Example Of Analysis Of Data In Research Paper


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A. The research project

1.                  The aim

The aim of the research paper, which is a requirement in the second term of the academic year for the advanced students, and sometimes for the intermediate students too, is to equip our students with precious skills of conducting research using various sources and then putting up all the data they have gathered into one meaningful whole and interpreting the results. They also learn how to format papers, how to present information, how to cite works and write bibliographies. In short, they learn those skills when stakes are not too high. Starting with their freshman year, the students will have to conduct research and will be graded on their work. In their prep year they can do that to practice.

2.                  What can be done as research projects:

2.1. Lifting or plagiarism: A common complaint of all prep school teachers is that students use material available on the internet, or in library books, copy information from these sources and without any acknowledgement, modification, analysis or paraphrasing submit the paper to their teachers. In such cases, many teachers are led to believe that doing research papers is pointless if not futile.

2.2 What topics lead to lifting: The most important step to prevent lifting is choosing the research question carefully. When the research question is not well formulated or when the student goes for information that can be found in encyclopedias, lifting becomes inevitable.

e.g. “ The History of ”

With a topic like this, our students are bound to come up with encyclopedic information. A prep school student, who is no expert in history, cannot interpret the history of using the sources he has found. He is going to find some books from the library, visit a few websites, find a few tourist brochures, put them together and write a paper, which is 90% plagiarized.

e.g. “Environmental Pollution in the Mediterranean Region”

Again such a topic is dangerous because 1. It is too broad as a topic, 2. It requires expert knowledge to interpret, 3. Our students are not informed enough to put together that kind of information intelligently. Therefore, the result is going to be quoting one or two writers without even acknowledging the sources.

e.g. “The Komodo Monster”(which is my favorite example.)

What can a student do about such a topic apart from consulting some encyclopedias or natural history books? A student of mine presented information in such a way that it looked like he himself had done all the studies in Malaysia jungles, observing the hunting habits of this monstrous lizard.

3.                  The correct research question:

Our students are novices in every way: They are novices in English language and they are also novices in academic life. Therefore, when they are assigned the question “What is X?” they will go to the library and gather information from whatever source they can find, put it together without putting it through any analytical process of thinking.

The correct research question must be formulated so as to produce results that the students have to find out by themselves, or at least that they have to interpret intelligently and with sufficient amount of reasoning.

Given their level of academic expertise, students must be pursuing research on topics that they are familiar with, or that they can study safely using their general knowledge plus some amount of reading. (The golden rule of(i+1) where “i” represents the student’s level of competence or information.) The reading they do must be of the kind they can analyze and read critically with their level of knowledge and English.

e.g. An oral history project to be done with the residents of one of the , for example with the storekeepers in BurgazAda , collecting their memories about the Turkish writer SaitFaikAbasiyanik. Prep school students can handle a project of this size and scope, with

  • some reading about the life of S. F. Abasiyanik,
  • studying some oral history interview techniques
  • minimal knowledge of transcribing their interviews
  • making sense of the data they have gathered.

e.g. Environmental pollution in YADYOK building or on BU campus.

  • setting the parameters of pollution
  • choosing some unobtrusive methods such as interviewing the personnel in charge of wasteremoval from the YADYOK building
  • interviewing the faculty secretary and the janitors, for instance,
  • reading some articles for theoretical background
  • reporting the results of their research

students may come up with data showing the extent of the pollution we are producing here before our noses.

B. Types of research

The research project can be of two types

  • Library research
  • Qualitative research

There are also quantitative methods of research; however, since our students do not possess the required knowledge of statistics that accompany that kind of research, we should make do with the two types mentioned above.

B.1.     Library research: As discussed above library research, or in more modern form the Internet sources, usually takes the form of informative research, that is the student gathers information on a topic. A library or Internet research project should be carefully monitored by the teacher to prevent plagiarizing.  A library research paper usually takes the form of the literature review paper.

        I. Purpose of the literature review paper The purpose of the literature review paper may be:

  1. State-of-the-art review: What information exists on the topic? What are the current views/ knowledge/theories/methods in the field?

e.g. AIDS: What medical knowledge is there?

