Pengaruh Kualitas Produk, Desain dan Citra Merek Terhadap ... · 67 LAMPIRAN 1 KUESIONER Pengaruh...
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Transcript of Pengaruh Kualitas Produk, Desain dan Citra Merek Terhadap ... · 67 LAMPIRAN 1 KUESIONER Pengaruh...
67
LAMPIRAN 1
KUESIONER
Pengaruh Kualitas Produk, Desain dan Citra Merek Terhadap Keputusan Pembelian Sepeda Motor Honda Scoopy
Tujuan Kuesioner Penelitian Kuesioner ini bertujuan mengumpulkan data yang berhubungan dengan
Kualitas Produk, Desain dan Citra Merek Terhadap Keputusan Pembelian Sepeda Motor Honda Scoopy diwilayah Cengkareng, Jakarta Barat. Penelitian ini dilakukan dalam rangka menyelesaikan tugas akhir skripsi Jurusan Ekonomi Manajemen di Universitas Esa Unggul. Saya sangat menghargai partisipasi anda dalam menjawab pertanyaan kuesioner ini. Atas kesediaan dalam meluangkan waktu untuk mengisi kuesioner ini, saya ucapkan terima kasih. BAGIAN 1: DATA RESPONDEN
Pilihlah salah satu jawaban pada setiap pertanyaan dengan memberikan tanda silang (X).
1. Jenis kelamin anda?
a. Laki-laki b.perempuan
2. Berapa usia anda saat ini?
a. 17-22 tahun
b. 23-28 tahun
c. 29-34 tahun
d. >35 tahun
3. Berapa pendapatan anda tiap bulan?
a. < Rp. 3.000.000
b. Rp. 3.000.000 s/d Rp. 3.999.000
c. Rp. 4.000.000 s/d Rp. 4.999.000
d. > Rp. 5.000.000
4. Apa pekerjaan anda?
a. Pegawai negeri sipil
68
b. Karyawan swasta
c. Wiraswasta
d. Lain-lain …………………………….. (tuliskan)
BAGIAN 2: CARA PENGISIAN
1. Bacalah sebaik-baiknya setiap pertanyaan dan setiap alternative jawaban yang diberikan.
2. Pilih alternatif jawaban yang paling sesuai menurut anda dan berikan tanda (√)
3. Jika terjadi kesalahan pengisian, berikan tanda (X) pada jawaban yang salah tersebut
Keterangan:
1. SS= sangat setuju (4)
2. S= Setuju (3)
3. TS= Tidak Setuju (2)
4. STS= Sangat Tidak Setuju (1)
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ALTERNATIVE
JAWABAN No PERNYATAAN
SS S TS STS
1 Kinerja mesin sepeda motor Honda scoopy sangat baik
2 Mudah pengoperasian dan mengunakan produk sepeda motor Honda scoopy
3 Tampilan sepeda motor Honda scoopy sangat menarik
4 Sepeda motor Honda scoopy tersedia banyak pilihan jenis dan tipe
5 Sepeda motor Honda scoopy sudah fuel injection
6 Sepeda motor Honda scoopy bergaransi mesin selama 3 tahun
7 Sepeda motor Honda scoopy tidak ada cacat pada body
8 Kualitas sepeda motor Honda scoopy sesuai standar nasional
9 Daya tahan mesin sepeda motor Honda scoopy tahan lama
10 Body sepeda motor Honda scoopy tidak mudah rusak atau pecah
11 Kombinasi warna sepeda motor Honda scoopy sangat baik
12 Sepeda motor Honda scoopy banyak pilihan warnanya
13 Pemilihan warna striping pada body sepeda motor Honda scoopy bervariasi
14 Striping sepeda motor Honda scoopy model terbaru
15 Desain pada body sepeda motor Honda scoopy yang unik
16 Desain sepeda motor Honda scoopy sangat inovatif
17 Merek sepeda motor Honda scoopy sangat popular dikalangan masyarakat
18 Sepeda motor Honda scoopy sangat modern
19 Kuatnya nama produk Honda scoopy dibenak masyarakat
20 Merek Honda scoopy sangat terpercaya
ALTERNATIVE JAWABAN No PERNYATAAN
SS S TS STS
21 Lambang dan logo Sepeda motor Honda scoopy sangat bagus
22 Sepeda motor Honda scoopy mudah diingat (terkenal)
23 Membeli sepeda motor Honda scoopy karena sesuai kebutuhan dan manfaat
24 Motivasi dari keluarga untuk membeli sepeda motor Honda scoopy
25 Informasi sepeda motor Honda scoopy dapat ditemukan di internet
26 Informasi sepeda motor Honda scoopy dapat ditemukan melalui media iklan
27 Informasi sepeda motor Honda scoopy didapatkan dari teman
28 Melakukan evaluasi produk sebelum membeli sepeda motor Honda scoopy
29 Memilih beberapa produk sebelum membeli sepeda motor honda scoopy
30 Menetapkan pilihan pada sepeda motor Honda scoopy
31 Berniat membeli sepeda motor Honda scoopy
32 Melakukan keputusan pembelian pada sepeda motor Honda scoopy
33 Kepuasan setelah membeli dan mengunakan sepeda motor Honda scoopy
70
34 Merekomendasikan kepada teman setelah membeli sepeda motor Honda scoopy
71
LAMPIRAN 2
UJI VALIDITAS
No Indikator Nilai r
Hitung Nilai r Tabel Keterangan
1 Kinerja mesin Honda scoopy sangat baik
0,564 0,361 Valid
2 Mudah pengoperasian dan mengunakan produk Honda scoopy
0,558 0,361 Valid
3 Tampilan sepeda motor Honda scoopy sangat menarik
0,469 0,361 Valid
4 Sepeda motor Honda scoopy tersedia banyak pilihan jenis dan tipe
0,663 0,361 Valid
