You are designing a study to look at the emotions that people experience when looking at different types of pictures (measured using self-report) and how this relates to brain activity (measured using fMRI). What are the most appropriate ways to solve the following problems: i) Some of the images you want to show are designed to upset people and include graphic materials. ii) fMRI datasets are very large and therefore analysis is prone to multiple comparison problems. iii) Previous studies give conflicting results iv) In general, there has been a large problem in psychology where results are not able to be replicated by subsequent studies; because of this, people are often skeptical of psychology study results. 1. Apply for ethics approval with a detailed outline of how you will make sure you have informed consent from participants, as well as debriefing after the experiment. ii. Apply Bonferroni adjustments to every possible comparison. iii. Use a Bayesian framework to start with a flat prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. Pre-register your hypotheses and analyses prior to collecting data, and when you are finished make your data and code available. i. Only select participants who have shown to have a high tolerance for graphic imagery. ii. Designate ahead of time which comparisons you are going to make based on your specific hypotheses. iii. Use a Bayesian framework to start with a flat prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. This problem does not apply to you because you are using quantitative, not qualitative data. i. Apply for ethics approval with a detailed outline of how you will make sure you have informed consent from participants, as well as debriefing them after the experiment. ii. Designate ahead of time which comparisons you are going to make based on your specific hypotheses. iii. Use a Bayesian framework to analyse the existing studies and calculate your prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. Pre-register your hypotheses and analyses prior to collecting data,and when you are finished make your data and code available. i. Apply for ethics approval with a detailed outline of how you will make sure you have informed consent from participants, as well as debriefing them after the experiment. ii. Apply Bonferroni adjustments to every possible comparison. iii. Use a Bayesian framework to analyse the existing studies and determine your prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. This problem does not apply to you because you are using quantitative, not qualitative data. You are designing a study to look at the emotions that people experience when looking at different types of pictures (measured using self-report) and how this relates to brain activity (measured using fMRI). What are the most appropriate ways to solve the following problems: i) Some of the images you want to show are designed to upset people and include graphic materials. ii) fMRI datasets are very large and therefore analysis is prone to multiple comparison problems. iii) Previous studies give conflicting results iv) In general, there has been a large problem in psychology where results are not able to be replicated by subsequent studies; because of this, people are often skeptical of psychology study results. 1. Apply for ethics approval with a detailed outline of how you will make sure you have informed consent from participants, as well as debriefing after the experiment. ii. Apply Bonferroni adjustments to every possible comparison. iii. Use a Bayesian framework to start with a flat prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. Pre-register your hypotheses and analyses prior to collecting data, and when you are finished make your data and code available. i. Only select participants who have shown to have a high tolerance for graphic imagery. ii. Designate ahead of time which comparisons you are going to make based on your specific hypotheses. iii. Use a Bayesian framework to start with a flat prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. This problem does not apply to you because you are using quantitative, not qualitative data. i. Apply for ethics approval with a detailed outline of how you will make sure you have informed consent from participants, as well as debriefing them after the experiment. ii. Designate ahead of time which comparisons you are going to make based on your specific hypotheses. iii. Use a Bayesian framework to analyse the existing studies and calculate your prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. Pre-register your hypotheses and analyses prior to collecting data,and when you are finished make your data and code available. i. Apply for ethics approval with a detailed outline of how you will make sure you have informed consent from participants, as well as debriefing them after the experiment. ii. Apply Bonferroni adjustments to every possible comparison. iii. Use a Bayesian framework to analyse the existing studies and determine your prior, then calculate Bayes Factors to compare the strength of different hypotheses after collecting your data. iv. This problem does not apply to you because you are using quantitative, not qualitative data