What are the current methods of treatment?

What drugs, medicine are used?

What is the epidemiology of the disease?

  1. Historical review: This review aims at revealing the history of the development of a topic.

e.g. Theories of second language learning from past to present

  1. Comparison of perspectives: the focus is on the comparison of theories, or approaches to an issue.

e.g. Theories of second language learning compared and contrasted

As discussed above, such a research process needs to be carefully monitored by the teacher. Starting from the submission of the research proposal, the teacher should emphasize the importance of student contribution and originality. During the stage of writing the paper, the importance of paraphrasing, summarizing and quoting from the original sources needs to be emphasized. Otherwise, students are bound to produce plagiarized papers.

Instead, such literature review may be integrated into the research paper itself. Literature review is an integral part of every research paper, and preliminary reading constitutes the backbone of the research process. From choosing a topic to what method to use, from the interpretation of data to the interpretation of results, the researcher has to refer to data and scientific knowledge accumulated by other researchers in the field.

II. How to conduct library research for literature review:

When looking for sources about a topic, students should consider the following issues:

  • What information is available on the topic?
  • What variables are there?
  • Which of the variables have been studied?
  • Which variables might be there in addition to those studied?
  • What new information is there?
  • What needs to be studied?
  • What concepts/ theories/ studies constitute the fundamentals of the issue?
  • What alternative theories are there?

While doing literature review, students may discover new areas to be studied and they may modify their initial questions.

For a sample of a paper based on library research, see “Tolkien and The Lord of the Rings”

B.2.   Qualitative research:Our aim should be maximum student involvement and qualitative research projects serve this purpose better.

What is qualitative research?

Qualitative research may include such methods as:

  • Interviews
  • Surveys
  • Observation
  • Oral history

Such techniques are easier to use, do not require exact measurements or sophisticated statistical methods, and with right amount of guidance our students can safely conduct research in the fields with which they are slightly familiar.

Qualitative research produces descriptive data based on the researcher’s observations or on the words of the people interviewed. Such data cannot be subjected to quantitative (statistical) analysis methods, but give in depth information on the subject studied.


In order to learn the views, opinions, and evaluations of people, we conduct interviews. Interviews can be of two types:

In structured interviews, students prepare a set of questions and try to obtain answers to these questions. Data analysis is easier, because they  have comparable categories for each respondent, and they can analyze what each respondent said as an answer to each question and compare and contrast their answers.

Unstructured interviews: The researchers only have the topic of the interview but no set questions to ask the interviewee. The interview may follow whatever course the interviewee chooses to talk about. Every subject may dwell on a different aspect of the topic in question, and as a result, data from individual subjects may not be comparable. On the other hand, such data provide in depth information in great detail about individual subjects.

For our own purposes, structured interviews where the interviewer focuses on a set of predetermined questions, and tries to obtain answers to these questions are more feasible since we cannot expect our students to conduct case studies or personality analyses.

I. Finding subjects: The selection of subjects to be interviewed depends on the topic of study. However, there are certain guidelines the interviewer should not neglect:

  1. Do not interview people you know well personally. In such cases, the subjects hesitate to open up and share their genuine opinions with the interviewer they know personally. The answers they give will be answers given to the person they know personally, not the answers they would give to an interviewer with whom they have no personal relations.
  2. It is difficult to find the right people to interview. One way is using your contacts. If you know people who know the people you want to interview, use your contacts and get an introduction to those people.
  3. If you have no contacts, you may go and contact directly the people you want to study. If you are lucky and approach the target group wisely, most people may agree to collaborate with you.
  4. Always introduce yourself, tell your name, where you come from, your school, what your study is about, what you are trying to do. If necessary, get a letter from your teacher describing your research study and introducing you.

II. The interview

  • The interview should last as long as necessary for the interviewer to obtain the answers she needs, and for the interviewee to express her opinions adequately.
  • If possible record the interview. To do the recording, it is necessary to ask for the permission of the interviewee first.
  • Make sure that all the preset questions are answered.
  • Take notes during the interview. Taking notes helps you to record impressions that might have gotten lost if not written down and also shows to the interviewee that you are actively interested in what he has to say.

