def main():
#(MANOVA?) GLM general linear model with within subject comparisons of interest, gender, diff
# VARIABLES
nameList = ['pp', 'gp','tb','bt', 't1', 'tl', 'ni','dn', 'cv','td','pe','kn','uv', 'rp','ya','na']
labelList= ['point2point', 'gist2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing', 'novelinfo', 'datesnumnames', 'concurrentverb', 'duringdistractor', 'personalexp', 'generalknowledge', 'visualizations', 'repitition','mainidea', 'keytopicword']
proquestions = ['1','2','3']
suffix = ['im','ii','bm','bi']
stratdiff = "s"
promain= "p"
mathread = "r"
GLMtext = GLMfunctions.makeGLMtext()
f.write("DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n"
for i in range(0,2):
## SELECT HIGH INTEREST ##
filtersign = ["~=","="]
if mathread == "m":
spint = "mathspint"
elif mathread == "r":
spint = "readspint"
filterText = """USE ALL. \nCOMPUTE filter_$=("""+spint+filtersign[i]+ "2"+"""). \nVARIABLE LABELS filter_$ '""" + spint+filtersign[i]+ "2" +"""(FILTER)'. \nVALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. \nFORMATS filter_$ (f1.0). \nFILTER BY filter_$. \nEXECUTE.\n\n"""
f.write(filterText)
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix,proquestions,promain,mathread,stratdiff,nameListi)
computelines = GLMfunctions.makeComputeLines(suffix,proquestions,promain,mathread,stratdiff,nameListi,labelListi)
s = recodeline+computelines
f.write(s)
# GLM
text = "GLM " + labelListi+"im "+ labelListi+"ii "+ labelListi+"bm "+ labelListi+"bi "+"BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
readstrat1 = ["point2point", "top2bottom"]
GLMfunctions.printCompute(readstrat1,"readstrat1",suffix,GLMtext,f)
recall = ['point2point', 'gist2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing']
encoding = ['novelinfo', 'datesnumnames','personalexp','generalknowledge','visualizations','mainidea','keytopicword']
compmonitor = ['concurrentverb','duringdistractor','repitition']
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = ['duringdistractor', 'repitition']
elaboration = ['novelinfo','datesnumnames','personalexp','generalknowledge','visualizations']
organizational = ['point2point', 'gist2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing','mainidea','keytopicword']
compmonitor2 = ['concurrentverb']
affectivemot = []
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = ['repitition']
elaborationb = ['novelinfo','datesnumnames','personalexp','generalknowledge','visualizations']
organizationalb = ['point2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing']
rehearsalc = ['duringdistractor']
elaborationc = []
organizationalc = ['gist2point','mainidea','keytopicword']
selection = ['novelinfo','datesnumnames']
acquisition = []
construction = ['visualizations','mainidea','keytopicword']
integration = ['personalexp','generalknowledge']
allgroups = [recall,encoding,compmonitor,rehearsal, elaboration, organizational, compmonitor2, affectivemot, rehearsalb, elaborationb, organizationalb, rehearsalc, elaborationc, organizationalc, selection, acquisition, construction, integration]
allgroupsname = ['recall','encoding','compmonitor','rehearsal', 'elaboration', 'organizational', 'compmonitor2', 'affectivemot', 'rehearsalb', 'elaborationb', 'organizationalb', 'rehearsalc', 'elaborationc', 'organizationalc', 'selection', 'acquisition', 'construction', 'integration']
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] +" = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i]+suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" "+ allgroupsname[k]+suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")
