Data Analysis

 

 

 

 

 

Results section Plan:

Objectives:

• In the beginning I will start by organizing and choosing which data file matches the chosen control strategy to convert it to SPSS and try to understand the scores.
• Decide on type of analyses.
• Then I will start by plotting the mean scores for each of the 4 conditions.
• Thirdly I will look at which conditions have the highest similarity ratings, and which have the lowest.
• I will a conduct a repeated measures ANOVA.
• Then I will look at mean scores to understand the effect and see which relevant IV has a higher score and if there is an interaction.
• If necessary and I found out that there is a significant interaction, after that I plan to use paired t-tests, follow up tests, Bonferroni correction to the alpha level.
• Calculate a difference measure for each to get a measure of change in specificity/valence from before to after.
• Subtract time 1 scores from time 2 scores = differences
• analyse those difference scores in a 2×2 ANOVA
• Look at the output to understand the scores
• Gather my tables and results in the SPSS output file.
• Prepare to write up and present the results section including and reporting the (mean, standard deviation, p value etc.)
• Relating and testing my results to my hypothesis.
• ‘If there are notable effects in the application of the memory control strategy, then the participants with different memories should depict distinct changes on memory content, emotions and feelings.

 

Notes from meeting and points to follow:

we have a 2×2 factorial design with two IVs/factors (Think/No-Think manipulation x Morally Right/Wrong memory type)

• looking at the similarity ratings as the main DV
• because they can capture any changes in memories that might occur as a result of the TNT manipulation, or the memory type, or the interaction between these two factors.
• start by plotting the mean scores for each of these 4 conditions

 

• then look at which conditions have the highest similarity ratings, which have the lowest
• we need to conduct inferential statistics to test if the main effects of the two factors and their interaction are significant.

• Conduct the ANOVA,( with descriptive statistics like the pdf example) you need to look at the mean scores to understand the effect – if there are significant main effects.
• which level of the relevant IV has a higher score?

• If there is an interaction, how do the scores seem to differ depending on the combination of the two factors?

• If (and only if) there is a significant interaction, you need to follow up with t-tests

• use paired t-tests to do follow up tests, and apply your own Bonferroni correction to the alpha level based on the number of follow up tests you are conducting

 

 

 

 

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