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Primary Data Collection: Questionnaire – statswork
 

Primary Data Collection: Questionnaire

 

Primary Data Collection: Questionnaire

One of the most widely used research techniques can be defined as collecting data through written questions (Blaxter, 1996; Neuman, 2000; Yin, 2003; Tashakkori & Teddlie, 1998). There are a number of different ways in which questionnaires can be administered; for example: posted to the intended respondents or administered over the telephone or face-to-face. A questionnaire design provides a quantitative description of trends, attitudes, or opinions of a population by studying a sample of that population. From sample results, the researcher generalises or makes claims about a population (Creswell, 2007).

Strength of questionnaire

It is appropriate for measuring attitudes and electing other content from research participants, inexpensive (i.e., main questionnaires and group administered questionnaires), it can provide information about participants’ internal meanings and ways of thinking, it can administer to probability samples, it has a quick turnaround, it can be administered to groups, it has perceived anonymity by respondents, and it has a moderately high measurement validity (i.e., high reliability and validity) for well-constructed and validated questionnaires, it can provide exact information needed by researcher using closed-ended items, it can provide detailed information in respondents’ own words using open-ended items, it is unproblematic to analyse data for closed-ended items, and it is useful for exploration as well as confirmation.

Weaknesses of questionnaire

Usually must be kept short, reactive effects may occur (i.e., interviewees may try to show only what is socially desirable), nonresponse to selective items, people filling out questionnaires may not recall important information and may lack self-awareness, response rate may be low for mail and email questionnaires, open-ended items may reflect differences in verbal ability; obscuring the issues of interest, Data Analysis can be time consuming for open-ended items, and measures need validation (Neuman, 2000, 2003; Creswell, 2007; Antonius, 2003).

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    Methodology consultationQuestionnaire designData collectionData cleaning Analysis of dataInterpretation of analysisOthers

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