## REMOVE FILTER ##
f.write("""FILTER OFF.\nUSE ALL.\nEXECUTE.\n\n""")
main()
def main():
# (MANOVA?) GLM general linear model with within subject comparisons of interest, gender, diff
f = open('manova-promathstrat.txt', 'w+')
# VARIABLES
nameList = [
'pa', 'ss', 'ky', 'kc', 'kn', 'nm', 'el', 'un', 'es', 're', 'rc', 'rn',
'sp', 'vi', 'sk', 'pe', 'pk', 'r1', 'gu'
]
labelList = [
'parts', 'sentence', 'keywords', 'keywordscontext', 'keywordsnumbers',
'numbers', 'eliminatekeys', 'usesallnum', 'estimate', 'reasonable',
'rereadscomp', 'rereadsnum', 'paraphrase', 'visualize', 'sketch',
'personalexp', 'priorknow', 'readthrough1st', 'guess'
]
proquestions = ['1', '2', '3']
suffix = ['im', 'ii', 'bm', 'bi']
stratdiff = "s"
promain = "p"
mathread = "m"
GLMtext = GLMfunctions.makeGLMtext()
f.write("DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n")
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix, proquestions, promain,
mathread, stratdiff,
nameListi)
computelines = GLMfunctions.makeComputeLines(suffix, proquestions,
promain, mathread,
stratdiff, nameListi,
labelListi)
s = recodeline + computelines
f.write(s)
# GLM
text = "GLM " + labelListi + "im " + labelListi + "ii " + labelListi + "bm " + labelListi + "bi " + "BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
mathstrat1 = ["sentence", "usesallnum"]
mathstrat3 = ["paraphrase", "visualize"]
mathstrat5 = ["eliminateskeys", "rereadsnum"]
GLMfunctions.printCompute(mathstrat1, "mathstrat1", suffix, GLMtext, f)
GLMfunctions.printCompute(mathstrat3, "mathstrat3", suffix, GLMtext, f)
GLMfunctions.printCompute(mathstrat5, "mathstrat5", suffix, GLMtext, f)
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = []
elaboration = [
'paraphrase', 'visualize', 'sketch', 'personalexp', 'priorknow'
]
organizational = [
'parts', 'sentence', 'keywords', 'keywordscontext', 'keywordsnumbers',
'numbers', 'eliminatekeys', 'usesallnum'
]
compmonitor2 = [
'estimate', 'reasonable', 'rereadscomp', 'rereadsnum', 'readthrough1st'
]
affectivemot = []
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = []
elaborationb = ['visualize', 'sketch']
organizationalb = ['numbers', 'eliminatekeys', 'usesallnum']
affectivemotb = []
rehearsalc = []
elaborationc = ['paraphrase', 'personalexp', 'priorknow']
organizationalc = [
'parts', 'sentence', 'keywords', 'keywordscontext', 'keywordsnumbers'
]
allgroups = [
rehearsal, elaboration, organizational, compmonitor2, affectivemot,
rehearsalb, elaborationb, organizationalb, rehearsalc, elaborationc,
organizationalc
]
allgroupsname = [
'rehearsal', 'elaboration', 'organizational', 'compmonitor2',
'affectivemot', 'rehearsalb', 'elaborationb', 'organizationalb',
'rehearsalc', 'elaborationc', 'organizationalc'
]
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] + " = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i] + suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" " + allgroupsname[k] + suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")
def main():
# (MANOVA?) GLM general linear model with within subject comparisons of interest, gender, diff
# VARIABLES
nameList = [
'su', 'sp', 'cd', 'cp', 'ck', 'cv', 'ex', 'td', 'lk', 'ua', 'qu', 'pa',
'na', 'ct'
]
labelList = [
'setup', 'partialsetup', 'comprehension', 'comprehensionparts',
'keywordcomprehension', 'vocabcomprehension', 'extramissinginfo',
'toodetailed', 'lackknowledge', 'unreasonable', 'questiondifficulty',
'positiveaffect', 'negativeaffect', 'concentration'