A soon as you get home, write down your impressions, comments, etc. before you forget them. It is a good idea to keep a research journal in which you record all your observations, questions, problems, and interpretations.


III. Tips on how to conduct an interview

Most people are happy to answer the questions asked by university students and welcome them, showing a cooperative attitude. However, there are a few guidelines every wise researcher must be careful about:

  • Be respectful, friendly and accepting
  • Don’t argue with your interviewees
  • Don’t judge them as right or wrong
  • Let them talk at their own speed, with their choice of topics. Sometimes it may be necessary to guide the subjects into the topic of the interview, asking a few questions, clarifying points.

IV. Analysis of interviews

The data obtained during the interviews can be analyzed in two ways:

  1. Each interview can be analyzed and reported as an individual case. The researcher summarizes the data, highlights certain points, lists points of importance, and draws conclusions.
  2. Data from different interviews can be analyzed for comparative purposes, thus each respondent’s answers are classified and interpreted in terms of points of comparison, in terms of their attitude to certain topics. Their opinions, evaluations, responses are classified and then compared.

Depending on the topic of research, one of the two methods may be used.

For a sample paper based on interview technique, see “Problems of the Turkish Theater”


Surveys are one of the most frequently used methods of social research, and are used by the government, academic researchers in universities, campaign organizations, marketing researchers, opinion pollsters, and many similar groups.

All surveys aim to describe or explain the characteristics or opinions of a general population through the use of a representative sample.

Our students too can conduct surveys of a smaller scale with a set of carefully designed questions. Examples of topics may be the leisure time activities of BU students or their attitudes towards current issues or their opinions on certain topics.

Comparative studies are also within the scope of such survey studies, e.g. comparison of the attitudes of BU students and ITU students to current political issues.

The theory in survey method is that all subjects are asked the same questions in the same way, therefore a questionnaire must be prepared and a sample of the target group must be taken.

I. Sampling

The important principle our students at this introductory level should know is that the sample should be representative of the population. For instance, if the students want to study the BU students in general, the sample should not consist of prep students only, but should include students from each year of the university. Or if they target the students staying at the dorm, then they should not include those students who do not stay at the dorm. More advanced principles of representativeness of the sample can be ignored for our purposes. However, since requirements of representativeness – such as random sampling or quota sampling, which require some knowledge of statistics - cannot be met in our case, we should not expect the students to employ any statistical methods, since these methods are based on such assumptions of representativeness.

II. Constructing the questionnaire

The most important point to be considered when designing a questionnaire is to construct the questions unambiguously and to be clear in mind about what the question is for, what it tries to find out or assess.

If the researcher keeps in mind these issues, questions will be well designed.

    • What function does each question serve?
    • What is the aim in asking this question?
    • Is it relevant to the purpose of the study?

After formulating the questions, it is a good idea to test them on a few people. Other people may interpret the questions differently from the questionnaire writer. Therefore, if the questions are piloted before they are given to the target population, possible misunderstandings and ambiguities in the questions may be remedied before they are actually used.

Language of the questions: Questions should be

·        In the language of the target population

·        Clear enough to be understood by the respondents

·        Clear enough to bring out the information the researcher is looking for

·        Worded as simply as possible

A good question does not lead the respondents or lead to ambiguity.

e.g. “How many newspapers a day do you buy?”

This question assumes that the respondents buy newspapers everyday. A better way to ask would be:

“Do you buy newspapers?”

If yes, “Do you buy newspapers every day?”

If yes, “How many newspapers a day do you buy?”


3. Observation

Our students can use observation technique to gather data on a topic of their choice. One simple example would be observing classroom behavior. Things to be observed could be “Are girls more active than boys?” or “How do teachers respond to disruptive behavior in class?”

I. Conducting successful observations

When making observations the important point is knowing what to observe. A lot of people look around in a classroom and see nothing unusual or nothing worth recording. However, to a trained eye there are patterns of behavior emerging, responses being given and themes forming. In order to make successful observations:

1.    Decide what behavior to observe

2.  Decide how you will make the observation: with the help of a checklist or unstructured observation. In either way, the researcher starts with a set of questions.

