Sumber : http://www.usq.edu.au/library/help/postgrad/resmeth.htm http://www.tardis.ed.ac.uk/~kate/qmcweb/q2.htm Dealing with Problem Questions Hypothetical questions A hypothetical question is one in which you are asking respondents to indicate what they think they would do under particular imaginary circumstances. These can't always be avoided in some attitudinal research, but they are difficult to administer and often give rise to unreliable answers Presuming / leading questions These are often included in poor questionnaires because the researcher feels strongly about a topic and assumes that everyone will be of the same opinion. Questions which rely on memory Problems which tax the respondent's memory too much are likely to lead to non-response or inaccurate replies. For example "What did you have for lunch each day last week?" Questions requiring prior knowledge For example, "What is your National Insurance number?" Sensitive questions Personal details / health / age Income If you have to ask sensitive questions, the problem can be alleviated somewhat by the use of SHOW CARDS. Put all of the possible responses on a card, preferably mixed up, and ask the respondent to indicate which number relates to their own circumstances. For example, Can you tell me the number on this card which corresponds to you income group? SHOW CARD WITH…… 1. 2. 3. 4. 5. 6. 7. £7,000 - 12,000 Over £60,000 £18,000 - £30,000 Under £7,000 £40,000 - £60,000 £12,000 - £18,000 £30,000 - £40,000 Mutually exclusive responses In the show card above, you will note that somebody earning exactly £30,000 would perhaps wonder whether to give answer 3 or answer 7 on the show card. In practice, people are usually able to give their income as an approximation. You should, however, always watch out for questions where the multiple choice answers are not mutually exclusive and where a respondent will be uncertain about which category he/she falls under. It seems to be a particular problem with age brackets, and you can often see examples of mistakes here in even professionally produced surveys. Long questions If your questions are too long and detailed, the respondent may get lost and the responses will relate only to the beginning or the end of the question. Where definitions and qualifications are necessary, use show cards. Analysis of Survey Data Influenced by: 1. The number of variables being examined Univariate analysis = one variable (e.g. gender, age) Methods: charts (e.g. bar chart, pie chart) or frequency table Bivariate = 2 variables (e.g. gender + purchase of computers) Methods: crosstabulations; scatterplots; regression; comparison of means (but we will only be covering the first two in this module) Multivariate = 3 or more variables (e.g. income influenced by both education level and gender) - Note: you'll be relieved to hear that you won't be covering any of these as part of this module) 2. Levels of measurement Nominal Ordinal Interval Ratio 3. Descriptive or inferential? Descriptive statistics: summarise patterns in responses (e.g. average age of respondents or number of respondents who buy a product) Inferential statistics: provide an idea about whether the patterns described in the sample are likely to apply in the population from which the sample is drawn ("tests of significance") Frequency tables Should have separate columns for: (a) number of people who gave each answer (N) (b) percentage column If (a) is excluded from the table, it is essential to include the total number of cases on whom percentages are calculated. Example Analysis of degree of agreement/disagreement with statement "Taking part in videoconferenced classes is a really exciting experience" Answer N % Strongly agree Agree Neutral Disagree Strongly Disagree Total 254 201 119 97 75 746 34 27 16 13 10 100 Crosstabulation Crosstabulation = tabular representation of relationship between two (or can be more than two) variables Also called CONTINGENCY TABLE Table divided into cells with each cell representing the coincidence of a specific value from each variable The total for each row and each column is given at the end of the row and column. These totals are called the ROW MARGINALS and COLUMN MARGINALS. It is normal (but not absolutely essential) for the dependent variable to appear in rows. This is because we may be crosstabulating one dependent variable by a series of possible explanatory variables. Hypotheses & Variables A hypothesis can be described as "a tentative answer to a research question" or a "provisional prediction". The plural is hypotheses. Hypotheses should be: Stated clearly, using appropriate terminology Testable Statement of relationships between variables Limited in scope Examples of hypotheses: Health education programmes influence the number of people who smoke Newspapers affect people's voting patterns Attendance at lectures influences exam marks Diet influences intelligence In the above examples, "something" (e.g. diet, lecture attendance) affects "something else" (e.g. intelligence, exam marks). These are variables. A variable is anything which is free to vary, and in order to describe them quantitatively, they have to be expressed in appropriate units (e.g. IQ scores, exam percentages) The pairs of variables in the above examples have separate names. The variable we manipulate is called the independent variable (IV). The variable we are hypothesising will alter as a result of our manipulations is called the dependent variable (DV). The dependent variable alters as a consequence of the value of the independent variable - its value is dependent on this. The value of the independent variable is free to vary according to the whims of the experimenters. Independent variable Health education programmes Newspaper Attendance at lectures Diet Dependent variable Number of people who smoke Voting patterns Exam marks Intelligence NB Many variables can be either dependent or independent, within the context of a particular study. For example, it could be argued that "Intelligence influences diet" or "Exam marks influence attendance at lectures". Directional hypotheses In the examples above, words like "influence" or "affects" are used without indicating direction. When a hypothesis states a predicted outcome (using words such as reduce, increase, lower, raise - it is called a directional or one-tailed hypothesis. Vaguer types of hypotheses (such as the ones given earlier) are known as non-directional or twotailed hypotheses.