Quantitative Methods Assessed Coursework: Answer both sections: The file Errors2019. sav contains scores of 76subjects. The subjects were divided into two groups (each of 38 subjects) based on their performance on an anxiety pretest. Group 1 was the low- anxious group while group 2 was the high anxious group. All subjects provided scores on One dependent measure: errors. Perform the analyses requested below on this data. In each case report the results in full using APA conventions. 1. Use SPSS or similar) to calculate the descriptive statistics (including mean, standard deviation, skew and kurtosis) for the errors variable for each group. Report the descriptive statistics in a table. Comment on the pattern you see across groups. 2. Use SPSS (or similar) to draw frequency histograms for the errors variables for both the low anxious group and the high anxious group (i.e., two histograms). Comment on the distribution. 3. Assume the hypothesis is that those in the low anxiety group (anxiety = 1) will make fewer errors than those in the high anxiety group (anxiety = 2). Perform an appropriate parametric statistical test to test this hypothesis and report the results in standard form. 4. Comment on the distribution of errors for each of the low and high anxiety groups. Will you be needing to transform the data? What are your options? And what would you expect from the transformation how will it affect the data? 5. Carry out an appropriate statistical test on the transformed data and report the results. Compare the results with those of question 3 and explain any similarities or differences. 6. Section B : Recent research using electrophysiological measures has shown that depression is associated with difficulty in filtering out irrelevant information from working memory (Owens, Koster & Derakshan, 2012, 2013). It has been suggested that the process of rumination (a tendency to ruminate and get stuck in thinking about negative information) may play an important role in explaining this effect (Koster, Lissnyder, Derakshan & De Raedt, 2011). You examine the relationship between rumination and distraction from task irrelevant information. In a visual search task (see Figure 1: an example of a trial, below) participants are shown a distractor (O) and a target (X) and are asked to look at the target as quickly as possible without looking at the distractor. You measure the deviation of the eye-movement towards the distractor. Research has shown that people who are good at inhibiting distracting information are more likely to look at the target and have smaller deviation towards the distractor. You are interested in examining the relationship between levels of rumination (asassessed by a rumination scale with larger scores reflecting greater rumination levels) and deviation of eye- movement toward the distractor. For ease of 2. interpretation deviation of eye-movements toward the distractor is reflected as the time taken to make an eye-movement towards the target (in msec), with longer RTs reflecting greater capture by distractor and bigger deviation to distractor). Figure 1. Example of a trial Open the datafile: rumination.sav. 1. Draw a scatter plotto reflect the relationship between rumination and RT to target. What pattern of relationship do you see? How do rumination and RT to target correlate? Is this correlation significant? Comment. 2. Evidence shows (see Koster et al., 2017) that rumination is acentral characteristic of depression. So, in your experiment you also measure depression levels using a questionnaire. How does the relationship between rumination and RT to target change after controlling for depression levels? What has this got to do if anything with the relationship between depression and RT?] 3. Perform a regression analysis to assess the predictive utility of rumination in explaining RT to target. How good a predictor is rumination of eye-movement to target? 4. Does the regression model have good external validity? Comment and elaborate on the pattern of residuals. Are there any cases that should concern you? What can you learn from the values of the standardised residuals and unstandardized predicted values of the model? Are there any odd cases? Comment on whether the model is under-or over-estimating the data. 5 If someone in your sample had a rumination score of 45, according to your model, how long will it take him/her to saccade toward target? NB: please write individual report how you come to it. And check the graph to work with this questions in attachment thanks.
Hide
