def administer(self, examinee, item):
model = item.get_model(0)
if (examinee.num_items() < self.num):
if (self.high): resp = 1
else: resp = 0
else:
if (oscats.oscats_rnd_uniform() < model.P(
1, examinee.get_sim_theta(), nullCovariates)):
resp = 1
else:
resp = 0
examinee.add_item(item, resp)
return resp
def administer(self, examinee, item):
model = item.get_model(0)
if examinee.num_items() < self.num:
if self.high:
resp = 1
else:
resp = 0
else:
if oscats.oscats_rnd_uniform() < model.P(1, examinee.get_sim_theta(), nullCovariates):
resp = 1
else:
resp = 0
examinee.add_item(item, resp)
return resp
dim = contSpace.get_dim_by_name("Cont.1")
out.write("%s\t%g\t" % (e.get_property("id"), tmp.get_cont(dim)))
tmp = e.get_theta_by_name("trueAlpha")
dim = binSpace.get_dim_by_name("Bin.1")
for k in range(N_ATTR) :
if tmp.get_bin(dim) : out.write("1")
else : out.write("0")
dim += 1
# Choose initial theta and alpha for this examinee
# We'll use the same initial abilities for all four tests
dim = contSpace.get_dim_by_name("Cont.1")
initTheta.set_cont(dim, oscats.oscats_rnd_normal(sqrt(0.05))-0.37)
dim = binSpace.get_dim_by_name("Bin.1")
for k in range(N_ATTR) :
initAlpha.set_bin(dim, oscats.oscats_rnd_uniform() < 0.5) ; dim += 1
# Initialize ability estimates for this examinee
e.set_theta_by_name("estTheta", oscats.Point(space=contSpace))
e.set_theta_by_name("estAlpha", oscats.Point(space=binSpace))
for test in tests :
# Reset initial latent ability for this test
e.get_theta_by_name("estTheta").copy(initTheta)
e.get_theta_by_name("estAlpha").copy(initAlpha)
# Do the administration!
test.administer(e)
# Output the resulting theta.hat and alpha.hat
tmp = e.get_theta_by_name("estTheta")
out.write("%s\t%g\t" % (e.get_property("id"), tmp.get_cont(dim)))
tmp = e.get_theta_by_name("trueAlpha")
dim = binSpace.get_dim_by_name("Bin.1")
for k in range(N_ATTR):
if tmp.get_bin(dim): out.write("1")
else: out.write("0")
dim += 1
# Choose initial theta and alpha for this examinee
# We'll use the same initial abilities for all four tests
dim = contSpace.get_dim_by_name("Cont.1")
initTheta.set_cont(dim, oscats.oscats_rnd_normal(sqrt(0.05)) - 0.37)
dim = binSpace.get_dim_by_name("Bin.1")
for k in range(N_ATTR):
initAlpha.set_bin(dim,
oscats.oscats_rnd_uniform() < 0.5)
dim += 1
# Initialize ability estimates for this examinee
e.set_theta_by_name("estTheta", oscats.Point(space=contSpace))
e.set_theta_by_name("estAlpha", oscats.Point(space=binSpace))
for test in tests:
# Reset initial latent ability for this test
e.get_theta_by_name("estTheta").copy(initTheta)
e.get_theta_by_name("estAlpha").copy(initAlpha)
# Do the administration!
test.administer(e)
# Output the resulting theta.hat and alpha.hat