]
proquestions = ['1', '2', '3']
suffix = ['im', 'ii', 'bm', 'bi']
stratdiff = "d"
promain = "p"
mathread = "m"
GLMtext = GLMfunctions.makeGLMtext()
for i in range(1, 3):
## SELECT HIGH INTEREST ##
if mathread == "r":
high = [1, 2]
low = [4, 5]
elif mathread == "m":
high = [1, 4]
low = [2, 5]
if i == 1:
filterText = """USE ALL. \nCOMPUTE filter_$=(cell = """ + str(
high[0]
) + "| " + """cell = """ + str(
high[1]
) + """). \nVARIABLE LABELS filter_$ 'cell = """ + str(
high[0]
) + " | " + str(
high[1]
) + """(FILTER)'. \nVALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. \nFORMATS filter_$ (f1.0). \nFILTER BY filter_$. \nEXECUTE.\n\n"""
elif i == 2:
filterText = """USE ALL. \nCOMPUTE filter_$=(cell = """ + str(
low[0]
) + "| " + """cell = """ + str(
low[1]
) + """). \nVARIABLE LABELS filter_$ 'cell = """ + str(
low[0]
) + " | " + str(
low[1]
) + """(FILTER)'. \nVALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. \nFORMATS filter_$ (f1.0). \nFILTER BY filter_$. \nEXECUTE.\n\n"""
f.write(
"DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n")
f.write(filterText)
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix, proquestions,
promain, mathread,
stratdiff, nameListi)
computelines = GLMfunctions.makeComputeLines(
suffix, proquestions, promain, mathread, stratdiff, nameListi,
labelListi)
s = recodeline + computelines
f.write(s)
# GLM
text = "GLM " + labelListi + "im " + labelListi + "ii " + labelListi + "bm " + labelListi + "bi " + "BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
mathdiff1 = [
"setup", "comprehension", "extramissinginfo", "toodetailed",
"partialsetup", "negativeaffect", "questiondifficulty"
]
mathdiff2 = [
"vocabcomprehension", "lackknowledge", "comprehensionparts"
]
GLMfunctions.printCompute(mathdiff1, "mathdiff1", suffix, GLMtext, f)
GLMfunctions.printCompute(mathdiff2, "mathdiff2", suffix, GLMtext, f)
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = [
'keywordcomprehension', 'vocabcomprehension', 'questiondifficulty'
]
elaboration = [
'comprehension', 'comprehensionparts', 'lackknowledge',
'positiveaffect', 'negativeaffect'
]
organizational = ['setup', 'partialsetup', 'toodetailed']
compmonitor2 = ['extramissinginfo', 'unreasonable']
affectivemot = ['concentration']
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = [
'keywordcomprehension', 'vocabcomprehension', 'questiondifficulty'
]
elaborationb = ['lackknowledge']
organizationalb = ['setup', 'partialsetup', 'toodetailed']
affectivemotb = []
rehearsalc = []
elaborationc = [
'comprehension', 'comprehensionparts', 'positiveaffect',
'negativeaffect'
]
organizationalc = []
allgroups = [
rehearsal, elaboration, organizational, compmonitor2, affectivemot,
rehearsalb, elaborationb, organizationalb, rehearsalc,
elaborationc, organizationalc
]
allgroupsname = [
'rehearsal', 'elaboration', 'organizational', 'compmonitor2',
'affectivemot', 'rehearsalb', 'elaborationb', 'organizationalb',
'rehearsalc', 'elaborationc', 'organizationalc'
]
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] + " = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i] + suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" " + allgroupsname[k] + suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")
## REMOVE FILTER ##
f.write("""FILTER OFF.\nUSE ALL.\nEXECUTE.\n\n""")
def main():
#(MANOVA?) GLM general linear model with within subject comparisons of interest, gender, diff
f = open('manova-proreadstrat.txt','w+')
