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.
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The State-Trait Anxiety Inventory (STAI) is a psychological inventory based on a 4-point Likert scale and consists of 40 questions on a self-report basis. Below is a short script for SPSS which will help speed up the coding process. A version that runs in R is also in the pipeline. All items should be labeled as separate numeric variables as stai1 and taiy1 ...etc The script computes and prints the results for all reverse scored items and then calculates and prints state and trait scores. It will also produce Cronbach's Alpha coefficients. The original scoring key for the STAI can be found here . *Part 1 - reverse scoring of specific items COMPUTE stai1r = 5 - stai1. EXECUTE. COMPUTE stai2r = 5 - stai2. EXECUTE. COMPUTE stai5r = 5 - stai5. EXECUTE. COMPUTE stai8r = 5 - stai8. EXECUTE. COMPUTE stai10r = 5 - stai10. EXECUTE. COMPUTE stai11r = 5 - stai11. EXECUTE. COMPUTE stai15r = 5 - stai15. EXECUTE. COMPUTE stai16r
Generating decent tables in R is something I have struggled with for some time, particularly when these need to follow APA guidelines . SPSS has proved to be a complete nightmare so in the past I've simply built templates in Word and copied the numbers across manually from the R console. This is both time-consuming and increases the chances of human error. Fortunately, David Stanley has written a great library that can quickly generate results and place them in APA tables. One note of caution - your data frame must be complete (i.e. no missing values) and only include variables you want to appear in the table. Subsetting your main data frame beforehand may be required in the first instance. Otherwise, it is plain sailing: For example, a data-set with six personality factors calculated from the HEXACO personality inventory across several hundred participants might look like this... To generate an APA correlation table run the following: ##load library l ibr