5 Sepeda motor Honda scoopy sudah fuel injection
0,711 0,361 Valid
6 Sepeda motor Honda scoopy bergaransi mesin selama 3 tahun
0,527 0,361 Valid
7 Sepeda motor Honda scoopy tidak ada cacat pada body
0,515 0,361 Valid
8 Kualitas sepeda motor Honda scoopy sesuai standar nasional
0,667 0,361 Valid
9 Daya tahan mesin sepeda motor Honda scoopy tahan lama
0,415 0,361 Valid
10 Body sepeda motor Honda scoopy tidak mudah rusak atau pecah
0,713 0,361 Valid
11 Kombinasi warna sepeda motor Honda scoopy sangat baik
0,396 0,361 Valid
12 Sepeda motor Honda scoopy banyak pilihan warnanya
0,604 0,361 Valid
13 Pemilihan warna striping pada body sepeda motor Honda scoopy bervariasi
0,634 0,361 Valid
14 Striping sepeda motor Honda scoopy model terbaru
0,659 0,361 Valid
15 Desain pada body sepeda motor Honda scoopy yang unik
0,381 0,361 Valid
16 Sepeda motor Honda scoopy sangat inovatif
0,515 0,361 Valid
17 Merek sepeda motor Honda scoopy sangat popular dikalangan masyarakat
0,550 0,361 Valid
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No Indikator Nilai r Hitung
Nilai r Tabel Keterangan
18 Sepeda motor Honda scoopy sangat modern
0,495 0,361 Valid
19 Kuatnya nama produk Honda scoopy dikalangan masyarakat
0,659 0,361 Valid
20 Merek Honda scoopy sangat terpercaya
0,765 0,361 Valid
21 Lambang dan logo Sepeda motor Honda scoopy sangat bagus
0,586 0,361 Valid
22 Sepeda motor Honda scoopy mudah diingat (terkenal)
0,712 0,361 Valid
23 Sangat menyukai sepeda motor Honda scoopy
0,406 0,361 Valid
24 Motivasi dari keluarga untuk membeli sepeda motor Honda scoopy
0,459 0,361 Valid
25 Informasi sepeda motor Honda scoopy dapat ditemukan di internet
0,419 0,361 Valid
26 Infomasi sepeda motor Honda scoopy dapat ditemui melalui media iklan
0,466 0,361 Valid
27 Informasi sepeda motor Honda scoopy didapatkan dari teman
0,391 0,361 Valid
28 Melakukan evaluasi produk sebelum membeli sepeda motor Honda scoopy
0,438 0,361 Valid
29 Memilih beberapa produk sebelum membeli sepeda motor honda scoopy
0,535 0,361 Valid
30 Menetapkan pilihan pada sepeda motor Honda scoopy
0,525 0,361 Valid
31 Berniat membeli sepeda motor Honda scoopy
0,570 0,361 Valid
32 Melakukan keputusan pembelian pada sepeda motor Honda scoopy
0,713 0,361 Valid
33 Kepuasan setelah membeli dan mengunakan sepeda motor Honda scoopy
0,597 0,361 Valid
34 Merekomendasikan kepada teman setelah membeli sepeda motor
0,444 0,361 Valid
73
Honda scoopy
73
LAMPIRAN 3 TABULASI PRE-TEST
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
1 3 3 3 4 4 3 4 4 3 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 2 3 3 4 4 4 4 4 4 4
2 3 3 3 2 4 3 4 4 3 3 3 4 3 4 4 4 3 3 3 4 2 3 3 4 4 2 2 3 4 2 4 3 4 2
3 4 4 4 4 4 4 4 4 4 4 4 4 3 4 3 4 4 4 3 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4
4 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 4 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3
5 3 3 3 3 3 2 3 3 3 3 2 3 2 3 3 3 2 2 2 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3
6 2 1 3 3 2 4 3 2 3 3 4 2 2 2 2 2 4 4 2 2 2 2 4 3 2 2 3 4 2 3 2 3 3 3
7 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3
8 4 4 3 4 4 4 4 4 3 4 4 4 4 3 3 4 4 4 4 3 3 4 4 4 3 4 3 4 3 4 4 4 4 3
9 3 3 3 3 3 3 3 3 3 3 3 3 2 2 4 3 3 3 2 2 3 3 3 3 2 4 3 2 4 3 3 3 3 4
10 3 3 3 3 3 3 3 3 3 3 3 3 2 2 4 4 3 3 2 2 4 4 3 3 2 4 3 4 4 3 3 3 3 4 11 3 3 3 3 3 3 3 3 3 3 3 3 2 2 4 4 3 3 2 2 4 3 3 3 2 4 3 2 4 3 3 3 3 4 12 3 2 3 4 3 3 2 3 3 4 3 3 3 3 3 1 3 3 3 3 3 3 3 2 2 1 3 4 3 4 3 4 2 3 13 3 4 3 3 3 1 2 4 3 3 1 3 2 2 2 3 1 2 2 2 3 3 4 3 2 3 3 2 2 3 3 3 2 3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
14 4 3 3 4 4 3 4 3 3 4 3 3 3 3 3 3 3 3 4 3 3 4 3 4 3 3 3 3 3 4 4 4 4 3 15 3 4 3 3 3 4 3 3 3 3 4 4 3 3 3 3 4 4 3 3 3 3 4 3 3 3 4 3 3 3 3 3 3 3 16 4 4 4 4 4 3 4 4 4 4 3 3 4 4 4 4 3 3 4 4 4 4 3 4 3 4 4 4 4 4 4 4 4 4
74
17 3 3 2 3 3 3 3 3 2 3 3 4 3 2 2 2 3 3 3 2 3 3 3 3 3 2 3 3 2 3 3 3 3 3 18 3 3 3 3 2 3 3 3 3 3 3 3 4 3 3 3 3 3 4 3 3 3 3 3 3 3 3 4 3 3 2 3 3 3 19 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 3 3 3 4 4 3 3 3 3 2 3 3 4 4 3 3 3 3 3 20 3 3 4 3 4 2 3 3 4 3 2 4 4 3 3 3 2 4 4 3 2 4 2 3 2 3 3 4 3 3 4 3 3 4 21 3 3 4 2 2 3 4 2 4 2 4 3 2 2 2 3 3 4 2 2 2 2 3 4 2 3 3 2 2 3 3 2 3 3 22 2 3 4 4 3 4 4 4 4 4 3 4 4 3 3 2 3 4 4 3 4 3 4 4 3 2 4 4 3 3 4 4 3 3 23 4 3 4 3 3 3 4 3 3 3 3 4 3 2 2 2 3 4 3 3 3 3 3 4 3 2 2 3 2 3 4 3 4 3 24 4 3 4 3 3 4 4 3 3 3 4 3 2 2 2 4 3 4 3 3 4 3 4 4 3 4 4 2 2 3 4 3 3 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