3.    Choose a setting: decide where you will observe the group you have selected

4.    Look carefully

5.    Make notes while you are observing the target group

6.    Evaluate your notes immediately after you finish your observation

7.    Analyze your data

If we go back to the initial example of “How do teachers respond to disruptive behavior in class?”

1. You decide what constitutes disruptive behavior, e.g. students talking among themselves, not responding to the teacher, doing other things in class, coming to class late, etc.

2.    Decide how you will do the observation: In class, from beginning to the end of the lesson, how many hours

3.    Decide where the observation will take place: In which school, in which class, etc.

4.    Decide whether to make notes or use a check list

II. Data analysis

v     Data can be analyzed in terms of the frequency of occurrence of the behavior or the emergence of themes and patterns.


e.g. Disruptive behavior study

·         How many times each type of behavior occurred

·         How the teachers responded and with what frequency

v     Another approach may be just going to the class, watching carefully what is going on during the lesson between the teacher and the students, seeing what the teacher considers as disruptive behavior and recording the behavior and how it is treated by the teacher.

Data analysis of such an observation will be more in a case study or narrative format.

e.g. Study on disruptive behavior in class

“In a class a student who had a leading role among his class mates was hostile towards the class teacher. He tried to make all the jokes himself and wanted to get a good laugh from the class. When the teacher tried to make a joke, the student tried to stop his class mates from laughing at the teacher’s jokes, thus preventing them from forming an alliance with the teacher.”

Observation, be it structured or unstructured, may produce many interesting results and can also teach our students to really look at things happening in their surroundings and see patterns, themes and order in what seems to be chaotic. Observation can also produce further research questions and is conducive to formulating further research projects.

4. Oral History

Different from written history, which records global events and changes of historical importance, oral history concerns itself with the experiences, memories, and evaluations of individuals. These individual recollections also constitute a part of history and are of significant importance because they bear witness to events from the viewpoint of the individual members of the society. With the spread of recording machines and the Internet, oral history studies which focus on the experiences and personal accounts of individuals gained themselves a niche in keeping records of the present and the past for the future generations.   

“We all have stories to tell, stories we have lived from the inside out. We give our experiences an order. We organize the memories of our lives into stories.

Oral history listens to these stories. Oral history is the systematic collection of living people’s testimony about their own experiences. Historians have finally recognized that the everyday memories of everyday people, not just the rich and famous, have historical importance. If we do not collect and preserve those memories, those stories, then one day they will disappear forever.

Your stories and the stories of the people around you are unique, valuable treasures for your family and your community. You and your family members can preserve unwritten family history using oral history techniques. Likewise you and your community can discover and preserve unwritten history large and small. Oral history is so flexible that people of all ages can adapt the techniques of asking and listening to create and learn about history and historical narratives.”

Moyer (1999, Step by Step Guide to Oral History)

A useful link where you can find all basic information on how to conduct oral history studies is:


With knowledge of simple techniques of asking questions and recording the data, our students too can employ oral history methods to record the recollections of people.

e.g. Oral history project in Arnavutkoy: "Arnavutkoy past and present, neighborly relations in the past and the present."

A group of advanced students in spring 2004 conducted such a study in Arnavutkoy. The steps were as follows:

·         They started to read about the past of the neighborhood, and as they read on they came up with more questions

·         They contacted the neighborhood NGO (ArnavutkoySemtGirisimi), and started talking with them about the looming danger of the third bridge across the Bosphorus.

·         They contacted the local residents

·         They started to gather information about which local residents are knowledgeable about the past of the neighborhood

·         They conducted interviews with these old residents and video recorded the interviews

·         They took pictures of the neighborhood residents and the key architectural spots

As can be seen, such a project involves the following steps:

·         Background reading

·         Setting up the research topic

·         Setting up the interview questions to focus on

·         Finding people

·         Conducting interviews

·         Recording the interviews

·         Evaluation of the data

 For sample research papers based on oral history technique, see “Arnavutkoy: Past and Present” and

C. The research process

1.    The proposal

The teachers must ask their students to formulate a research question and write a proposal in which the students state

  • Their research question,
  • The method they are going to employ to collect data
  • The results they expect to find

After the teachers approve the research proposal, the students can then start to collect data.