# VARIABLES
nameList = ['ptp', 'gtp','ttb','btt', 'th1', 'fin', 'nov','dnn', 'ver','tid','pex','pkn','eye', 'agn','ide','ktw']
labelList= ['point2point', 'gist2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing', 'novelinfo', 'datesnumnames', 'concurrentverb', 'duringdistractor', 'personalexp', 'generalknowledge', 'visualizations', 'repitition','mainidea', 'keytopicword']
proquestions = ['1','2','3']
suffix = ['im','ii','bm','bi']
stratdiff = "s"
promain= "p"
mathread = "r"
GLMtext = GLMfunctions.makeGLMtext()
f.write("DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n"
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix,proquestions,promain,mathread,stratdiff,nameListi)
computelines = GLMfunctions.makeComputeLines(suffix,proquestions,promain,mathread,stratdiff,nameListi,labelListi)
s = recodeline+computelines
f.write(s)
# GLM
text = "GLM " + labelListi+"im "+ labelListi+"ii "+ labelListi+"bm "+ labelListi+"bi "+"BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
readstrat1 = ["point2point", "top2bottom"]
GLMfunctions.printCompute(readstrat1,"readstrat1",suffix,GLMtext,f)
recall = ['point2point', 'gist2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing']
encoding = ['novelinfo', 'datesnumnames','personalexp','generalknowledge','visualizations','mainidea','keytopicword']
compmonitor = ['concurrentverb','duringdistractor','repitition']
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = ['duringdistractor', 'repitition']
elaboration = ['novelinfo','datesnumnames','personalexp','generalknowledge','visualizations']
organizational = ['point2point', 'gist2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing','mainidea','keytopicword']
compmonitor2 = ['concurrentverb']
affectivemot = []
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = ['repitition']
elaborationb = ['novelinfo','datesnumnames','personalexp','generalknowledge','visualizations']
organizationalb = ['point2point', 'top2bottom', 'bottom2top', 'firstthing', 'lastthing']
affectivemotb = []
rehearsalc = ['duringdistractor']
elaborationc = []
organizationalc = ['gist2point','mainidea','keytopicword']
selection = ['novelinfo','datesnumnames']
acquisition = []
construction = ['visualizations','mainidea','keytopicword']
integration = ['personalexp','generalknowledge']
allgroups = [recall,encoding,compmonitor,rehearsal, elaboration, organizational, compmonitor2, affectivemot, rehearsalb, elaborationb, organizationalb, rehearsalc, elaborationc, organizationalc, selection, acquisition, construction, integration]
allgroupsname = ['recall','encoding','compmonitor','rehearsal', 'elaboration', 'organizational', 'compmonitor2', 'affectivemot', 'rehearsalb', 'elaborationb', 'organizationalb', 'rehearsalc', 'elaborationc', 'organizationalc', 'selection', 'acquisition', 'construction', 'integration']
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] +" = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i]+suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" "+ allgroupsname[k]+suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")
main()
def main():
# (MANOVA?) GLM general linear model with within subject comparisons of interest, gender, diff
# VARIABLES
nameList = ['vo', 'gc','ac','sk', 'pp', 'kt', 'kc','de', 'cv','fp','fa','ya','na', 'nv','nc','in','nt','tg','di','nd']
labelList= ['vocab','generalcomp','factuncertainty','seperating','confuseprevious','lacktopicknow','lackcontentknow','toodetailed','cantverbalize','forgetparagraph','forgetall','positiveaffect','negativeaffect','nervous','noconcentrate','interviewerprocedure','notalki', 'toogeneral','distractor','nodifficulties']