25 3 3 3 3 3 3 3 3 3 3 4 3 3 4 3 4 3 3 3 3 3 3 3 3 3 4 3 3 3 3 4 3 4 4 26 4 4 4 4 3 4 4 4 3 4 3 4 4 3 3 4 4 4 4 3 4 3 4 4 4 4 4 4 3 3 4 4 4 4 27 4 3 4 4 4 4 3 4 4 4 3 4 4 2 2 4 3 4 4 4 4 4 4 3 3 4 2 4 2 4 2 4 4 3 28 4 3 4 3 4 4 3 4 4 3 4 4 3 4 4 4 3 4 3 4 3 4 4 3 3 4 4 3 4 4 3 3 3 4 29 3 4 4 4 4 3 3 3 4 4 4 4 3 4 4 4 4 4 4 4 4 4 3 3 3 4 3 3 4 4 4 4 3 4 30 3 3 4 2 2 3 4 2 4 2 4 3 2 2 2 4 3 4 2 2 3 2 3 4 3 4 3 2 2 4 3 2 4 3
75
LAMPIRAN 4
TABULASI 100 RESPONDEN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
1 3 4 4 3 3 3 3 2 4 2 2 2 3 3 2 4 4 3 2 4 2 2 3 2 3 3 3 4 3 2 4 2 2 2
2 3 3 4 3 3 4 4 3 3 3 2 2 4 4 3 4 3 4 3 3 3 2 3 3 4 3 3 3 4 3 3 3 2 2
3 4 4 3 4 4 3 4 4 4 4 3 4 3 4 4 4 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4 4 3 4
4 4 3 4 4 3 4 4 4 2 4 3 3 4 4 4 4 4 4 4 2 4 3 4 3 4 4 3 4 4 4 2 4 3 3
5 4 3 4 4 3 4 4 3 3 3 4 3 4 4 3 3 3 4 3 3 3 4 4 3 4 4 3 3 4 3 3 3 4 3
6 2 3 4 3 2 4 3 2 3 3 3 3 4 3 2 4 3 3 2 3 3 3 2 3 4 3 2 3 3 2 3 3 3 3
7 3 4 3 4 4 4 3 4 4 2 3 2 3 3 4 4 4 3 4 4 2 3 3 4 3 4 4 4 3 4 4 2 3 2 8
4 4 3 4 3 3 4 3 4 4 3 3 3 4 3 4 4 4 3 4 4 3 4 4 3 4 3 4 4 3 4 4 3 3 9
3 3 4 3 4 4 4 3 3 4 4 3 4 4 3 4 3 4 3 3 4 4 3 3 4 3 2 3 4 3 3 4 4 3 10 4 3 4 3 3 4 3 3 3 3 3 3 4 3 3 4 3 3 3 3 3 3 4 3 4 3 3 3 3 3 3 3 3 3 11 3 4 3 4 4 3 3 4 4 4 3 4 3 3 4 4 4 3 4 4 4 3 3 4 3 4 4 4 3 4 4 4 3 4 12 4 3 4 4 3 4 4 3 3 4 4 3 4 4 3 4 3 4 3 3 4 4 4 3 4 4 3 3 4 3 3 4 4 3 13 2 3 4 3 2 4 3 2 4 4 3 3 4 3 2 3 4 3 2 4 4 3 2 3 4 3 2 4 3 2 2 3 3 3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
14 3 4 3 4 4 3 4 4 4 3 4 4 4 4 4 3 4 4 4 4 3 4 3 4 3 4 4 4 4 4 4 3 3 2 15 4 4 3 4 3 4 4 3 4 3 3 3 4 4 3 2 4 4 3 4 3 3 4 4 3 4 3 4 2 3 4 3 3 3 16 3 3 4 3 2 4 4 3 3 3 4 3 4 4 3 4 3 4 3 3 3 4 3 3 4 3 2 3 4 3 3 3 4 3
76
17 4 3 4 3 3 4 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 4 3 4 3 3 3 3 3 3 3 3 3 18 3 4 3 4 4 3 3 4 4 3 3 4 3 3 4 4 4 3 4 4 3 3 3 4 3 4 4 4 3 4 4 3 3 4 19 4 4 3 4 4 3 4 4 4 3 3 4 3 4 4 3 4 4 4 4 3 3 4 4 3 4 4 4 4 4 4 3 3 4 20 4 3 4 4 3 4 4 4 3 3 3 3 4 4 4 3 3 4 4 3 3 3 4 3 4 4 3 3 4 4 3 3 3 3 21 4 3 4 4 3 4 3 3 3 2 4 3 4 3 3 3 3 3 3 3 2 4 4 3 4 4 3 3 3 3 3 2 4 3 22 2 3 4 3 2 4 3 2 3 4 3 3 4 3 2 3 3 3 2 3 4 3 2 3 4 3 2 3 3 2 3 4 3 3 23 3 4 3 4 4 3 3 4 4 3 3 2 3 3 4 3 4 3 4 4 3 3 3 4 3 4 4 4 3 4 4 3 3 2 24 4 4 3 4 3 3 3 3 4 3 3 3 3 3 3 2 4 3 3 4 3 3 4 4 3 4 3 4 3 3 4 3 3 3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
25 4 3 4 3 3 4 4 3 3 4 2 3 4 4 3 4 3 4 3 3 4 2 4 3 4 3 3 3 4 3 3 4 2 3 26 4 2 2 4 3 4 3 4 2 3 2 3 4 3 4 3 2 3 4 2 3 2 4 2 4 4 3 2 3 4 2 3 2 3 27 4 4 3 3 4 3 3 3 4 3 4 3 3 3 3 3 4 3 3 4 3 4 4 4 3 3 4 4 3 3 4 3 4 3 28 4 3 3 4 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 4 3 3 4 3 3 3 3 3 3 3 3 29 4 3 3 3 3 3 2 4 3 3 3 3 3 2 4 3 3 4 4 3 3 3 4 3 3 3 3 3 2 4 3 3 3 3 30 3 3 3 3 3 3 2 4 3 3 4 4 3 2 4 3 3 4 4 3 3 4 3 3 3 3 3 3 2 4 3 3 4 4 31 4 4 3 4 4 3 4 4 4 3 3 2 3 4 4 3 4 4 4 4 3 3 4 4 3 4 4 4 4 4 4 3 3 2
77
32 4 4 4 4 3 4 3 4 4 3 3 3 4 3 4 3 4 3 4 4 3 3 4 4 4 4 3 4 3 4 4 3 3 3 33 4 2 2 4 3 4 4 4 3 3 3 3 4 4 4 3 3 4 4 3 3 3 4 2 4 4 3 3 4 4 3 3 3 3 34 4 4 3 3 4 3 2 3 4 3 3 4 3 2 3 4 4 4 3 4 3 3 4 4 3 3 4 4 2 3 4 3 3 4 35 4 3 3 4 3 3 4 3 3 3 3 3 3 4 3 3 3 4 3 3 3 3 4 3 3 4 3 3 4 3 3 3 3 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
36 4 3 3 3 3 3 3 4 3 4 3 4 3 3 4 3 3 3 4 3 4 3 4 3 3 3 3 3 3 4 3 4 3 4 37 3 3 3 3 3 3 3 4 3 3 3 2 3 3 4 3 3 3 4 3 3 3 3 3 3 3 3 3 3 4 3 3 3 2 38 4 4 3 4 4 3 3 4 4 2 3 4 3 3 4 4 4 3 4 4 2 3 4 4 3 4 4 4 3 4 4 2 3 4 39 4 4 4 4 4 4 4 4 4 3 3 3 4 4 4 3 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 3 3 3 40 4 3 4 3 3 4 3 3 3 4 3 4 4 3 3 3 3 3 3 3 4 3 4 3 4 3 3 3 3 3 3 4 3 4 41 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 42 3 4 2 2 4 2 4 2 4 3 2 3 2 4 2 3 4 4 2 4 3 2 3 4 2 2 4 4 4 2 4 3 2 3
78
43 3 2 4 3 3 4 3 4 2 4 3 4 4 3 4 3 2 3 4 2 4 3 3 2 4 3 3 2 3 4 2 4 3 4 44 1 3 3 2 3 3 3 3 3 3 2 4 3 3 3 3 3 3 3 3 3 2 1 3 3 2 3 3 3 3 3 3 2 4 45 3 3 2 2 3 4 3 4 3 2 3 4 4 3 4 4 3 3 4 3 2 3 3 3 4 2 3 3 3 4 3 2 3 4 46 3 3 4 2 3 4 4 2 3 3 4 3 4 4 2 3 3 4 2 3 3 4 3 3 4 2 3 3 4 2 3 3 4 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
47 3 4 4 3 4 4 3 4 4 4 3 4 4 3 4 3 4 3 4 4 4 3 3 4 4 3 4 4 3 4 4 4 3 4 48 3 3 4 4 4 4 4 3 3 4 3 4 4 4 3 4 3 4 3 3 4 3 3 3 4 4 4 3 4 3 3 4 3 4 49 4 3 2 4 3 4 4 3 3 3 3 2 4 4 3 3 3 4 3 3 3 3 4 3 4 4 3 3 4 3 3 3 3 2 50 2 3 3 4 2 3 2 3 3 3 2 3 3 2 3 4 3 4 3 3 3 2 2 3 3 4 2 3 2 3 3 3 2 3 51 3 2 3 4 4 3 4 4 2 3 4 3 3 4 4 4 2 4 4 2 3 4 3 2 3 4 4 2 4 4 2 3 4 3 52 3 4 3 4 3 3 3 3 4 4 3 3 3 3 3 4 4 3 3 4 4 3 3 4 3 4 3 4 3 3 4 4 3 3 53 4 4 3 4 3 3 3 3 4 3 3 3 3 3 3 4 4 3 3 4 3 3 4 4 3 4 3 4 3 3 4 3 3 3
