CONTOH Hipotesis Penelitian dan Hipotesis Statistik

Contoh HIPOTESIS DESKRIPTIF:

80% mahasiswa Unpar berasal dari keluarga berpendapatan menengah ke atas.

Tindakan agresif banyak dilakukan oleh anak yang berasal dari keluarga broken home.

Orang yang berpendidikan tinggi relatif lebih mudah menerima perubahan.

Terjadi peningkatan jumlah masyarakat yang hidup di bawah garis kemiskinan semenjak dinaikkannya harga BBM

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Contoh HIPOTESIS ASOSIATIF/KORELASIONAL:

>>> klik sini untuk perbedaan PENGARUH & HUBUNGAN <<<

Hipotesis PENELITIAN:
Ada hubungan antara usia dan kepuasan kerja.

Hipotesis STATISTIK:
H0: ρ = 0

H1: ρ ≠ 0

PENTING: Hipotesis statistik HANYA DIGUNAKAN jika kita mengambil sampel dari populasi, diuji menggunakan STATISTIK INFERENSIAL, yang tujuannya untuk menguji apakah sampel mewakili populasi atau tidak. Hipotesis statistik TIDAK WAJIB dilakukan jika: [1] kita mengambil data dari populasi (sensus), atau [2] kita tidak ingin melakukan generalisasi untuk membuktikan apakah sampel mewakili populasi atau tidak.

Terdapat hubungan positif antara kepuasan konsumen dan loyalitas konsumen.
H0: ρ ≤ 0
H1: ρ > 0

Ada hubungan antara tingkat kerajinan mahasiswa dan nilai yang diperoleh: semakin rajin mahasiswa, nilai yang diperoleh juga akan semakin baik.
H0: ρ ≤ 0
H1: ρ > 0

Ada hubungan antara lama antrian dengan kepuasan pelanggan: semakin lama suatu antrian, kepuasan pelanggan juga akan semakin rendah.
H0: ρ ≥ 0
H1: ρ < 0

Catatan: Mengapa H0 disebut hipotesis nol/null hypothesis?! Karena tanda ‘= 0’ (baca: sama dengan nol) HARUS diletakkan pada H0. Dengan me-reject H0, maka kita akan menerima H1, artinya: Ada hubungan/pengaruh!!! Jika kita menerima H0, besarnya pengaruh/hubungan akan sama dengan nol. Sebagai contoh, dalam kasus regresi sebagai berikut: Y = 1,2 + 0X, karena besarnya pengaruh β adalah sama dengan nol, maka berapa pun nilai X yang dimasukkan, Y akan bernilai 1,2.

Tujuan dari uji hipotesis adalah menerima H1. Untuk mengurangi kesalahan, biasakan memulai membuat hipotesis statistik dari H1 dulu, lalu hal-hal yang belum tercantum di H1 kita masukan menjadi H0. Sebagai contoh, jika di H1 kita memasukkan tanda ≠, maka di H0 kita harus memasukkan =. Jika di H1 kita memasukkan tanda >, maka di Ho kita harus memasukkan hal-hal yang belum dijelaskan di H1, yaitu < dan =. Mudah kan?!

Banyak kesalahan-kesalahan terjadi dengan menuliskan hipotesis statistik sebagai berikut:

Ho: ρ > 0 // ρ < 0 // ρ > 0
H1: ρ ≤ 0 // ρ ≥ 0 // ρ < 0

Apakah kalian tahu di mana letak kesalahannya?

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Contoh HIPOTESIS KAUSAL:

Ada pengaruh antara tingkat awareness dengan knowledge konsumen.
Ho: β = 0
H1: β ≠ 0

Angka yang bukan nol nilainya bisa positif, bisa juga negatif. Digunakan untuk NON-DIRECTIONAL HYPOTHESES. Dengan me-reject H0, pengaruhnya mungkin positif, mungkin juga negatif.

Perhatikan baik-baik hipotesis berikut:

Ada pengaruh antara kepuasan kerja dengan produktivitas karyawan.
Ho: β = 0
H1: β ≠ 0

Dengan me-reject H0, berarti H1 diterima: Ada pengaruh antara kepuasan kerja dengan produktivitas karyawan, namun kita tidak tahu pengaruhnya positif atau negatif. Jika hasil regresi memunculkan persamaan sebagai berikut: Y = 20 – 3X, maka kita akan menerima H1 karena β = -3 ≠ 0, artinya: dengan kenaikan kepuasan kerja sebesar 1 akan menurunkan produktivitas karyawan sebesar 3 (pengaruhnya negatif), padahal seperti yang kita semua tahu bahwa semakin tinggi kepuasan kerja, produktivitas karyawan juga akan meningkat (pengaruhnya positif). Apakah hal ini benar? Bandingkan dengan hipotesis berikut:

Ada pengaruh positif antara kepuasan kerja dengan produktivitas karyawan.
Ho: β ≤ 0
H1: β > 0

Jika kita menggunakan DIRECTIONAL HYPOTHESES, dengan persamaan regresi yang sama: Y = 20 – 3X, kita tentu akan menerima H0 karena nilai β = -3 < 0. Itulah alasannya kenapa saya selalu NGOTOT UNTUK MENGGUNAKAN DIRECTIONAL HYPOTHESIS.

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Contoh HIPOTESIS PERBEDAAN:

Ada perbedaan motivasi kerja antara pria dan wanita.
H0: μp = μw // H0: μp – μw = 0
H1: μp ≠ μw // H1: μp – μw ≠ 0

Ada perbedaan motivasi kerja antara pria dan wanita, dimana wanita lebih bermotivasi dalam bekerja daripada pria.
H0: μp ≥ μw // H0: μp – μw ≥ 0
H1: μp < μw // H1: μp – μw < 0

Ada perbedaan pengaruh insentif finansial dan non finansial terhadap unjuk kerja. Insentif finansial lebih berpengaruh terhadap peningkatan unjuk kerja pegawai dibandingkan dengan insentif non finansial.
H0: βF ≤ βNF // H0: βF – βNF ≤ 0
H1: βF > βNF // H1: βF – βNF > 0

>>> Klik sini untuk Pengertian & Uji Hipotesis <<<

———

CATATAN:

Untuk menghitung besarnya hubungan, kita menggunakan korelasi yang memiliki koefisien korelasi r untuk sampel dan ρ untuk populasi.

Untuk menghitung besarnya pengaruh, kita menggunakan regresi dengan persamaan Y = a + bX untuk sampel dan Y = α + βX untuk populasi. Besarnya pengaruh dilambangkan dengan b atau β.

Untuk uji beda, kita akan menguji rata-rata hitung yang dilambangkan dengan X-bar untuk sampel dan μ untuk populasi.

Dalam penelitian, sangat diharapkan agar sampel yang diambil dapat mewakili populasi, karenanya dalam membuat hipotesis statistik, umumnya kita akan menggunakan parameter populasi.

>>> Klik sini untuk Cara Membuat Hipotesis <<<

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34 Responses to “CONTOH Hipotesis Penelitian dan Hipotesis Statistik”

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    terima kasih, anda telah banyak membantu saya.

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    kak, bisa tolong agak lebih rinci dalam hipotesis deskriptif dalam variabel bebas&terikatnya, indikator dan data perbedaan jelasnya seperti apa ya? saya masih kesulitan.. tq

    • fort says:

      kak, bisa tolong agak lebih rinci dalam hipotesis deskriptif dalam variabel bebas&terikatnya, indikator dan data perbedaan jelasnya seperti apa ya? saya masih kesulitan.. tq

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