proquestions = ['1','2','3']
suffix = ['im','ii','bm','bi']
stratdiff = "d"
promain= "p"
mathread = "r"
GLMtext = GLMfunctions.makeGLMtext()
f.write("DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n"
for i in range(0,2):
## SELECT HIGH INTEREST ##
filtersign = ["~=","="]
if mathread == "m":
spint = "mathspint"
elif mathread == "r":
spint = "readspint"
filterText = """USE ALL. \nCOMPUTE filter_$=("""+spint+filtersign[i]+ "2"+"""). \nVARIABLE LABELS filter_$ '""" + spint+filtersign[i]+ "2" +"""(FILTER)'. \nVALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. \nFORMATS filter_$ (f1.0). \nFILTER BY filter_$. \nEXECUTE.\n\n"""
f.write(filterText)
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix,proquestions,promain,mathread,stratdiff,nameListi)
computelines = GLMfunctions.makeComputeLines(suffix,proquestions,promain,mathread,stratdiff,nameListi,labelListi)
s = recodeline+computelines
f.write(s)
# GLM
text = "GLM " + labelListi+"im "+ labelListi+"ii "+ labelListi+"bm "+ labelListi+"bi "+"BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
readdiff1 = ["vocab", "generalcomp", "lackcontentknow"]
readdiff2 = ["toodetailed", "forgetall"]
readdiff3 = ["negativeaffect", "toogeneral"]
GLMfunctions.printCompute(readdiff1,"readdiff1",suffix,GLMtext,f)
GLMfunctions.printCompute(readdiff2,"readdiff2",suffix,GLMtext,f)
GLMfunctions.printCompute(readdiff3,"readdiff3",suffix,GLMtext,f)
# Recall vs encoding vs comprehension monitoring
recall = ['cantverbalize','forgetparagraph','forgetall','nervous','noconcentrate','notalki', 'toogeneral','distractor']
encoding = ['vocab','generalcomp','factuncertainty','seperating','confuseprevious','lacktopicknow','lackcontentknow','toodetailed','positiveaffect','negativeaffect']
compmonitor = []
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = ['cantverbalize','forgetparagraph','forgetall']
elaboration = ['lacktopicknow','lackcontentknow','positiveaffect','negativeaffect']
organizational = ['vocab','generalcomp','factuncertainty','seperating','confuseprevious','toodetailed','toogeneral']
compmonitor2 = ['notalki']
affectivemot = ['nervous','noconcentrate','distractor']
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = ['cantverbalize','forgetparagraph','forgetall']
elaborationb = []
organizationalb = ['vocab','generalcomp','confuseprevious','toodetailed','toogeneral']
rehearsalc = []
elaborationc = ['lacktopicknow','lackcontentknow','positiveaffect','negativeaffect']
organizationalc = ['factuncertainty','seperating']
selection = ['confuseprevious','toodetailed','positiveaffect','negativeaffect']
acquisition = ['vocab','generalcomp']
construction = ['factuncertainty']
integration = ['seperating','lacktopicknow','lackcontentknow']
allgroups = [recall,encoding,compmonitor,rehearsal, elaboration, organizational, compmonitor2, affectivemot, rehearsalb, elaborationb, organizationalb, rehearsalc, elaborationc, organizationalc, selection, acquisition, construction, integration]
allgroupsname = ['recall','encoding','compmonitor','rehearsal', 'elaboration', 'organizational', 'compmonitor2', 'affectivemot', 'rehearsalb', 'elaborationb', 'organizationalb', 'rehearsalc', 'elaborationc', 'organizationalc', 'selection', 'acquisition', 'construction', 'integration']
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] +" = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i]+suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" "+ allgroupsname[k]+suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")
