To simply accept the data as interval would be to ignore both the subjectivity of these opinion-type questions and the response format the numbers represent.The decision clearly needs to first take into account how the sample investigated can be analyzed to infer characteristics about the population as a whole.
The sample in this case includes: 1) the individuals surveyed and 2) the number and nature of the questions asked and how they represent the underlying construct.In the following section, we will provide recommendations for analyzing ordinal data for the three most common response formats used in attitudinal surveys.We will argue that, for semantic-differential and Likert-type items, the question of which analysis to perform hinges on the validity of making conclusions from a single item versus a scale (instrument subjected to tests of validity to support representation of an underlying construct).RECOMMENDED STATISTICAL ANALYSES FOR ATTITUDINAL DATA Dichotomous Items There are a variety of statistical test procedures designed for nonparametric data that are strictly nominal in nature.We will focus on providing recommendations for analysis of dichotomous items producing ordinal data because these are most common in attitudinal surveys.
For an excellent overview and treatment of comparisons of many different types of categorical data, we recommend reading the chapter “Inferences on Percentages and Frequencies” in Huck (2012, Chapter 17, pp.404–433) and in Agresi (2007, Chapters 1–4, pp.Let us consider a hypothetical research question: Imagine that a researcher wishes to compare two independent samples of students who have been surveyed with respect to dichotomous items (e., items that ask students to indicate whether they were satisfied or unsatisfied with different aspects of a curriculum).If the researcher wishes to compare the percentage of the students in one group, who found the curriculum satisfying, with the percentage of students in the second group, who did not, he or she could use Fisher's exact test, which is used for nonparametric data, often with small sample sizes, or an independent-samples chi-square test, which is used for parametric data from a larger sample size (Huck, 2012 ).The independent-samples chi-square test has the added benefit of being useful for more than two samples and for multiple categories of responses (Huck, 2012 ).This would be useful in the scenario in which a researcher wished to know whether the frequency of satisfaction differed between students with different demographic characteristics, such as gender or ethnicity.If the researcher wished to further examine the relationship between two or more categorical variables, a chi-square test of independence could be used (Huck, 2012 ).
Semantic-Differential and Likert Items As described in Table 2, a semantic-differential or Likert item on its own is most likely ordinal, but a composite score from a measurement scale made up of the sum of a set of a set of interrelated items can take on properties that appear much more continuous than categorical, especially as response options, items, and sample size increase (Carifio and Perla, 2007 ).For these reasons, many researchers use parametric statistical analysis techniques for summed survey responses, in which they describe central tendency using means and SDs and utilize t tests, ANOVA, and regression analyses (Steinberg, 2011 ).Still, taking on qualities that appear more continuous than ordinal is not inherently accompanied by interval data properties.A familiar example may better illustrate this point.Consider a course test composed of 50 items, all of which were written to assess knowledge of a particular unit in a course.
Each item is scored as either right (1) or wrong (0).Total scores on the test are calculated by summing items scored correct, yielding a possible range of 0–50.After administering the test, the instructor receives complaints from students that the test was gender biased, involving examples that, on average, males would be more familiar with than females.The instructor decides to test for evidence of this by first using a one-way ANOVA to assess whether there is a statistically significant difference between genders in the average number of items correct.As long as the focus is superficial (on the number of items correct, not on a more abstract concept, such as knowledge of the unit), these total scores are technically interval.
In this instance, a one-unit difference in total score means the same thing (one test item correct) wherever it occurs along the spectrum of possible scores.As long as other assumptions of the test were reasonable for the data (i., independence of observations, normality, homogeneity of variance; Field, 2009 ), this would be a highly suitable approach.But the test was not developed to blindly assess number of items correct; it was meant to allow inferences to be made about a student's level of knowledge of the course unit, a latent construct.
Let us say that the instructor believes this construct is continuous, normally distributed, and essentially measuring one single trait (unidimensional).The instructor did his or her best to write test items representing an adequate sample of the total content covered in the unit and to include items that ranged in difficulty level, so a wide range of knowledge could be demonstrated.Knowing this would increase confidence that, say, a student who earned a 40 knew more than a student who earned a 20.But how much more? What if the difference in the two scores were much smaller, for example, 2 points, with the lower score this time being a 38? Surely, it is possible that the student with the 40 answered all of the easier items correctly, but missed really difficult questions, whereas the student with the 38 missed a few easy ones but got more difficult questions correct.
Further, would a point difference between two very high scores (e.
