Р. П. Мильруд Доктор педагогических наук, профессор кафедры иностранных языков
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| schedules), refers to those questionnaires where interviewers physically meet respondents and ask the questions face to
face. These differ from semi-structured and in-depth interviews, as there is a defined schedule of questions, from which interviewers should not deviate. Prior to designing a questionnaire, you must know precisely what data you need to collect to answer your research question(s). The validity and reliability of the data you collect depend largely on the design of your questions, the struc- ture of your questionnaire, and the rigour of your pilot testing. When designing your questionnaire you should consider the wording of individual questions prior to the order in which they appear. Questions can be divided into open and closed. The six types of closed questions are list, category, ranking, rating (scale), quantity and grid. Wherever possible closed questions should be pre-coded on your questionnaire to facilitate analysis. The order and flow of questions in the questionnaire should be logical to the respondent. This can be assisted by filter questions and linking phrases. The ques- tionnaire should be laid out so that it is easy to read and the responses are easy to fill in. Questionnaires must be introduced carefully to the respondent to ensure a high response rate. For self-administered questionnaires this should take the form of a covering letter; for interviewer-administered questions it will be done by the interviewer. All questionnaires should be pilot tested prior to collecting data to assess the validity and likely reliabil- ity of the questions. Administration of questionnaires needs to be appropriate to the type of questionnaire. 6. Virtually all research will involve some numerical data or contain data that could usefully be quantified to help you answer your research question(s). Quantitative data refers to all such data and can be a product of all research strategies. To be useful these data need to be analysed and interpreted. Data for quantitative analysis can be collected and subsequently coded at different levels of numerical measure- ment. The data type (precision of measurement) will constrain the data presentation, summary and analysis techniques you can use. Data are entered for computer analysis as a data matrix in which each column usually represents a variable and each row a case. Your first variable should be a unique identifier to facilitate error checking. All data should, with few exceptions, be recorded using numerical codes to facilitate analyses. Where possible you should use existing coding schemes to enable comparisons. For primary data you should include pre-set codes on the data collection form to minimise coding after collection. For variables where responses are not known you will need to develop a codebook after data have been collected for the first 50 to 100 cases. You should enter codes for all data values including missing data. The data matrix must be checked for errors. Your initial analysis should explore data using both tables and diagrams. Your choice of table or diagram will be influenced by your research question(s) and objectives, the aspects of the data you wish to emphasise, and the level of measurement at which the data were recorded. This may involve using: − tables to show specific values; − bar charts, multiple bar charts and histograms to show highest and lowest values; − line graphs to show trends; − pie charts and percentage component bar charts to show proportions; − box plots to show distributions; − scatter graphs to show relationships between variables. Subsequent analyses will involve describing your data and exploring relationships using statistics, such as: − the mean, median and mode to describe the central tendency; − the inter-quartile range and the standard deviation to describe the dispersion; − chi square to test whether two variables are significantly associated; − Kolmogorov-Smirnov to test whether the values differ significantly from a specified population; − correlation and regression to assess the strength of relationships between variables; − regression analysis to predict values. 7. Qualitative data are based on meanings expressed through words. They result in the collection of non- standardised data that require classification and are analysed through the use of conceptualisation. The process of qualitative analysis generally involves the development of data categories, allocating units of your original data to appropriate categories, recognising relationships within and between categories of data, and developing and testing hypotheses to produce well-grounded conclusions. There are a number of aids that you might use to help you through the process of qualitative analysis, including in- terview, observation, document and interim summaries, self-memos and maintaining a researcher's diary. Different qualitative analytical strategies can be identified, related to using either a deductively based or an induc- tively based approach to research. The use of these different strategies has implications for the procedures involved in the analysis of qualitative data. Quantifying some categories of qualitative data may help you to analyse this. The use of computer-assisted qualitative data analysis software can help you to perform four basic and useful func- tions during qualitative analysis, related to project management, coding and retrieval, data management, and hypothesis building and theorizing. жүктеу/скачать 0.69 Mb. Достарыңызбен бөлісу:
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