79
54 3 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 2 3 2 3 3 55 4 4 4 3 4 4 4 2 4 4 3 2 4 4 2 3 4 4 2 4 4 3 4 4 4 3 4 4 4 2 4 4 3 2 56 4 2 3 4 4 3 2 4 2 2 1 3 3 2 4 3 2 2 4 2 2 1 4 2 3 4 4 2 2 4 2 2 1 3 57 2 4 3 4 3 3 3 4 4 4 3 4 3 3 4 3 4 3 4 4 4 3 2 4 3 4 3 4 3 4 4 4 3 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
58 3 4 3 4 3 3 3 4 4 3 4 3 3 3 4 3 4 3 4 4 3 4 3 4 3 4 3 4 3 4 4 3 4 3 59 4 3 3 3 3 3 4 3 3 4 4 3 3 4 3 2 3 4 3 3 4 4 4 3 3 3 3 3 4 3 3 4 4 3 60 4 4 4 4 4 4 3 4 4 3 3 4 4 3 4 3 4 3 4 4 3 3 4 4 4 4 4 4 3 4 4 3 3 4 61 4 4 2 3 4 2 3 3 4 3 3 4 2 3 3 3 4 3 3 4 3 3 4 4 2 3 4 4 3 3 4 3 3 4 62 4 4 3 2 3 3 3 4 4 3 2 3 3 3 4 4 4 3 4 4 3 2 4 4 3 2 3 4 3 4 4 3 2 3 63 3 4 3 4 2 3 4 3 4 3 4 4 3 4 3 4 4 4 3 4 3 4 3 4 3 4 2 4 4 3 4 3 4 4 64 3 2 3 3 3 3 2 2 2 1 2 3 3 2 2 3 2 2 2 2 1 2 3 2 3 3 3 2 2 2 2 1 2 3
80
65 3 3 4 4 3 4 3 3 3 3 3 4 4 3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 4 66 3 3 4 4 3 4 4 4 3 4 4 3 4 4 4 3 3 4 4 3 4 4 3 3 4 4 3 3 4 4 3 4 4 3 67 3 4 4 3 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 3 4 3 4 4 3 4 4 4 4 4 3 4 4 68 3 3 4 3 3 4 2 4 3 3 4 4 4 2 4 4 3 4 4 3 3 4 3 3 4 3 3 3 2 4 3 3 4 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
69 3 3 2 3 3 4 4 3 3 3 3 3 4 4 3 3 3 4 3 3 3 3 3 3 4 3 3 3 4 3 3 3 3 3 70 2 2 3 3 2 3 4 2 2 3 2 3 3 4 2 3 2 4 2 2 3 2 2 2 3 3 2 2 4 2 2 3 2 3 71 2 2 2 2 3 2 4 2 2 2 3 2 2 4 2 3 2 4 2 2 2 3 2 2 2 2 3 2 4 2 2 2 3 2 72 3 3 2 3 3 2 3 3 3 2 3 3 2 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 2 3 3 73 3 4 3 3 4 3 3 3 4 2 3 4 3 3 3 3 4 3 3 4 2 3 3 4 3 3 4 4 3 3 4 2 3 4 74 4 2 2 4 3 2 3 3 2 3 3 4 2 3 3 4 2 3 3 2 3 3 4 2 2 4 3 2 3 3 2 3 3 4 75 4 2 2 4 3 2 4 3 2 3 3 4 2 4 3 3 2 4 3 2 3 3 4 2 2 4 3 2 4 3 2 3 3 4
81
76 4 2 2 4 3 2 2 3 2 3 3 4 2 2 3 3 2 2 3 2 3 3 4 2 2 4 3 2 2 3 2 3 3 4 77 2 3 3 3 4 3 2 2 3 3 2 1 3 2 2 3 3 2 2 3 3 2 2 3 3 3 4 3 2 2 3 3 2 1 78 3 4 2 3 2 4 3 3 4 2 3 3 4 3 3 3 4 3 3 4 2 3 3 4 4 3 2 4 3 3 4 2 3 3 79 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 4 3 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
80 4 3 3 3 4 3 3 3 3 2 4 3 3 3 3 3 3 3 3 3 2 4 4 3 3 3 4 3 3 3 3 2 4 3 81 3 4 4 3 4 4 4 4 4 3 4 4 4 4 4 3 4 4 4 4 3 4 3 4 4 3 4 4 4 4 4 3 4 4 82 3 3 2 3 2 4 4 3 3 4 2 2 4 4 3 4 3 4 3 3 4 2 3 3 4 3 2 3 4 3 3 4 2 2 83 3 4 3 4 4 3 4 3 4 4 3 3 3 4 3 4 4 4 3 4 4 3 3 4 3 4 4 4 4 3 4 4 3 3 84 3 4 4 3 4 4 2 3 4 3 2 3 4 2 3 3 4 4 3 4 3 2 3 4 4 3 4 4 2 3 4 3 2 3 85 4 4 3 4 4 3 3 3 4 2 3 3 3 3 3 4 4 3 3 4 2 3 4 4 3 4 4 4 3 3 4 2 3 3 86 3 2 2 3 3 2 3 2 2 4 3 3 2 3 2 4 2 3 2 2 4 3 3 2 2 3 3 2 3 2 2 4 3 3
82
87 3 2 3 3 3 3 3 2 2 2 2 2 3 3 2 3 2 3 2 2 2 2 3 2 3 3 3 2 3 2 2 2 2 2 88 3 3 2 3 3 4 4 3 3 3 2 2 4 4 3 3 3 4 3 3 3 2 3 3 4 3 3 3 4 3 3 3 2 2 89 4 4 3 4 4 3 4 4 4 4 3 4 3 4 4 4 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4 4 3 4 90 4 3 4 4 3 4 4 4 3 4 3 3 4 4 4 4 3 4 4 3 4 3 4 3 4 4 3 3 4 4 3 4 3 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
91 4 3 4 4 3 4 4 3 3 3 4 3 4 4 3 3 3 4 3 3 3 4 4 3 4 4 3 3 4 3 3 3 4 3 92 2 3 4 3 2 4 3 2 3 3 3 3 4 3 2 4 3 3 2 3 3 3 2 3 4 3 2 3 3 2 3 3 3 3 93 3 4 3 4 4 3 3 4 4 2 3 2 3 3 4 4 4 3 4 4 2 3 3 4 3 4 4 4 3 4 4 2 3 2 94 4 4 3 4 3 3 3 3 4 4 3 3 3 3 3 3 4 3 3 4 4 3 4 4 3 4 3 4 3 3 4 4 3 3 95 3 3 4 3 2 4 4 3 3 4 4 3 4 4 3 3 3 4 3 3 4 4 3 3 4 3 2 3 4 3 3 4 4 3 96 4 3 4 3 3 4 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 4 3 4 3 3 3 3 3 3 3 3 3 97 3 4 3 4 4 3 3 4 4 4 3 4 3 3 4 3 4 3 4 4 4 3 3 4 3 4 4 4 3 4 4 4 3 4
83
98 4 3 4 4 3 4 4 3 3 4 4 3 4 4 3 4 3 4 3 3 4 4 4 3 4 4 3 3 4 3 3 4 4 3 99 2 3 4 3 2 4 3 2 3 3 3 3 4 3 2 2 3 3 2 3 3 3 2 3 4 3 2 3 3 2 3 3 3 3 100 4 4 3 4 4 3 3 3 4 2 3 3 3 3 3 4 4 3 3 4 2 3 4 4 3 4 4 4 3 3 4 2 3 3
85
LAMPIRAN 5
REGRESI LINIER BERGANDA
Descriptive Statistics
Mean Std. Deviation N
keputusan pembelian 38.97 4.091 100
kualitas produk 32.82 3.392 100
desain 19.46 2.022 100
citra merek 19.41 2.391 100
Correlations
keputusan
pembelian
kualitas
produk
desain citra
merek
keputusan pembelian 1.000 .942 .738 .906
kualitas produk .942 1.000 .691 .856
Desain .738 .691 1.000 .721Pearson Correlation
citra merek .906 .856 .721 1.000
keputusan pembelian . .000 .000 .000
kualitas produk .000 . .000 .000
Desain .000 .000 . .000Sig. (1-tailed)
citra merek .000 .000 .000 .