## REMOVE FILTER ##
f.write("""FILTER OFF.\nUSE ALL.\nEXECUTE.\n\n""")
main()
def main():
# (MANOVA?) GLM general linear model with within subject comparisons of interest, gender, diff
f = open('manova-promathdiff.txt', 'w+')
# VARIABLES
nameList = [
'su', 'sp', 'cd', 'cp', 'ck', 'cv', 'ex', 'td', 'lk', 'ua', 'qu', 'pa',
'na', 'ct'
]
labelList = [
'setup', 'partialsetup', 'comprehension', 'comprehensionparts',
'keywordcomprehension', 'vocabcomprehension', 'extramissinginfo',
'toodetailed', 'lackknowledge', 'unreasonable', 'questiondifficulty',
'positiveaffect', 'negativeaffect', 'concentration'
]
proquestions = ['1', '2', '3']
suffix = ['im', 'ii', 'bm', 'bi']
stratdiff = "d"
promain = "p"
mathread = "m"
GLMtext = GLMfunctions.makeGLMtext()
f.write("DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n")
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix, proquestions, promain,
mathread, stratdiff,
nameListi)
computelines = GLMfunctions.makeComputeLines(suffix, proquestions,
promain, mathread,
stratdiff, nameListi,
labelListi)
s = recodeline + computelines
f.write(s)
# GLM
text = "GLM " + labelListi + "im " + labelListi + "ii " + labelListi + "bm " + labelListi + "bi " + "BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
mathdiff1 = [
"setup", "comprehension", "extramissinginfo", "toodetailed",
"partialsetup", "negativeaffect", "questiondifficulty"
]
mathdiff2 = ["vocabcomprehension", "lackknowledge", "comprehensionparts"]
GLMfunctions.printCompute(mathdiff1, "mathdiff1", suffix, GLMtext, f)
GLMfunctions.printCompute(mathdiff2, "mathdiff2", suffix, GLMtext, f)
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = [
'keywordcomprehension', 'vocabcomprehension', 'questiondifficulty'
]
elaboration = [
'comprehension', 'comprehensionparts', 'lackknowledge',
'positiveaffect', 'negativeaffect'
]
organizational = ['setup', 'partialsetup', 'toodetailed']
compmonitor2 = ['extramissinginfo', 'unreasonable']
affectivemot = ['concentration']
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = [
'keywordcomprehension', 'vocabcomprehension', 'questiondifficulty'
]
elaborationb = ['lackknowledge']
organizationalb = ['setup', 'partialsetup', 'toodetailed']
affectivemotb = []
rehearsalc = []
elaborationc = [
'comprehension', 'comprehensionparts', 'positiveaffect',
'negativeaffect'
]
organizationalc = []
allgroups = [
rehearsal, elaboration, organizational, compmonitor2, affectivemot,
rehearsalb, elaborationb, organizationalb, rehearsalc, elaborationc,
organizationalc
]
allgroupsname = [
'rehearsal', 'elaboration', 'organizational', 'compmonitor2',
'affectivemot', 'rehearsalb', 'elaborationb', 'organizationalb',
'rehearsalc', 'elaborationc', 'organizationalc'
]
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] + " = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i] + suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" " + allgroupsname[k] + suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")
def main():
# TEST
f = open('test.txt', 'w+')
# VARIABLES
nameList = [
'vo', 'gc', 'ac', 'sk', 'pp', 'kt', 'kc', 'de', 'cv', 'fp', 'fa', 'ya',
'na', 'nv', 'nc', 'in', 'nt', 'tg', 'di', 'nd'
]
labelList = [
'vocab', 'generalcomp', 'factuncertainty', 'seperating',
'confuseprevious', 'lacktopicknow', 'lackcontentknow', 'toodetailed',
'cantverbalize', 'forgetparagraph', 'forgetall', 'positiveaffect',
'negativeaffect', 'nervous', 'noconcentrate', 'interviewerprocedure',
'notalki', 'toogeneral', 'distractor', 'nodifficulties'
