Conference App to support the Division of Occupational Psychology Annual Conference 2014






Comments

Post a Comment

Popular posts from this blog

Rosenberg self-esteem scale: SPSS Script

The Rosenberg self-esteem scale is a psychological inventory based on a 4-point likert scale and consists of 10 questions. It is used extensively to measure self-esteem across the social sciences. Below is a short script for SPSS which will help speed up the coding process.  All items should be labelled as separate numeric variables as R1, R2...etc The script computes and prints the results for all reverse-scored items and then calculates the total score.  *Part 1 - reverse scoring of specific items COMPUTE R3 = 5 - Q3. EXECUTE. COMPUTE R5 = 5 - Q5. EXECUTE. COMPUTE R8 = 5 - Q8. EXECUTE. COMPUTE R9 = 5 - Q9. EXECUTE. COMPUTE R10 = 5 - Q10. EXECUTE. *Part 2 - total score COMPUTE Rosenberg = Q1+Q2+R3+Q4+R5+Q6+Q7+R8+R9+R10. EXECUTE. *Reliability  RELIABILITY   /VARIABLES=Q1 Q2 R3 Q4 R5 Q6 Q7 R8 R9 R10   /SCALE('ALL VARIABLES') ALL   /MODEL=ALPHA.
Read more

The Hexaco Personality Inventory - SPSS Script

I am currently using the 60-item version of the Hexaco-PI-R personality inventory and decided to write a short script for SPSS to help speed up the coding process. I have posted it below because I couldn't find anyone else who had posted one online. All items should be labeled as separate numeric variables as hexaco1, hexaco2, hexaco3 ...etc The script computes and prints the results for all reverse scored items and then calculates and prints Factor/ Facet scores. It will also produce Cronbach's Alpha coefficients for each factor. The original scoring key for the HEXACO-PI-R can be found here . ******************************************* *Part 1 - reverse scoring of specific items *Honesty-Humility COMPUTE rhexaco30 = 6 - hexaco30. EXECUTE. COMPUTE rhexaco12 = 6 - hexaco12. EXECUTE. COMPUTE rhexaco60 = 6 - hexaco60. EXECUTE. COMPUTE rhexaco42 = 6 - hexaco42. EXECUTE. COMPUTE rhexaco24 = 6 - hexaco24. EXECUTE. COMPUTE rhexaco48 = 6 - hexaco48. EXECU...
Read more

Kernel Density Plots: Has the histogram had its day?

Image
Simple statistical concepts include the mean, median, standard deviation, and percentiles. These are useful for summarising data. Except these summary statistics are only useful under certain circumstances. When basic assumptions are not met, then any conclusions based on simple summary statistics are likely to be inaccurate. Unable to give a hint as to what is wrong, the numbers can often look perfectly reasonable. Lets consider a sample of 64 reaction time observations (in milliseconds): Mean = 387ms Median = 340ms These look ok, until you view the distribution, which is not unimodal.  Despite being a staple in data visualisation, histograms can often be a poor method for determining the shape of a given distribution because they are strongly affected by the number of bins used. For example, visualising the same data with only four bins can make the same observations appear normally distributed. Similarly, a box-plot can also hide data irregulari...
Read more