keputusan pembelian 100 100 100 100
kualitas produk 100 100 100 100
Desain 100 100 100 100N
citra merek 100 100 100 100
86
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .942a .887 .886 1.384
2 .961b .924 .922 1.141
3 .963c .927 .925 1.122
a. Predictors: (Constant), kualitas produk
b. Predictors: (Constant), kualitas produk, citra merek
c. Predictors: (Constant), kualitas produk, citra merek, desain
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
Regression 1469.176 1 1469.176 766.932 .000b
Residual 187.734 98 1.916 1
Total 1656.910 99
Regression 1530.697 2 765.348 588.201 .000c
Residual 126.213 97 1.301 2
Total 1656.910 99
Regression 1536.160 3 512.053 407.100 .000d
Residual 120.750 96 1.258 3
Total 1656.910 99
a. Dependent Variable: keputusan pembelian
b. Predictors: (Constant), kualitas produk
c. Predictors: (Constant), kualitas produk, citra merek
d. Predictors: (Constant), kualitas produk, citra merek, desain
87
Coefficientsa
Unstandardized Coefficients Standardized Coefficients
Model
B Std. Error Beta
t Sig.
(Constant) 1.691 1.353 1.250 .2141
kualitas produk 1.136 .041 .942 27.694 .000
(Constant) 1.947 1.116 1.745 .084
kualitas produk .751 .065 .623 11.482 .0002
citra merek .638 .093 .373 6.876 .000
(Constant) .902 1.206 .748 .456
kualitas produk .723 .066 .599 10.996 .000
citra merek .568 .097 .332 5.841 .0003
desain .171 .082 .085 2.084 .040
a. Dependent Variable: keputusan pembelian
Excluded Variablesa
Collinearity Statistics
Model Beta In t Sig. Partial Correlation
Tolerance
desain .167b 3.777 .000 .358 .522 1
citra merek .373b 6.876 .000 .572 .267
2 desain .085c 2.084 .040 .208 .460
a. Dependent Variable: keputusan pembelian
b. Predictors in the Model: (Constant), kualitas produk
c. Predictors in the Model: (Constant), kualitas produk, citra merek
88
88
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
1 .131 -
.014
.416** .307**
.008 .159 .342** .102 .037 .163 .102 .008 .159
Sig. (2-
tailed)
.195 .888 .000 .002 .937 .114 .000 .314 .715 .106 .314 .937 .114
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.131 1 .144 .187 .419**
.025 .053 .277** .968** .077 .152 .128 .025 .053
Sig. (2-
tailed)
.195 .154 .063 .000 .803 .600 .005 .000 .447 .131 .206 .803 .600
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
-.014 .144 1 .031 -
.059
.643**
.093 .008 .117 .273** .288** .058 .643**
.093
Sig. (2-
tailed)
.888 .154 .759 .562 .000 .359 .939 .245 .006 .004 .569 .000 .359
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.416** .187 .031 1 .201*
-
.004
.138 .421** .175 .136 .176 .109 -
.004
.138
Sig. (2-
tailed)
.000 .063 .759 .045 .967 .172 .000 .081 .179 .080 .282 .967 .172
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
89
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.307** .419**
-
.059
.201* 1 -
.229*
-.012 .396** .389** -.102 .023 .137 -
.229*
-.012
Sig. (2-
tailed)
.002 .000 .562 .045 .022 .902 .000 .000 .313 .818 .173 .022 .902
5
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.008 .025 .643**
-.004 -
.229*
1 .244* .109 .039 .212* .175 -
.091
.975**
.244*
Sig. (2-
tailed)
.937 .803 .000 .967 .022 .014 .279 .698 .034 .081 .367 .000 .014
6
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.159 .053 .093 .138 -
.012
.244*
1 .033 .046 .341** .303** -
.085
.269**
1.000**
Sig. (2-
tailed)
.114 .600 .359 .172 .902 .014 .743 .651 .001 .002 .401 .007 .000
7
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.342** .277**
.008 .421** .396**
.109 .033 1 .250* .133 .201* .361**
.109 .033
Sig. (2-
tailed)
.000 .005 .939 .000 .000 .279 .743 .012 .186 .045 .000 .279 .743
8
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
90
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.102 .968**
.117 .175 .389**
.039 .046 .250* 1 .074 .150 .124 .039 .046
Sig. (2-
tailed)
.314 .000 .245 .081 .000 .698 .651 .012 .464 .135 .220 .698 .651
9
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.037 .077 .273**
.136 -
.102
.212*
.341** .133 .074 1 .189 .197*
.235*
.341**
Sig. (2-
tailed)
.715 .447 .006 .179 .313 .034 .001 .186 .464 .060 .049 .019 .001
1
0
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.163 .152 .288**
.176 .023 .175 .303** .201* .150 .189 1 .287**
.200*
.303**
Sig. (2-
tailed)
.106 .131 .004 .080 .818 .081 .002 .045 .135 .060 .004 .046 .002
1
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.102 .128 .058 .109 .137 -
.091
-.085 .361** .124 .197* .287** 1 -
.044
-.085
Sig. (2-
tailed)
.314 .206 .569 .282 .173 .367 .401 .000 .220 .049 .004 .665 .401
1
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
91
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.008 .025 .643**
-.004 -
.229*
.975**
.269** .109 .039 .235* .200* -
.044
1 .269**
Sig. (2-
tailed)
.937 .803 .000 .967 .022 .000 .007 .279 .698 .019 .046 .665 .007
1
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.159 .053 .093 .138 -
.012
.244*
1.000**
.033 .046 .341** .303** -
.085
.269**
1
Sig. (2-
tailed)
.114 .600 .359 .172 .902 .014 .000 .743 .651 .001 .002 .401 .007
1
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.342** .277**
.008 .421** .396**
.109 .033 1.000**
.250* .133 .201* .361**
.109 .033
Sig. (2-
tailed)
.000 .005 .939 .000 .000 .279 .743 .000 .012 .186 .045 .000 .279 .743
1
5
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.019 .111 .085 .137 .075 .010 .060 .120 .075 .098 .007 .074 -
.019
.060
Sig. (2-
tailed)
.848 .269 .401 .174 .458 .921 .551 .233 .457 .330 .944 .464 .854 .551
1
6
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
92
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.131 .968**
.152 .205* .384**
.068 .078 .287** .957** .112 .150 .119 .068 .078
Sig. (2-
tailed)
.195 .000 .131 .041 .000 .498 .439 .004 .000 .265 .136 .239 .498 .439
1
7
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.124 .076 .139 .060 -
.028
.257**
.700** .125 .065 .348** .326** .014 .284**
.700**
Sig. (2-
tailed)
.218 .450 .169 .551 .779 .010 .000 .215 .520 .000 .001 .887 .004 .000
1
8
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.342** .277**