]
proquestions = ['1', '2', '3']
suffix = ['im', 'ii', 'bm', 'bi']
stratdiff = "d"
promain = "p"
mathread = "r"
GLMtext = GLMfunctions.makeGLMtext()
f.write("DATASET COPY proCopy WINDOW=FRONT.\nDATASET ACTIVATE proCopy.\n")
for i in range(len(nameList)):
#RECODE AND COMPUTE
nameListi = nameList[i]
labelListi = labelList[i]
recodeline = GLMfunctions.makeRecodeLine(suffix, proquestions, promain,
mathread, stratdiff,
nameListi)
computelines = GLMfunctions.makeComputeLines(suffix, proquestions,
promain, mathread,
stratdiff, nameListi,
labelListi)
s = recodeline + computelines
f.write(s)
# GLM
text = "GLM " + labelListi + "im " + labelListi + "ii " + labelListi + "bm " + labelListi + "bi " + "BY sex"
f.write(text)
f.write(GLMtext)
f.write('\n')
# GROUPED
readdiff1 = ["vocab", "generalcomp", "lackcontentknow"]
readdiff2 = ["toodetailed", "forgetall"]
readdiff3 = ["negativeaffect", "toogeneral"]
GLMfunctions.printCompute(readdiff1, "readdiff1", suffix, GLMtext, f)
GLMfunctions.printCompute(readdiff2, "readdiff2", suffix, GLMtext, f)
GLMfunctions.printCompute(readdiff3, "readdiff3", suffix, GLMtext, f)
# Recall vs encoding vs comprehension monitoring
recall = [
'cantverbalize', 'forgetparagraph', 'forgetall', 'nervous',
'noconcentrate', 'notalki', 'toogeneral', 'distractor'
]
encoding = [
'vocab', 'generalcomp', 'factuncertainty', 'seperating',
'confuseprevious', 'lacktopicknow', 'lackcontentknow', 'toodetailed',
'positiveaffect', 'negativeaffect'
]
compmonitor = []
# Rehearsal vs elaboration vs organizational vs comprehension monitoring vs affective/motivational
rehearsal = ['cantverbalize', 'forgetparagraph', 'forgetall']
elaboration = [
'lacktopicknow', 'lackcontentknow', 'positiveaffect', 'negativeaffect'
]
organizational = [
'vocab', 'generalcomp', 'factuncertainty', 'seperating',
'confuseprevious', 'toodetailed', 'toogeneral'
]
compmonitor2 = ['notalki']
affectivemot = ['nervous', 'noconcentrate', 'distractor']
#Basic vs complex for rehearsal, elaboration, organizational
rehearsalb = ['cantverbalize', 'forgetparagraph', 'forgetall']
elaborationb = []
organizationalb = [
'vocab', 'generalcomp', 'confuseprevious', 'toodetailed', 'toogeneral'
]
rehearsalc = []
elaborationc = [
'lacktopicknow', 'lackcontentknow', 'positiveaffect', 'negativeaffect'
]
organizationalc = ['factuncertainty', 'seperating']
selection = [
'confuseprevious', 'toodetailed', 'positiveaffect', 'negativeaffect'
]
acquisition = ['vocab', 'generalcomp']
construction = ['factuncertainty']
integration = ['seperating', 'lacktopicknow', 'lackcontentknow']
allgroups = [
recall, encoding, compmonitor, rehearsal, elaboration, organizational,
compmonitor2, affectivemot, rehearsalb, elaborationb, organizationalb,
rehearsalc, elaborationc, organizationalc, selection, acquisition,
construction, integration
]
allgroupsname = [
'recall', 'encoding', 'compmonitor', 'rehearsal', 'elaboration',
'organizational', 'compmonitor2', 'affectivemot', 'rehearsalb',
'elaborationb', 'organizationalb', 'rehearsalc', 'elaborationc',
'organizationalc', 'selection', 'acquisition', 'construction',
'integration'
]
for k in range(len(allgroups)):
for j in range(len(suffix)):
f.write("compute " + allgroupsname[k] + suffix[j] + " = (")
for i in range(len(allgroups[k])):
if i != 0:
f.write("+")
f.write(allgroups[k][i] + suffix[j])
f.write(")/" + str(len(allgroups[k])) + ".\n")
f.write("\n\nGLM")
for j in range(len(suffix)):
f.write(" " + allgroupsname[k] + suffix[j])
f.write(" BY sex")
f.write(GLMtext + "\n")