, between 45 and 50) mean the same amount of knowledge difference as it would for the same difference between two midrange scores (e., 22 and a 27)? To make such claims would be to assume a one-to-one correspondence between a one-unit change in items correct and a one-unit change in knowledge.As Bond and Fox (2007) point out, “scales to which we routinely ascribe that measurement status in the human sciences are merely presumed … almost never tested empirically” (p.
The above example illustrates how data with interval qualities can emerge from nominal/ordinal data when items are combined into total scores, but that the assumption of interval properties breaks down when, without further evidence of a one-to-one correspondence, we use the observed total score to indirectly measure a latent construct, such as knowledge or attitude toward a course.As a solution to this problem, many in the measurement field point to item-based psychometric theory, such as Rasch modeling and item response theory (IRT), techniques that allow ordinal data to be rescaled to an interval metric (Harwell and Gatti, 2001 ).
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This is accomplished by using the response data for each item of large samples of respondents as empirical evidence to assess and calibrate the mathematical measurement model of their instrument (Ostini and Nering, 2006 )., how endorsable the item is) and person parameters (i., how much of the latent trait the person possesses) ., how much of the latent trait the person possesses).
) Once these parameters are reasonably estimated, the measurement model for the instrument allows the researcher to estimate a new respondent's location along the latent trait being measured by using an interval continuous scale (Bond and Fox, 2007 ).A comprehensive treatment of the work involved in developing an IRT-based measure is beyond the scope of this article, but we recommend the article “Rescaling Ordinal Data to Interval Data in Educational Research” by Harwell and Gatti (2001) for an accessible account and examples of how IRT can be used to rescale ordinal data paper. The list is comprised of the sequential enumerated citations, with details, beginning with , and is not alphabetical. Page Format. • Place references flush left. • Single-space entries, double-space between. • Place number of entry at left margin, enclose in brackets. • Indent text of entries. The following examples .A comprehensive treatment of the work involved in developing an IRT-based measure is beyond the scope of this article, but we recommend the article “Rescaling Ordinal Data to Interval Data in Educational Research” by Harwell and Gatti (2001) for an accessible account and examples of how IRT can be used to rescale ordinal data.We also recommend the book Applying the Rasch Model: Fundamental Measurement in the Human Sciences by Bond and Fox (2007) , which provides a context-rich overview of an item-based approach to Likert survey construction and assessment paper. The list is comprised of the sequential enumerated citations, with details, beginning with , and is not alphabetical. Page Format. • Place references flush left. • Single-space entries, double-space between. • Place number of entry at left margin, enclose in brackets. • Indent text of entries. The following examples .We also recommend the book Applying the Rasch Model: Fundamental Measurement in the Human Sciences by Bond and Fox (2007) , which provides a context-rich overview of an item-based approach to Likert survey construction and assessment.Figure 2 presents a decision matrix based on this initial step and offering recommended descriptive statistics and appropriate tests of association in each case.
LIKERT DATA ANALYSIS EXAMPLE FROM BIOLOGY EDUCATION In a study published in a science education research journal, the authors gave a survey of attitudes, concepts, and skills to students in a science research program obesity.LIKERT DATA ANALYSIS EXAMPLE FROM BIOLOGY EDUCATION In a study published in a science education research journal, the authors gave a survey of attitudes, concepts, and skills to students in a science research program.Students were surveyed pre-, mid-, and postprogram.The survey consisted of Likert-style items (coded 1–5).Students surveyed were engaged in either a traditional model program or a collaborative model program.Likert Scale Analysis In the article, the authors tested the internal reliability (using Cronbach's ) of each set of items (attitudes, concepts, and skills) within the survey to see whether it would be reasonable to analyze each set of items as three separate scales.
They wanted to exclude the possibility that all items correlated equally well together, thus indicating they perhaps described a unidimensional, single, latent trait.Also, if the items did not correlate together as predicted, the authors would not have had evidence supporting the validity of the items comprising a scale and should not then sum them to create scores for each scale.The researchers set a criterion that each scale had to meet an of 0.70 or greater for this to be an appropriate procedure.Scale scores were then analyzed as the dependent variable in separate repeated-measures ANOVAs with gender, ethnicity, and treatment group as between-subject factors.
What Is Defendable about This Approach? The authors checked the reliability of their Likert item sets prior to summing them for analysis., sum of Likert items), as opposed to single Likert items, likely increased the reliability of the outcome variable.Providing an estimate of the internal consistency of each Likert scale increased confidence that items on each scale were measuring something similar.