2.    Preliminary reading

Qualitative research does not preclude reading and consulting resources in the library or on the Internet because in order to

·        formulate their research proposal,

·        construct their observational tools,

·        develop their interview questions,

·        interpret the data they have gathered,

the students will need some background information. This information they can only acquire by doing some reading. Therefore, as a second step after the submission of the proposals teachers can ask their students to submit a bibliography of related reading.

3.    Setting up a work schedule

From the very start, it is a good idea to set up a work schedule, announce the deadlines to the students and keep a copy of this on the class bulletin board.

·        Working towards deadlines,

·        Planning ahead

·        Keeping the deadlines

must be some of the basic skills the students must learn while doing these projects.

4.    Keeping a research log book or a journal

One way of making sure that the students are carrying out the research work themselves and also going in the right direction is to ask them to keep a research journal. In this journal, which may be checked by the teacher at regular intervals, the students can write

·        The stages of research they are in

·        The problems they encounter

Data analysis and presentation

Scope and purpose
Quality indicators

Scope and purpose

Data analysis is the process of developing answers to questions through the examination and interpretation of data.  The basic steps in the analytic process consist of identifying issues, determining the availability of suitable data, deciding on which methods are appropriate for answering the questions of interest, applying the methods and evaluating, summarizing and communicating the results. 

Analytical results underscore the usefulness of data sources by shedding light on relevant issues. Some Statistics Canada programs depend on analytical output as a major data product because, for confidentiality reasons, it is not possible to release the microdata to the public. Data analysis also plays a key role in data quality assessment by pointing to data quality problems in a given survey. Analysis can thus influence future improvements to the survey process.

Data analysis is essential for understanding results from surveys, administrative sources and pilot studies; for providing information on data gaps; for designing and redesigning surveys; for planning new statistical activities; and for formulating quality objectives.

Results of data analysis are often published or summarized in official Statistics Canada releases. 


A statistical agency is concerned with the relevance and usefulness to users of the information contained in its data. Analysis is the principal tool for obtaining information from the data.

Data from a survey can be used for descriptive or analytic studies. Descriptive studies are directed at the estimation of summary measures of a target population, for example, the average profits of owner-operated businesses in 2005 or the proportion of 2007 high school graduates who went on to higher education in the next twelve months.  Analytical studies may be used to explain the behaviour of and relationships among characteristics; for example, a study of risk factors for obesity in children would be analytic. 

To be effective, the analyst needs to understand the relevant issues both current and those likely to emerge in the future and how to present the results to the audience. The study of background information allows the analyst to choose suitable data sources and appropriate statistical methods. Any conclusions presented in an analysis, including those that can impact public policy, must be supported by the data being analyzed.


Initial preparation

  • Prior to conducting an analytical study the following questions should be addressed:

    • Objectives. What are the objectives of this analysis? What issue am I addressing? What question(s) will I answer?

    • Justification. Why is this issue interesting?  How will these answers contribute to existing knowledge? How is this study relevant?

    • Data. What data am I using? Why it is the best source for this analysis? Are there any limitations?

    • Analytical methods. What statistical techniques are appropriate? Will they satisfy the objectives?

    • Audience. Who is interested in this issue and why?

 Suitable data

  • Ensure that the data are appropriate for the analysis to be carried out.  This requires investigation of a wide range of details such as whether the target population of the data source is sufficiently related to the target population of the analysis, whether the source variables and their concepts and definitions are relevant to the study, whether the longitudinal or cross-sectional nature of the data source is appropriate for the analysis, whether the sample size in the study domain is sufficient to obtain meaningful results and whether the quality of the data, as outlined in the survey documentation or assessed through analysis is sufficient.

  •  If more than one data source is being used for the analysis, investigate whether the sources are consistent and how they may be appropriately integrated into the analysis.

Appropriate methods and tools

  • Choose an analytical approach that is appropriate for the question being investigated and the data to be analyzed. 

