UNIVERSITY OF THE FREE STATE DEPARTMENT OF COMPUTER SCIENCE AND INFORMATICS CSIS6813 MODULE TEST 2
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1 UNIVERSITY OF THE FREE STATE DEPARTMENT OF COMPUTER SCIENCE AND INFORMATICS CSIS6813 MODULE TEST 2 DATE: 3 May 2017 MARKS: 75 ASSESSOR: Prof PJ Blignaut MODERATOR: Prof C de Villiers (UP) TIME: 2 hours 1. Sampling and data collection (25) 1.1 Distinguish between the theoretical and accessible population from which a data sample can be extracted. (2) 1.2 What is a cluster sample? (3) 1.3 Distinguish between discrete and continuous quantitative data. Give an example of each. (4) 1.4 Discuss three types of scales that can be used in questionnaires. Provide examples to clarify if necessary. (9) 1.5 What is a structured interview? (3) 1.6 Distinguish between internal and external validity of an experiment. What are the advantages and disadvantages of a laboratory experiment in this regard? (4) 2. Data analysis (50) 2.1 Provide some guidelines on the formulation and presentation of analysis and evidence in the body of a research report. (10) 2.2 Variables can be measured at four levels, namely nominal, ordinal, ratio and interval. Discuss and provide examples of each. (8) 2.3 In hypothesis testing, what is a Type I error? (3) 2.4 In statistics, what is a spurious relationship? Give an example. (5) 2.6 When will each of the following statistical tests be used: t-test, Anova, Pearson correlation, Regression, Chi-square. Refer to normality of the data as well as the number and nature of (independent variables) and dependent variables. Complete the following table. (20 ½) Test Normality Maximum number of Maximum levels per factor t-test Anova Theoretical: Theoretical: Practical: Practical: Pearson n/a n/a n/a Regression Theoretical: Practical: n/a Chi-square Theoretical: Practical: dependent variables 1
2 2.7 An experiment was done to determine whether male students drink more alcohol than female students. A survey with the single question Do you use alcohol more than once per week? was distributed on campus. The results were summarised in a contingency table as follows: Yes No Male students Female students A chi-square value of 4.72 was obtained with p= Formulate a conclusion based on these results. (4) 2.8 Consider the following scenario: A group of 30 users are given a set of tasks to execute during a usability study to evaluate two interface designs: an established design (A) and a proposed new design (B). The correctness with which the tasks are executed is measured as a percentage Discuss the problems that can arise if the same group of users evaluate both interfaces. Suggest ways to eliminate these problems or minimise their effects. (4) What statistical test will be appropriate to determine whether one interface is significantly better than the other if the same group of users evaluate both interfaces? (2) Formulate a one-tailed null-hypothesis for this experiment. (2) Suppose the test referred to in was applied and the following results obtained. Formulate a conclusion based on these results. Average A = 62.3 Average B = n = 30 df = 29 t = p (one-tailed) = α = (2) 2
3 MEMORANDUM 1.1 Theoretical population: Group to be generalized to Accessible population: Group from which the sample is drawn. Subset of theoretical population. 1.2 Divide the population into clusters, usually along geographic boundaries, and then randomly sampling the clusters. All units within each sampled cluster must be measured. 1.3 Discrete quantitative data is obtained by means of a counting process (integer numbers). Example: Number of people smoking in a sample. Continuous quantitative data is obtained by means of a measuring process (real numbers). Example: Age of people in a sample. 1.4 Likert scale Requires the respondent to indicate their opinion on a rating scale where the two ends of the scale are opposites of one another. For example, the scale may range from strongly disagree to strongly agree. LPC scale (also known as a semantic differential (SD) scale) - Similar to a Likert scale except that the respondents have to choose from a scale anchored on either side by explicit adjectives. Example: Was your experience with the system: Frustrating Satisfying Guttman scale The respondent to check each statement with which they agree. Usually the items are cumulatively structured, which means that you would not only agree to the last item. If you agree with the last item you would generally agree to all the items above it. Example Please check each statement that you agree with: - Are you willing to permit immigrants to live in your country? - Are you willing to permit immigrants to live in your community? - Are you willing to permit immigrants to live in your neighbourhood? - Are you willing to have an immigrant live next door to you? - Would you let your child marry an immigrant? 