What Might Improve This Approach? The authors reported the internal consistency (a form of reliability) for each of the three scales and the results of their ANOVAs involving these scales, but no other descriptive information about the data, such as measures of central tendency or dispersion.The authors used ANOVA without providing evidence that the data assumptions of this parametric test were met.Although ANOVA is robust in the face of some violations of basic assumptions, such as normality and homogeneous variances with equal sample sizes (Glass ), describing the data would help the reader to better judge the appropriateness of the analyses.Further, the authors’ use of ANOVA treats the dependent variable as interval, but no argument for doing so or limitation of interpretation was provided.For example, the authors could conduct exploratory factor analysis in addition to computing internal reliability (using Cronbach's ) to provide evidence of the clustering of items together in these three categories.
Also, if they had adequate numbers of responses, they could use Rasch modeling (IRT) to determine whether the items were indeed of equal difficulty to suggest interval qualities.(See Table 4 for more sources of information about the specifics of ANOVA and its assumptions.) It is also worth noting that, in psychological measurement, many other aspects of reliability and validity of scales are standard in preliminary validation studies.Evidence of other aspects of the scale's reliability (e., split-half, test–retest) and validity (e., convergent validity, content validity) would bolster any claims that these scales are reliable (i., provide consistent, stable scores) and valid (i.
Table 4 also contains resources for further information related to these common issues in measurement theory.If the data were judged to be a poor fit with the assumptions of ANOVA, the authors could have chosen a nonparametric approach instead, such as the Mann-Whitney U-test.LIKERT-ITEM ANALYSIS In the same article, the authors targeted several individual Likert items from the scale measuring self-perceptions of science abilities.
The student responses to these items were summarized in a table.The authors chose these particular items, because students’ responses were indicative of key differences between two types of educational programs tested.The items were included in the table, along with the proportions of students responding “definitely yes” regarding their perceived ability level for a particular task.The authors then conducted separate Fisher exact tests to tests for differences in proportions within the “definitely yes” categories by time (pre-, mid-, and postcourse) and then by program model.What Is Defendable about This Approach? When analyzing individual Likert items, the authors used a nonparametric test for categorical data (i.
As these Likert items were ordinal to the best of the authors’ knowledge, a nonparametric test was the most fitting choice.What Might Improve This Approach? The authors transformed the items into dichotomous variables (i., 1 = definitely yes; 0 = chose a lesser category) instead of analyzing the entire spectrum of the 5-point response format or collapsing somewhere else along the range of options.There should be substantive reasons for collapsing categories (Bond and Fox, 2007 ), but the authors did not provide a rationale for this choice.It often makes sense to do so when there is a response choice with very few or no responses.Whatever the author's reason, it should be stated.RECOMMENDATIONS Validation of an attitudinal measure can be an expensive and labor-intensive process.
If you plan to measure students’ science attitudes during the planning phases of your study, look for measurement instruments that have already been developed and validated to measure the qualities you wish to study.If none exist, we recommend collaborating with a measurement expert to develop and validate your own measure.However, if this is not an option—for instance, if you are working with pre-existing data or you do not have the resources to develop and validate a measure of your own—keep in mind the following ideas when planning your analyses (Figure 2).Avoid Clustering Questions Together without Supportive Empirical Evidence In some analyses we have seen, the researcher grouped questions together to form a scale based solely on the researcher's personal perspective of which items seemed to fit well together.Then the average score across these item clusters was presented in a bar graph.
The problem with this approach is that items were grouped together to make a scale score based on face validity alone (in this case, the subjective opinion of the researcher).However, no empirical evidence of the items covariance or relationship to some theoretical construct was presented.In other words, we have no empirical evidence that these items measure a single construct.It is possible, but it is not always easy, to predict how well items comprise a unidimensional scale.Without further evidence of validity, however, we simply cannot say either way.
Failing to at least include evidence of a scale's internal consistency is likely to be noticed by reviewers with a measurement background.Report Central Tendency and Dispersion Accordingly with the Data Type For Likert items (not scales), we recommend summarizing central tendency using the median or the mode, rather than the mean, as these are more meaningful representations for categorical data.To give the reader a sense of the dispersion of responses, provide the percentage of people who responded in each response category on the item.
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In the case of a well-developed scale, it is more appropriate to compute mean scores to represent central tendency and to report SDs to show dispersion of scores.However, keep in mind the admonitions of those who champion item response approaches to scale development (e.