.008 .421** .396**
.109 .033 1.000**
.250* .133 .201* .361**
.109 .033
Sig. (2-
tailed)
.000 .005 .939 .000 .000 .279 .743 .000 .012 .186 .045 .000 .279 .743
1
9
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.102 .968**
.117 .175 .389**
.039 .046 .250* 1.000**
.074 .150 .124 .039 .046
Sig. (2-
tailed)
.314 .000 .245 .081 .000 .698 .651 .012 .000 .464 .135 .220 .698 .651
2
0
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
93
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.037 .077 .273**
.136 -
.102
.212*
.341** .133 .074 1.000**
.189 .197*
.235*
.341**
Sig. (2-
tailed)
.715 .447 .006 .179 .313 .034 .001 .186 .464 .000 .060 .049 .019 .001
2
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.163 .152 .288**
.176 .023 .175 .303** .201* .150 .189 1.000**
.287**
.200*
.303**
Sig. (2-
tailed)
.106 .131 .004 .080 .818 .081 .002 .045 .135 .060 .000 .004 .046 .002
2
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
1.000**
.131 -
.014
.416** .307**
.008 .159 .342** .102 .037 .163 .102 .008 .159
Sig. (2-
tailed)
.000 .195 .888 .000 .002 .937 .114 .000 .314 .715 .106 .314 .937 .114
2
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.144 .958**
.110 .206* .423**
.043 .066 .324** .926** .123 .198* .176 .043 .066
Sig. (2-
tailed)
.154 .000 .278 .040 .000 .672 .511 .001 .000 .224 .048 .080 .672 .511
2
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
94
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.001 -
.008
.657**
-.034 -
.245*
.975**
.236* .097 .006 .242* .179 -
.061
.975**
.236*
Sig. (2-
tailed)
.991 .933 .000 .739 .014 .000 .018 .338 .950 .015 .075 .548 .000 .018
2
5
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.416** .187 .031 1.000**
.201*
-
.004
.138 .421** .175 .136 .176 .109 -
.004
.138
Sig. (2-
tailed)
.000 .063 .759 .000 .045 .967 .172 .000 .081 .179 .080 .282 .967 .172
2
6
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.319** .427**
-
.093
.222* .952**
-
.257**
-.046 .402** .398** -.140 -.023 .143 -
.257**
-.046
Sig. (2-
tailed)
.001 .000 .359 .027 .000 .010 .652 .000 .000 .165 .819 .156 .010 .652
2
7
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.131 .968**
.152 .205* .384**
.068 .078 .287** .957** .112 .150 .119 .068 .078
Sig. (2-
tailed)
.195 .000 .131 .041 .000 .498 .439 .004 .000 .265 .136 .239 .498 .439
2
8
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
95
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.128 .020 .101 .107 -
.002
.211*
.953** .042 .013 .341** .301** -
.077
.235*
.953**
Sig. (2-
tailed)
.203 .845 .318 .288 .983 .035 .000 .678 .896 .001 .002 .448 .018 .000
2
9
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.342** .277**
.008 .421** .396**
.109 .033 1.000**
.250* .133 .201* .361**
.109 .033
Sig. (2-
tailed)
.000 .005 .939 .000 .000 .279 .743 .000 .012 .186 .045 .000 .279 .743
3
0
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.157 .968**
.084 .194 .439**
.011 .059 .298** .958** .037 .151 .129 .011 .059
Sig. (2-
tailed)
.120 .000 .407 .053 .000 .915 .559 .003 .000 .718 .134 .202 .915 .559
3
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.065 .083 .260**
.147 -
.075
.200*
.351** .160 .060 .990** .191 .202*
.223*
.351**
Sig. (2-
tailed)
.519 .409 .009 .144 .457 .046 .000 .113 .556 .000 .056 .044 .026 .000
3
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
96
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14
Pearson
Correlati
on
.172 .137 .296**
.163 .004 .187 .290** .185 .136 .194 .988** .270**
.187 .290**
Sig. (2-
tailed)
.086 .174 .003 .104 .969 .063 .003 .065 .179 .053 .000 .007 .063 .003
3
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.116 .095 .067 .081 .100 -
.072
-.116 .325** .092 .201* .241* .956**
-
.072
-.116
Sig. (2-
tailed)
.251 .345 .511 .426 .324 .475 .250 .001 .362 .045 .016 .000 .475 .250
3
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.432** .654**
.358**
.473** .403**
.340**
.429** .646** .632** .459** .516** .375**
.355**
.429**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000
T
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
97
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.342** .01
9
.131 .124 .342** .102 .037 .163 1.000**
.144 .001 .416 .319 .131
Sig. (2-
tailed)
.000 .84
8
.195 .218 .000 .314 .715 .106 .000 .154 .991 .000 .001 .195
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.277** .11
1
.968** .076 .277** .968** .077 .152 .131 .958**
-
.008
.187 .427 .968
Sig. (2-
tailed)
.005 .26
9
.000 .450 .005 .000 .447 .131 .195 .000 .933 .063 .000 .000
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.008 .08
5
.152 .139 .008 .117 .273** .288** -.014 .110 .657**
.031 -
.093
.152
Sig. (2-
tailed)
.939 .40
1
.131 .169 .939 .245 .006 .004 .888 .278 .000 .759 .359 .131
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.421** .13
7
.205* .060 .421** .175 .136 .176 .416** .206*
-
.034
1.000**
.222 .205
Sig. (2-
tailed)
.000 .17
4
.041 .551 .000 .081 .179 .080 .000 .040 .739 .000 .027 .041
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
98
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.396** .07
5
.384** -
.028
.396** .389** -.102 .023 .307** .423**
-
.245*
.201** .952**
.384
Sig. (2-
tailed)
.000 .45
8
.000 .779 .000 .000 .313 .818 .002 .000 .014 .045 .000 .000
5
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.109 .01
0
.068 .257**
.109 .039 .212* .175 .008 .043 .975**
-.004 -
.257
.068**
Sig. (2-
tailed)
.279 .92
1
.498 .010 .279 .698 .034 .081 .937 .672 .000 .967 .010 .498
6
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.033 .06
0
.078 .700**
.033 .046 .341** .303** .159 .066 .236*
.138 -
.046
.078
Sig. (2-
tailed)
.743 .55
1
.439 .000 .743 .651 .001 .002 .114 .511 .018 .172 .652 .439
7
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
1.000**
.12
0
.287** .125 1.000**
.250* .133 .201* .342** .324**
.097 .421** .402**
.287
Sig. (2-
tailed)
.000 .23
3
.004 .215 .000 .012 .186 .045 .000 .001 .338 .000 .000 .004
8
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
