The first item in this example woul

The first item in this example would
be better at differentiating between
people with low levels of the trait,
while the latter item would be better
at differentiating between people with
high levels of the trait. Conversely, the
higher difficulty item would perform
poorly when used to differentiate people
at the low end of the trait range (people
low on the trait are all fairly likely to get
this ‘hard’ item ‘wrong’), and the low
difficulty item would perform poorly
for differentiating between people at the
high end of the trait range (people high
on the trait would all be fairly likely to
get this ‘easy’ item ‘correct’).
The difficulty and discrimination
parameters can be combined to provide
item Test Information Functions. By
combining these functions, we can
estimate the level of precision (i.e.,
reliability) of the entire scale across
the entire trait range. You can get a
good idea of how these parameters are
combined to provide test information
(I) by looking at the following equation:
(3.0) Ij
(θ) = αj
2
× Pj
(θi
) × (1- Pj
(θi
))
In this equation, αj
2
is the squared
item discrimination parameter for the
jth item, and Pj
(θi
) is the probability
of endorsing item j for individuals
with a given (i) level of trait θ. A Test
Information Function that looked like a
bell curve centered on a score of θ = 0
would indicate that the scale provided
the most information about participants
who were near the average level of the
trait, but provided progressively less
information about people at the high or
low extremes of the trait range.

The first item in this example would 
be better at differentiating between 
people with low levels of the trait, 
while the latter item would be better 
at differentiating between people with 
high levels of the trait. Conversely, the 
higher difficulty item would perform 
poorly when used to differentiate people 
at the low end of the trait range (people 
low on the trait are all fairly likely to get 
this ‘hard’ item ‘wrong’), and the low 
difficulty item would perform poorly 
for differentiating between people at the 
high end of the trait range (people high 
on the trait would all be fairly likely to 
get this ‘easy’ item ‘correct’). 
The difficulty and discrimination 
parameters can be combined to provide 
item Test Information Functions. By 
combining these functions, we can 
estimate the level of precision (i.e., 
reliability) of the entire scale across 
the entire trait range. You can get a 
good idea of how these parameters are 
combined to provide test information 
(I) by looking at the following equation: 
(3.0) Ij
(θ) = αj
2
 × Pj
(θi
) × (1- Pj
(θi
))
In this equation, αj
2
 is the squared 
item discrimination parameter for the 
jth item, and Pj
(θi
) is the probability 
of endorsing item j for individuals 
with a given (i) level of trait θ. A Test 
Information Function that looked like a 
bell curve centered on a score of θ = 0 
would indicate that the scale provided 
the most information about participants 
who were near the average level of the 
trait, but provided progressively less 
information about people at the high or 
low extremes of the trait range.

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Hasil (Bahasa Indonesia) 1: [Salinan]

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The first item in this example would be better at differentiating between people with low levels of the trait, while the latter item would be better at differentiating between people with high levels of the trait. Conversely, the higher difficulty item would perform poorly when used to differentiate people at the low end of the trait range (people low on the trait are all fairly likely to get this ‘hard’ item ‘wrong’), and the low difficulty item would perform poorly for differentiating between people at the high end of the trait range (people high on the trait would all be fairly likely to get this ‘easy’ item ‘correct’). The difficulty and discrimination parameters can be combined to provide item Test Information Functions. By combining these functions, we can estimate the level of precision (i.e., reliability) of the entire scale across the entire trait range. You can get a good idea of how these parameters are combined to provide test information (I) by looking at the following equation: (3.0) Ij(θ) = αj2 × Pj(θi) × (1- Pj(θi))In this equation, αj2 is the squared item discrimination parameter for the jth item, and Pj(θi) is the probability of endorsing item j for individuals with a given (i) level of trait θ. A Test Information Function that looked like a bell curve centered on a score of θ = 0 would indicate that the scale provided the most information about participants who were near the average level of the
trait, but provided progressively less
information about people at the high or
low extremes of the trait range.

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Hasil (Bahasa Indonesia) 2:[Salinan]

Disalin!

Item pertama dalam contoh ini akan
lebih baik di membedakan antara
orang dengan tingkat rendah sifat tersebut,
sementara item kedua akan lebih baik
di antara orang-orang dengan membedakan
tingkat tinggi sifat tersebut. Sebaliknya,
lebih tinggi tingkat kesulitan butir soal akan tampil
buruk bila digunakan untuk membedakan orang
pada akhir rendah dari kisaran sifat (orang
rendah pada sifat tersebut semua cukup kemungkinan untuk mendapatkan
item ini 'keras' 'salah'), dan rendah
item yang kesulitan akan berkinerja buruk
untuk membedakan antara orang-orang di
high end dari kisaran sifat (orang tinggi
pada sifat yang semua akan cukup mungkin untuk
mendapatkan ini 'mudah' item 'benar').
Kesulitan dan diskriminasi
parameter dapat dikombinasikan untuk menyediakan
barang Informasi Uji Fungsi. Dengan
menggabungkan fungsi-fungsi ini, kita dapat
memperkirakan tingkat presisi (yaitu,
keandalan) dari seluruh skala di
seluruh rentang sifat. Anda bisa mendapatkan
ide bagus tentang bagaimana parameter ini
dikombinasikan untuk memberikan informasi tes
(I) dengan melihat persamaan berikut:
(3.0) Ij
(θ) = αj
2
× Pj
(θi) × (1- Pj (θi)) Dalam persamaan ini, αj 2 adalah kuadrat parameter diskriminasi barang untuk barang-j, dan Pj (θi) adalah probabilitas dari mendukung barang j untuk individu dengan yang diberikan (i) tingkat sifat θ. Uji Fungsi Informasi yang tampak seperti kurva lonceng berpusat pada skor θ = 0 akan menunjukkan bahwa skala yang disediakan sebagian besar informasi tentang peserta yang berada di dekat tingkat rata-rata dari sifat, tetapi tersedia semakin sedikit informasi tentang orang-orang di tinggi atau ekstrem rendah kisaran sifat.

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Hasil (Bahasa Indonesia) 3:[Salinan]

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