, Bond and Fox, 2007 ): If your measure is of a latent construct, such as student motivation, but your measure has not been empirically rescaled to allow for an interval interpretation of the data, how reasonable is it to report the mean and SD? For Scales, Statistical Tests for Continuous Data Such as F and t tests May Be Appropriate, but Proceed with Caution Researchers are commonly interested in whether variables are associated with each other in data beyond chance findings This paper is aimed at serving as an initial primer for education researchers rather than as a research paper or a comprehensive guide. we asked students to list other students in that same classroom with whom they studied; this is an example of a census network whose population is bounded within a single classroom.., Bond and Fox, 2007 ): If your measure is of a latent construct, such as student motivation, but your measure has not been empirically rescaled to allow for an interval interpretation of the data, how reasonable is it to report the mean and SD? For Scales, Statistical Tests for Continuous Data Such as F and t tests May Be Appropriate, but Proceed with Caution Researchers are commonly interested in whether variables are associated with each other in data beyond chance findings.
Statistical tests that address these questions are commonly referred to as tests of association or, in the case of categorical data, tests of independence.The idea behind a test of independence (e 12 May 2017 - 1 Essay Writing Tips: Oxford Style; 2 Oxford Writing Standards; 3 Oxford Paper Format Guidelines; 4 Oxford Style Referencing Guide; 5 Oxford Style Paper Format: Reference List Remember that the given indentation has to be single-spaced, whether or not the rest of the text has the same spacing..The idea behind a test of independence (e., chi-square test) is similar to commonly used parametric tests, such as the t test, because both of these tests assess whether variables are statistically associated with each other 12 May 2017 - 1 Essay Writing Tips: Oxford Style; 2 Oxford Writing Standards; 3 Oxford Paper Format Guidelines; 4 Oxford Style Referencing Guide; 5 Oxford Style Paper Format: Reference List Remember that the given indentation has to be single-spaced, whether or not the rest of the text has the same spacing.., chi-square test) is similar to commonly used parametric tests, such as the t test, because both of these tests assess whether variables are statistically associated with each other.If you are testing for statistical association between variables, we do not recommend analyzing individual Likert items with statistical tests such as t tests, ANOVA, and linear regression, as they are designed for continuous data.Instead, nonparametric methods for ordinal data, such as the median Mann-Whitney U-test, or parametric analyses designed for ordinal data, such as ordered logistic regression (Agresti, 2007 ), are more appropriate.If you are analyzing a Likert scale, however, common parametric tests are appropriate if the other relevant data assumptions, such as normality, homogeneity of variance, independence of errors, and interval measurement scale, are met.See Glass for a review of tests of the robustness of ANOVA in the cases of violations of some of these assumptions.
Remember, though, just like an F-test in an ANOVA, statistical significance only refers to whether variables are associated with each other.In the same way that Pearson's r or partial eta-squared with continuous data estimate the magnitude and direction of an association (effect size), measures of association for categorical data (e., odds ratio, Cramer's V) should be used in addition to tests of statistical significance.
If you are using statistical methods appropriate for continuous data, gather evidence to increase your confidence that your data are interval, or at least approximately so.
First, research the psychological characteristic you are intending to measure.Inquire whether theory and prior research support the idea that this characteristic is a unidimensional continuous trait (Bond and Fox, 2007 ).Test to see that the data you have collected are normally distributed.If you have developed your own items and scales, provide response options with wordings that model an interval range as much as possible.For example, provide at least five response options, as Likert items with five or more response options have been shown to behave more like continuous variables (Comrey, 1988 ).
If possible, run your analyses with nonparametric techniques and compare your results.If your study will include nonparametric data that may only show small effects, plan from the start for a suitable sample size to have enough statistical power.SUMMARY Student attitudes impact learning, and measuring attitudes can provide an important contribution to research studies of instructional interventions.However, the conclusions made from instruments that gauge attitudes are only as good as the quality of the measures and the methods used to analyze the data collected.When researchers use scores on attitudinal scales, they must remember these scores serve as a proxy for a latent construct.
As such, they must have supporting evidence for their validity.In addition, data assumptions, including the level of measurement, should be carefully considered when choosing a statistical approach.Even though items on these scales may have numbers assigned to each level of agreement, it is not automatically assumed that these numbers represent equally distant units that can provide interval-level data necessary for parametric statistical procedures.Development and validation of instruments to measure learning of expert-like thinking.
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