  • When analyzing data from a probability sample, analytical methods that ignore the survey design can be appropriate, provided that sufficient model conditions for analysis are met. (See Binder and Roberts, 2003.) However, methods that incorporate the sample design information will generally be effective even when some aspects of the model are incorrectly specified.

  • Assess whether the survey design information can be incorporated into the analysis and if so how this should be done such as using design-based methods.  See Binder and Roberts (2009) and Thompson (1997) for discussion of approaches to inferences on data from a probability sample.

    • See Chambers and Skinner (2003), Korn and Graubard (1999), Lehtonen and Pahkinen (1995), Lohr (1999), and Skinner, Holt and Smith (1989) for a number of examples illustrating design-based analytical methods.

    • For a design-based analysis consult the survey documentation about the recommended approach for variance estimation for the survey. If the data from more than one survey are included in the same analysis, determine whether or not the different samples were independently selected and how this would impact the appropriate approach to variance estimation.

    • The data files for probability surveys frequently contain more than one weight variable, particularly if the survey is longitudinal or if it has both cross-sectional and longitudinal purposes. Consult the survey documentation and survey experts if it is not obvious as to which might be the best weight to be used in any particular design-based analysis.

    • When analyzing data from a probability survey, there may be insufficient design information available to carry out analyses using a full design-based approach.  Assess the alternatives.

  • Consult with experts on the subject matter, on the data source and on the statistical methods if any of these is unfamiliar to you.

  • Having determined the appropriate analytical method for the data, investigate the software choices that are available to apply the method. If analyzing data from a probability sample by design-based methods, use software specifically for survey data since standard analytical software packages that can produce weighted point estimates do not correctly calculate variances for survey-weighted estimates.

  • It is advisable to use commercial software, if suitable, for implementing the chosen analyses, since these software packages have usually undergone more testing than non-commercial software.

  • Determine whether it is necessary to reformat your data in order to use the selected software.

  • Include a variety of diagnostics among your analytical methods if you are fitting any models to your data.

  • Data sources vary widely with respect to missing data.  At one extreme, there are data sources which seem complete - where any missing units have been accounted for through a weight variable with a nonresponse component and all missing items on responding units have been filled in by imputed values.  At the other extreme, there are data sources where no processing has been done with respect to missing data.  The work required by the analyst to handle missing data can thus vary widely. It should be noted that the handling of missing data in analysis is an ongoing topic of research.
    • Refer to the documentation about the data source to determine the degree and types of missing data and the processing of missing data that has been performed.  This information will be a starting point for what further work may be required.

    • Consider how unit and/or item nonresponse could be handled in the analysis, taking into consideration the degree and types of missing data in the data sources being used.

    • Consider whether imputed values should be included in the analysis and if so, how they should be handled.  If imputed values are not used, consideration must be given to what other methods may be used to properly account for the effect of nonresponse in the analysis.

    • If the analysis includes modelling, it could be appropriate to include some aspects of nonresponse in the analytical model.

    • Report any caveats about how the approaches used to handle missing data could have impact on results

Interpretation of results

  • Since most analyses are based on observational studies rather than on the results of a controlled experiment, avoid drawing conclusions concerning causality.

  • When studying changes over time, beware of focusing on short-term trends without inspecting them in light of medium-and long-term trends. Frequently, short-term trends are merely minor fluctuations around a more important medium- and/or long-term trend.

  • Where possible, avoid arbitrary time reference points. Instead, use meaningful points of reference, such as the last major turning point for economic data, generation-to-generation differences for demographic statistics, and legislative changes for social statistics.

Presentation of results

  • Focus the article on the important variables and topics. Trying to be too comprehensive will often interfere with a strong story line.

  • Arrange ideas in a logical order and in order of relevance or importance. Use headings, subheadings and sidebars to strengthen the organization of the article.

  • Keep the language as simple as the subject permits. Depending on the targeted audience for the article, some loss of precision may sometimes be an acceptable trade-off for more readable text.

  • Use graphs in addition to text and tables to communicate the message. Use headings that capture the meaning (e.g. "Women's earnings still trail men's") in preference to traditional chart titles (e.g."Income by age and sex"). Always help readers understand the information in the tables and charts by discussing it in the text.

