Category Archives: Research Methods

How to Create a Reference Page and Citations in MS Word

1. First, create a Bibliography in Word 2007/2010

1) Click References tab

2) Click Manage Sources on the Citations & Bibliography menu

3) Either Copy sources from the Master List to the Current List or create new sources that will automatically be added to both the Master and Current List

a. Sources in the Current List will be shown in the dropdown Insert Citation list.
Make your selection.

b. Enter information for each source.

4) Once all your sources are entered, close the window.

5) Select Style on the Citations & Bibliography menu and choose the appropriate style (typically APA but differs with professor; for Swasy, choose Chicago)

6) Click the Bibliography dropdown list and select Insert Bibliography

7) The bibliography will appear in your Word doc.

8) Edit accordingly (most bibliographies are double spaced)

2. Then, create EITHER Footnotes OR In-Text Citations

To Create Footnotes

1) Click References tab

2) Click Insert Footnote from the Footnotes menu Make sure your cursor has clicked the place in text where you want to cite the footnote

3) Word will direct you to fill in the footnote at the bottom of the page

4) Chicago Style footnotes/endnotes look like this:

Firstname Lastname, “Title of Webpage,” Publishing Organization or Name of Web Sit in italics, publication date and/or access date if available, URL Word will have the corresponding bibliography entry when you Insert Bibliography at the end of your paper.

To Create In-Text Citations

1) Click References tab
2) Click Insert Citation from the Citations & Bibliography menu and select appropriate source from the dropdown list
3) Make sure you have selected the appropriate style from the Style section of the Citations & Bibliography menu


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Posted by on April 17, 2014 in Research Methods, Uncategorized


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Interview Question Research

A questionnaire is one of the simplest data gathering mechanisms, and one which is well-suited to classroom use — most academics, for example, use questionnaires during or at the end of modules to ascertain the effectiveness of the teaching/learning during the module.

Questionnaires are a good way of ascertaining simple information quickly, but writing an effective questionnaire is a skill which is much more difficult to master than might be expected. Each question included — whatever type is chosen — must be carefully worded so as to be clear and unambiguous, and to allow for a full range of possible responses, and each question should be “neutral”, so as not to influence the choice of response. The layout of the questionnaire must not confuse the respondents, and should assist them in its completion. Furthermore, a questionnaire should be attractive so as to enthuse the respondent to complete it fully.

A questionnaire should be piloted (that is, trialled on a small group of respondents — possibly even colleagues) before it is actually deployed. This is really important — however well a questionnaire has been written, it is still possible for ambiguous or misleading phrases to have been accidentally used.

The types of question may be used for different purposes. Basic factual questions can help to provide a framework within which further questions make sense (such as age, gender, course enrolment, etc.). Questions about behaviour yield factual “added value” which is of direct interest. Other questions, which rely on the respondents’ opinions or judgements, may be difficult to interpret.

Questionnaires can be delivered on paper or online. Paper has the advantage that copies can be distributed at a suitable moment when respondents are co-located (for example, the start of a lecture), however they would then need manual or semi-manual processing (although OCR technology can assist). Online questionnaires can be managed with minimal human intervention, and consequent avoidance of transcription errors, but response rates may be low unless it must be completed as part of another online activity. The online approach, however, allows for innovative presentation (possibly inclusion of multimedia), for rapid turnround rates, and if appropriate for a questionnaire to be customised for individual respondents. Further issues to be considered include preserving anonymity online, since responses are potentially traceable, and avoiding duplicate responses.

Questions can include the following basic types:

  • yes/no are quick to code responses, and forces the respondent to make a decision;
  • multiple choice questions assume a range of expected responses;
  • rank orderings are similar to multiple choice, but with added information about “priorities”;
  • rating scales (such as Likert scales) offer flexible responses; and
  • free response (open-ended) questions are appropriate when there is no expected range of answers.

Analysis of questionnaire data is normally quantitative, since a (relatively) large number of responses may be aimed for, sufficient to perform basic statistical analyses. Questions which require a “text field” response may need a simple qualitative analysis, but a questionnaire is normally inappropriate to elicit good qualitative data.

Anonymity is an issue. If a questionnaire asks for a respondent’s name or ID, then the responses to questions may not be accurate — for example, a student may not believe assurances that the data will not be used to grade them. On the other hand, knowledge of a respondent’s identity may help to correlate the data with other information, such as identifying factors which might have affected a student’s grade. This is a sensitive issue.

An approach which can be employed is the “follow-up” — respondents may be asked permission to be approached subsequently in order to gather further data. Also, where a significant proportion of potential respondents have failed to respond, it may be appropriate to contact them again for a second (or even a third) attempt at getting them to complete the questionnaire.


  • Fast to administer.
  • A good source of quantitative data.
  • Anonymity may elicit genuine responses.
  • Online questionnaires can be unintrusive.


  • Significant time and resources are required to design a questionnaire.
  • Anonymity may prevent the answers being correlated with other data.
  • Responses may be inaccurate because respondents may give answers they think are expected.


How does students’ expertise in programming before University affect the perceived difficulty of their introductory programming module?

Further reading

Most books on social science research address the basics of questionnaire writing, including Cohen (2000, 245-266), and we have found Foddy (1993), Gillham (2000) and Oppenheim (1992) particularly helpful. Oppenheim’s text, in particular, is accessible and easy to source.


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Posted by on December 27, 2013 in Research Methods


Factor analysis

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved, uncorrelated variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in fewer such unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors, plus “error” terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Computationally this technique is equivalent tolow rank approximation of the matrix of observed variables. Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.

Type of factor analysis

Exploratory factor analysis (EFA) is used to uncover the underlying structure of a relatively large set of variables. The researcher’s a priori assumption is that any indicator may be associated with any factor. This is the most common form of factor analysis. There is no prior theory and one uses factor loadings to intuit the factor structure of the data.

Confirmatory factor analysis (CFA) seeks to determine if the number of factors and the loadings of measured (indicator) variables on them conform to what is expected on the basis of pre-established theory. Indicator variables are selected on the basis of prior theory and factor analysis is used to see if they load as predicted on the expected number of factors. The researcher’s a priori assumption is that each factor (the number and labels of which may be specified a priori) is associated with a specified subset of indicator variables. A minimum requirement of confirmatory factor analysis is that one hypothesizes beforehand the number of factors in the model, but usually also the researcher will posit expectations about which variables will load on which factors. The researcher seeks to determine, for instance, if measures created to represent a latent variable really belong together.

[edit]Types of factoring

Principal component analysis (PCA): The most common form of factor analysis, PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables. It then removes this variance and seeks a second linear combination which explains the maximum proportion of the remaining variance, and so on. This is called the principal axis method and results in orthogonal (uncorrelated) factors.

Canonical factor analysis, also called Rao’s canonical factoring, is a different method of computing the same model as PCA, which uses the principal axis method. CFA seeks factors which have the highest canonical correlation with the observed variables. CFA is unaffected by arbitrary rescaling of the data.

Common factor analysis, also called principal factor analysis (PFA) or principal axis factoring (PAF), seeks the least number of factors which can account for the common variance (correlation) of a set of variables.

Image factoring: based on the correlation matrix of predicted variables rather than actual variables, where each variable is predicted from the others using multiple regression.

Alpha factoring: based on maximizing the reliability of factors, assuming variables are randomly sampled from a universe of variables. All other methods assume cases to be sampled and variables fixed.

Factor regression model: a combinatorial model of factor model and regression model; or alternatively, it can be viewed as the hybrid factor model,[2] whose factors are partially known.


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Posted by on February 3, 2012 in Research Methods

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