The second example analyzes reviewing behavior of individual reviewers by aggregating reviewer file and then adding in reviewer characteristics and author information from sociologist file and aggregated manuscript information.
Unit of Analysis: Reviewers with Summary Information on All Reviews Prepared
* Begin with review file
FILE='Location and name of manuscript file'.
/* initialize outcome variables to 0 and set to 1 for each instance of an outcome
do repeat outcome = Accept CondAccept NA RandR Reject .
compute outcome = 0.
if (recommend eq 'Accept' ) accept = 1.
if (recommend eq 'Cond Accept' ) condaccept = 1.
if (recommend eq 'N/A' ) NA = 1.
if (recommend eq 'R & R') RandR = 1.
if (recommend eq 'Reject') reject = 1.
frequencies variables = Accept CondAccept NA RandR Reject .
/* aggregate command yields total N of reviews as well as of each outcome
DATASET DECLARE AGG_Reviews.
SORT CASES BY PersonID.
/* calculate percents for each outcome
compute perAccept = Accept_sum / N_of_reviews.
compute perCondAccept = CondAccept_sum / N_of_reviews.
compute perNA = NA_sum / N_of_reviews.
compute perRandR = RandR_sum / N_of_reviews.
compute perReject = Reject_sum / N_of_reviews.
descriptives variables = perAccept perCondAccept perNA perRandR perReject N_of_reviews.
/* match in individual level characteristics for each reviewer
MATCH FILES /FILE= *
/TABLE= 'Location and name of manuscript file'.
/BY personID .
/* transform alphanumberic gender variable into nominal numeric
compute gender_num =3.
if (gender eq 'male') gender_num = 1.
if (gender eq 'female') gender_num = 2.
value labels gender_num 1 'Male' 2 'Female' 3 'Other'.
frequencies variables = gender_num gender.
/* temporarily select only men and women and run t-test - men do more reviews and accept a greater percent
select if (gender_num le 2).
T-TEST GROUPS=gender_num(1 2)
/VARIABLES=N_of_reviews perAccept, perCondAccept, perRandR, perReject
From this example, in Tables 4 and 5, we can see 62.1% of all those who reviewed for ASR during this time period were male, but also that on average male reviews did significantly more reviews (5.23) than female reviewers (4.26). However, there was little difference in the decisions they made: the percentage acceptance rate was significantly higher for men than women (p=.05), but there was no significant difference according to gender for any other outcome.
There are more older reviewers than younger reviewers and the older viewers were significantly more likely to recommend accepting a manuscript and significantly less likely to outright reject a manuscript than younger reviewers. (Table 6)