99
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.250* .07
5
.957** .065 .250* 1.000**
.074 .150 .102 .926**
.006 .175 .398**
.957
Sig. (2-
tailed)
.012 .45
7
.000 .520 .012 .000 .464 .135 .314 .000 .950 .081 .000 .000
9
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.133 .09
8
.112 .348**
.133 .074 1.000**
.189 .037 .123 .242*
.136 -
.140
.112**
Sig. (2-
tailed)
.186 .33
0
.265 .000 .186 .464 .000 .060 .715 .224 .015 .179 .165 .265
1
0
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.201* .00
7
.150 .326**
.201* .150 .189 1.000**
.163 .198*
.179 .176 -
.023
.150**
Sig. (2-
tailed)
.045 .94
4
.136 .001 .045 .135 .060 .000 .106 .048 .075 .080 .819 .136
1
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.361** .07
4
.119 .014 .361** .124 .197* .287** .102 .176 -
.061
.109 .143 .119
Sig. (2-
tailed)
.000 .46
4
.239 .887 .000 .220 .049 .004 .314 .080 .548 .282 .156 .239
1
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
100
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.109 -
.01
9
.068 .284**
.109 .039 .235* .200* .008 .043 .975**
-.004 -
.257
.068**
Sig. (2-
tailed)
.279 .85
4
.498 .004 .279 .698 .019 .046 .937 .672 .000 .967 .010 .498
1
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.033 .06
0
.078 .700**
.033 .046 .341** .303** .159 .066 .236*
.138 -
.046
.078
Sig. (2-
tailed)
.743 .55
1
.439 .000 .743 .651 .001 .002 .114 .511 .018 .172 .652 .439
1
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
1 .12
0
.287** .125 1.000**
.250* .133 .201* .342** .324**
.097 .421** .402**
.287
Sig. (2-
tailed)
.23
3
.004 .215 .000 .012 .186 .045 .000 .001 .338 .000 .000 .004
1
5
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.120 1 .111 .125 .120 .075 .098 .007 .019 .076 .030 .137 .038 .111
Sig. (2-
tailed)
.233 .271 .216 .233 .457 .330 .944 .848 .452 .765 .174 .709 .271
1
6
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
101
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.287** .11
1
1 .096 .287** .957** .112 .150 .131 .927**
.036 .205 .395**
1.00
0
Sig. (2-
tailed)
.004 .27
1
.344 .004 .000 .265 .136 .195 .000 .721 .041 .000 .000
1
7
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.125 .12
5
.096 1 .125 .065 .348** .326** .124 .097 .254*
.060 -
.059
.096
Sig. (2-
tailed)
.215 .21
6
.344 .215 .520 .000 .001 .218 .335 .011 .551 .559 .344
1
8
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
1.000**
.12
0
.287** .125 1 .250* .133 .201* .342** .324**
.097 .421** .402**
.287
Sig. (2-
tailed)
.000 .23
3
.004 .215 .012 .186 .045 .000 .001 .338 .000 .000 .004
1
9
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.250* .07
5
.957** .065 .250* 1 .074 .150 .102 .926**
.006 .175 .398**
.957
Sig. (2-
tailed)
.012 .45
7
.000 .520 .012 .464 .135 .314 .000 .950 .081 .000 .000
2
0
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
102
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.133 .09
8
.112 .348**
.133 .074 1 .189 .037 .123 .242*
.136 -
.140
.112**
Sig. (2-
tailed)
.186 .33
0
.265 .000 .186 .464 .060 .715 .224 .015 .179 .165 .265
2
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.201* .00
7
.150 .326**
.201* .150 .189 1 .163 .198*
.179 .176 -
.023
.150**
Sig. (2-
tailed)
.045 .94
4
.136 .001 .045 .135 .060 .106 .048 .075 .080 .819 .136
2
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.342** .01
9
.131 .124 .342** .102 .037 .163 1 .144 .001 .416** .319 .131
Sig. (2-
tailed)
.000 .84
8
.195 .218 .000 .314 .715 .106 .154 .991 .000 .001 .195
2
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.324** .07
6
.927** .097 .324** .926** .123 .198* .144 1 .009 .206 .431**
.927
Sig. (2-
tailed)
.001 .45
2
.000 .335 .001 .000 .224 .048 .154 .931 .040 .000 .000
2
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
103
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.097 .03
0
.036 .254*
.097 .006 .242* .179 .001 .009 1 -.034 -
.273
.036**
Sig. (2-
tailed)
.338 .76
5
.721 .011 .338 .950 .015 .075 .991 .931 .739 .006 .721
2
5
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.421** .13
7
.205* .060 .421** .175 .136 .176 .416** .206*
-
.034
1** .222 .205
Sig. (2-
tailed)
.000 .17
4
.041 .551 .000 .081 .179 .080 .000 .040 .739 .027 .041
2
6
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.402** .03
8
.395** -
.059
.402** .398** -.140 -.023 .319** .431**
-
.273**
.222** 1** .395
Sig. (2-
tailed)
.000 .70
9
.000 .559 .000 .000 .165 .819 .001 .000 .006 .027 .000
2
7
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.287** .11
1
1.000**
.096 .287** .957** .112 .150 .131 .927**
.036 .205 .395**
1
Sig. (2-
tailed)
.004 .27
1
.000 .344 .004 .000 .265 .136 .195 .000 .721 .041 .000
2
8
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
104
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.042 .13
4
.046 .659**
.042 .013 .341** .301** .128 .033 .251*
.107 -
.036
.046
Sig. (2-
tailed)
.678 .18
4
.652 .000 .678 .896 .001 .002 .203 .748 .012 .288 .722 .652
2
9
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
1.000**
.12
0
.287** .125 1.000**
.250* .133 .201* .342** .324**
.097 .421** .402**
.287
Sig. (2-
tailed)
.000 .23
3
.004 .215 .000 .012 .186 .045 .000 .001 .338 .000 .000 .004
3
0
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.298** .09
3
.916** .086 .298** .958** .037 .151 .157 .926**
-
.023
.194 .447**
.916
Sig. (2-
tailed)
.003 .35
9
.000 .394 .003 .000 .718 .134 .120 .000 .821 .053 .000 .000
3
1
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.160 .10
8
.099 .362**
.160 .060 .990** .191 .065 .129 .229*
.147 -
.114
.099**
Sig. (2-
tailed)
.113 .28
4
.329 .000 .113 .556 .000 .056 .519 .200 .022 .144 .257 .329
3
2
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
105
P15 P16 P17 P18 P19 P20 P21 P22 P23 P24 P25 P26 P27 P28
Pearson
Correlati
on
.185 .01
7
.135 .313**
.185 .136 .194 .988** .172 .183 .190 .163 -
.043
.135**
Sig. (2-
tailed)
.065 .86
6
.179 .001 .065 .179 .053 .000 .086 .068 .058 .104 .669 .179
3
3
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.325** .09
2