1.5 The structured interview consists of list questions that must be answered using a predefined set of responses. - No individual opinions are sought, but rather the collective feeling of users towards the product is established. - Leaves no room for exploration of the opinion and feelings of the respondent. 1.6 Internal validity is an indication that what was done was the cause of what was observed. External validity is the degree to which the conclusions of the study can be generalised to other people at other times. By setting up a controlled experiment in a laboratory you maximise internal validity but at the expense of the external validity. 3
4 2.1 Open discussion (max 10) You need to convince readers - The ability to make your point through reasoning and evidence - Every hypothesis must be supported by argument and tested by facts Types of evidence - Expert opinions, interviews, case studies, mathematical equations, financial statements, statistics from surveys, etc. Usable evidence: Reliable, Enough, Applicable, Current, Used appropriately Avoid using a moral stance as basis for argument Don t hide evidence: Provide evidence for and against Avoid controversy Check your evidence - Must be related - Check the weight / importance - All sources must be referenced Engage others - You may agree or disagree, borrow or modify but never without substantiation - Stay impersonal (play the ball, not the man) - Stick to the point - Be fair don t distort other s words - If you don t understand the details of an argument, don t criticise it - Always acknowledge 2.2 Nominal measurements simply name the attribute and there is no particular significance associated with the numbers that are used. The numbers simply serve to label or categorise the variable and as such they are not weighted. - Example: The numbers on the jerseys of basketball players. A player with the number of 15 is not any more valuable or competent than a player with the number of 30. Ordinal scales refer to rank-ordered observations and the values used have weight. The interval between consecutive values of the ordinal measurement have no associated meaning, but when using a ratio or an interval measurement the distance between the attributes has a meaning. Example: Options on a demographics question in a questionnaire. Ratio and interval scales have equal distance between units. - Ratio measurements are those where there is a meaningful absolute zero, such as with weight or temperature. - A temperature is also an interval measurement since the difference between 30 C and 40 C is the same as the difference between 70 C and 80 C. 2.3 A type I error is the probability of wrongly rejecting the null-hypothesis. 2.4 In a spurious relationship two or more events or variables are not causally related to each other (i.e. they are independent), yet it may be wrongly inferred that they are, due to either coincidence or the presence of a certain third, unseen factor (a.k.a. "confounding factor"). Example: Many deaths in February correlates with many ice creams sold in February. Conclusion: Ice cream kills people. Confounding factor: Heatwave in February 4
5 ½ = 10 Test Normality Maximum Maximum number of levels per dependent factor variables t-test Yes 1 2 Categorical Continuous Anova Yes Theoretical: Theoretical: Practical: 3-5 Practical: 3-5 Categorical Continuous Pearson No n/a n/a n/a Continuous Yes if the model Regression equation is unknown. Theoretical: No for determining Practical: 5 coefficients of specific n/a Continuous Continuous equation. Chi-square No 1 Theoretical: Practical: 3-5 Categorical Categorical 2.7 The null-hypothesis that there is no difference between the drinking habits of male and female students can be rejected on 95% level of significance. In other words the difference in drinking habits of male of female students is significant on the 95% level of significance Users can learn from their experience with the first system and therefore results for the second system may be invalid. (i) Use different user groups but with similar profiles to test the two interfaces. (ii) Mix users so that some test interface A first and others interface B Paired t-test for means or Repeated measures Anova Users do not perform significantly better with interface B than with interface A Average B is significantly higher than average A. Therefore, users perform significantly better with interface B than with interface A. 5
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