  • When tables are used, take care that the overall format contributes to the clarity of the data in the tables and prevents misinterpretation.  This includes spacing; the wording, placement and appearance of titles; row and column headings and other labeling. 

  • Explain rounding practices or procedures. In the presentation of rounded data, do not use more significant digits than are consistent with the accuracy of the data.

  • Satisfy any confidentiality requirements (e.g. minimum cell sizes) imposed by the surveys or administrative sources whose data are being analysed.

  • Include information about the data sources used and any shortcomings in the data that may have affected the analysis.  Either have a section in the paper about the data or a reference to where the reader can get the details.

  • Include information about the analytical methods and tools used.  Either have a section on methods or a reference to where the reader can get the details.

  • Include information regarding the quality of the results. Standard errors, confidence intervals and/or coefficients of variation provide the reader important information about data quality. The choice of indicator may vary depending on where the article is published.

  • Ensure that all references are accurate, consistent and are referenced in the text.

  • Check for errors in the article. Check details such as the consistency of figures used in the text, tables and charts, the accuracy of external data, and simple arithmetic.

  • Ensure that the intentions stated in the introduction are fulfilled by the rest of the article. Make sure that the conclusions are consistent with the evidence.

  • Have the article reviewed by others for relevance, accuracy and comprehensibility, regardless of where it is to be disseminated.  As a good practice, ask someone from the data providing division to review how the data were used.  If the article is to be disseminated outside of Statistics Canada, it must undergo institutional and peer review as specified in the Policy on the Review of Information Products (Statistics Canada, 2003). 

  • If the article is to be disseminated in a Statistics Canada publication make sure that it complies with the current Statistics Canada Publishing Standards. These standards affect graphs, tables and style, among other things.

  • As a good practice, consider presenting the results to peers prior to finalizing the text. This is another kind of peer review that can help improve the article. Always do a dry run of presentations involving external audiences.

  • Refer to available documents that could provide further guidance for improvement of your article, such as Guidelines on Writing Analytical Articles (Statistics Canada 2008 ) and the Style Guide (Statistics Canada 2004)

Quality indicators

Main quality elements:  relevance, interpretability, accuracy, accessibility

An analytical product is relevant if there is an audience who is (or will be) interested in the results of the study.

For the interpretability of an analytical article to be high, the style of writing must suit the intended audience. As well, sufficient details must be provided that another person, if allowed access to the data, could replicate the results.

For an analytical product to be accurate, appropriate methods and tools need to be used to produce the results.

For an analytical product to be accessible, it must be available to people for whom the research results would be useful.


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Binder, D.A. and G. Roberts. 2009. "Design and Model Based Inference for Model Parameters." In Handbook of Statistics 29B: Sample Surveys: Inference and Analysis. Pfeffermann, D. and Rao, C.R. (eds.) Vol. 29B. Chapter 24. Amsterdam.Elsevier. 666 p.

Chambers, R.L. and C.J. Skinner (eds.) 2003. Analysis of Survey Data. Chichester. Wiley. 398 p.

Korn, E.L. and B.I. Graubard. 1999. Analysis of Health Surveys. New York. Wiley. 408 p.

Lehtonen, R. and E.J. Pahkinen. 2004. Practical Methods for Design and Analysis of Complex Surveys.Second edition. Chichester. Wiley.

Lohr, S.L. 1999. Sampling: Design and Analysis. Duxbury Press. 512 p.

Skinner, C.K., D.Holt and T.M.F. Smith. 1989. Analysis of Complex Surveys. Chichester. Wiley. 328 p.

Thompson, M.E. 1997. Theory of Sample Surveys. London. Chapman and Hall. 312 p.

Statistics Canada. 2003. "Policy on the Review of Information Products." Statistics Canada Policy Manual. Section 2.5. Last updated March 4, 2009.

Statistics Canada. 2004. Style Guide.  Last updated October 6, 2004.

Statistics Canada. 2008. Guidelines on Writing Analytical Articles. Last updated September 16, 2008.

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