.088 -
.015
.325** .092 .201* .241* .116 .143 -
.043
.081 .105 .088
Sig. (2-
tailed)
.001 .36
0
.386 .879 .001 .362 .045 .016 .251 .156 .672 .426 .300 .386
3
4
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Pearson
Correlati
on
.646** .20
3*
.668** .437**
.646** .632** .459** .516** .432** .683**
.330**
.473** .382**
.668**
Sig. (2-
tailed)
.000 .04
3
.000 .000 .000 .000 .000 .000 .000 .000 .001 .000 .000 .000
T
N 100 100 100 100 100 100 100 100 100 100 100 100 100 100
106
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.128** .342**
.157 .065 .172** .116 .432
Sig. (2-
tailed)
.203 .000 .120 .519 .086 .251 .000
1
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.020 .277**
.968 .083 .137** .095**
.654
Sig. (2-
tailed)
.845 .005 .000 .409 .174 .345 .000
2
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.101 .008 .084**
.260 .296 .067 .358**
Sig. (2-
tailed)
.318 .939 .407 .009 .003 .511 .000
3
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.107 .421*
.194 .147 .163** .081 .473
Sig. (2-
tailed)
.288 .000 .053 .144 .104 .426 .000
4
N 100 100 100 100 100 100 100
107
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
-.002* .396 .439*
-
.075
.004** .100**
.403
Sig. (2-
tailed)
.983 .000 .000 .457 .969 .324 .000
5
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.211 .109*
.011 .200*
.187 -
.072
.340*
Sig. (2-
tailed)
.035 .279 .915 .046 .063 .475 .001
6
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.953 .033 .059*
.351 .290 -
.116
.429**
Sig. (2-
tailed)
.000 .743 .559 .000 .003 .250 .000
7
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.042** 1.00
0**
.298 .160 .185 .325*
.646
Sig. (2-
tailed)
.678 .000 .003 .113 .065 .001 .000
8
N 100 100 100 100 100 100 100
108
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.013 .250**
.958 .060 .136* .092 .632
Sig. (2-
tailed)
.896 .012 .000 .556 .179 .362 .000
9
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.341 .133 .037*
.990**
.194 .201 .459
Sig. (2-
tailed)
.001 .186 .718 .000 .053 .045 .000
10
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.301 .201 .151 .191**
.988* .241 .516
Sig. (2-
tailed)
.002 .045 .134 .056 .000 .016 .000
11
N 100 100 100 100 100 100 100
Pearson
Correlati
on
-.077 .361 .129 .202 .270** .956 .375*
Sig. (2-
tailed)
.448 .000 .202 .044 .007 .000 .000
12
N 100 100 100 100 100 100 100
109
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.235 .109*
.011**
.223**
.187 -
.072
.355*
Sig. (2-
tailed)
.018 .279 .915 .026 .063 .475 .000
13
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.953 .033 .059*
.351**
.290 -
.116
.429**
Sig. (2-
tailed)
.000 .743 .559 .000 .003 .250 .000
14
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.042** 1.00
0**
.298 .160 .185** .325*
.646
Sig. (2-
tailed)
.678 .000 .003 .113 .065 .001 .000
15
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.134 .120 .093 .108 .017 .092 .203
Sig. (2-
tailed)
.184 .233 .359 .284 .866 .360 .043
16
N 100 100 100 100 100 100 100
110
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.046* .287**
.916 .099 .135** .088**
.668
Sig. (2-
tailed)
.652 .004 .000 .329 .179 .386 .000
17
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.659 .125 .086**
.362**
.313 -
.015
.437**
Sig. (2-
tailed)
.000 .215 .394 .000 .001 .879 .000
18
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.042** 1.00
0**
.298 .160 .185** .325*
.646
Sig. (2-
tailed)
.678 .000 .003 .113 .065 .001 .000
19
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.013 .250**
.958 .060 .136* .092**
.632
Sig. (2-
tailed)
.896 .012 .000 .556 .179 .362 .000
20
N 100 100 100 100 100 100 100
111
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.341 .133 .037*
.990**
.194 .201 .459**
Sig. (2-
tailed)
.001 .186 .718 .000 .053 .045 .000
21
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.301 .201 .151 .191**
.988* .241 .516
Sig. (2-
tailed)
.002 .045 .134 .056 .000 .016 .000
22
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.128** .342**
.157 .065 .172** .116 .432
Sig. (2-
tailed)
.203 .000 .120 .519 .086 .251 .000
23
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.033* .324**
.926 .129 .183** .143**
.683
Sig. (2-
tailed)
.748 .001 .000 .200 .068 .156 .000
24
N 100 100 100 100 100 100 100
112
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.251 .097*
-
.023**
.229*
.190 -
.043
.330*
Sig. (2-
tailed)
.012 .338 .821 .022 .058 .672 .001
25
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.107** .421*
.194 .147 .163** .081 .473
Sig. (2-
tailed)
.288 .000 .053 .144 .104 .426 .000
26
N 100 100 100 100 100 100 100
Pearson
Correlati
on
-.036* .402**
.447**
-
.114
-.043** .105**
.382
Sig. (2-
tailed)
.722 .000 .000 .257 .669 .300 .000
27
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.046* .287**
.916 .099 .135** .088**
.668
Sig. (2-
tailed)
.652 .004 .000 .329 .179 .386 .000
28
N 100 100 100 100 100 100 100
113
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
1 .042 .026*
.350**
.287 -
.109
.408**
Sig. (2-
tailed)
.678 .798 .000 .004 .282 .000
29
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.042** 1** .298 .160 .185** .325*
.646
Sig. (2-
tailed)
.678 .003 .113 .065 .001 .000
30
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.026 .298**
1 .063 .136** .097**
.645
Sig. (2-
tailed)
.798 .003 .531 .178 .339 .000
31
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.350 .160 .063*
1** .196 .206 .473**
Sig. (2-
tailed)
.000 .113 .531 .050 .040 .000
32
N 100 100 100 100 100 100 100
114
P29 P30 P31 P32 P33 P34 Skor total
Pearson
Correlati
on
.287 .185 .136 .196**
1 .270 .503
Sig. (2-
tailed)
.004 .065 .178 .050 .007 .000
33
N 100 100 100 100 100 100 100
Pearson
Correlati
on
-.109 .325 .097 .206 .270** 1 .337*
Sig. (2-
tailed)
.282 .001 .339 .040 .007 .001
34
N 100 100 100 100 100 100 100
Pearson
Correlati
on
.408** .646**
.645**
.473**
.503** .337**
1**
Sig. (2-
tailed)
.000 .000 .000 .000 .000 .001
T
N 100 100 100 100 100 100 100
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
115