JENIS DATA PENELITIAN I Data kualitatif qualitative data

JENIS DATA PENELITIAN I. Data kualitatif (qualitative data) II. Data kuantitatif (quantitative data) November 23, 2020 Indawan Syahri 1

DATA KUALITATIF Peneliti adalah instrumen kunci A. FIELDNOTES (catatan lapangan) B. TRANSCRIPTS from TAPED INTERVIEWS (transkip wawancara) C. DOCUMENTS (dokummen) D. PHOTOGRAPHIES (gambar/foto) Bogdan & Biklen (2000) November 23, 2020 Indawan Syahri 2

A. FIELDNOTES • Data tertulis - apa yang peneliti dengar, lihat, alami, dan pikirkan • Deskripsi - orang, objek, tempat, kejadian, aktifitas, dan percakapan • Catatan lapangan merupakan alat yang utama pengumpul data dalam observasi – participant observation – nonparticipant observation. • Catatan lapangan – data tambahan – Data rekaman wawancara tidak merekam konteks yang berhungunag dengan penglihatan, impresi atau penilaian. November 23, 2020 Indawan Syahri 3

ISI FIELDNOTES (1) 1. DESCRIPTIVE FIELDNOTES – Gambaran subjek: Penampilan fisik, Cara berpakaian, Cara bertindak, Gaya berbicara, dan Aktivitas. . – Rekonstruksi dialog: Parafrase, Ringkasan percakapan, Bahasa tubuh, Aksen, dan Raut muka – Deskripsi tata fisik Contoh: pengaturan perabot ruangan – Catatan kejadian tertentu. – Catatan activitas • Deskripsi tingka laku secara detail – Observer’s behavior: Tingka laku, Assumsi, dll November 23, 2020 Indawan Syahri 4

ISI FIELDNOTES (2) 2. REFLECTIVE FIELDNOTES - observer’s comment “O. C. ” – Refleksi analisis – Refleksi metode – Refleksi dilema dan konflik – Klarifikasi November 23, 2020 Indawan Syahri 5

B. TRANSKRIP WAWANCARA • Transkrip – data utama wawancara • Transkrip - cakupan: – – – Siapa yang diwawancara Waktu wawancara Tempat wawancara Informasi penting lain Judul wawancara, contoh: “The first Day of School”. November 23, 2020 Indawan Syahri 6

C. DOKUMEN • Dokumen personal – diaries – personal letters – autobiography • Dokumen Resmi – – Dokumen internal Komunikasi eksternal Raport siswa Arsip personalia • Dokumen Populer – – – November 23, 2020 Video Film Majalah Televisi Iklan Indawan Syahri 7

D. GAMBAR /FOTO • GAMBAR TERSEDIA (Found photographs) 1. Memperlihatkan sosok individu atau kejadian yang diteliti 2. Memberikan informasi faktual 3. Memberikan gambaran sejarah • GAMBAR YANG DIAMBIL LANGSUNG (Research-produced photographs) – simplify the collection of factual information – better grasp population distribution November 23, 2020 Indawan Syahri 8

DATA KUANTITATIF A. Statistik Deskriptif B. Statistik Inferensial November 23, 2020 Indawan Syahri 9

MEASURE OF CENTRAL TENDENCY MODE (the most frequently occurring scores) MEDIAN (the middle score) MEAN (the average of all scores) 10

Measures of variability • In order to describe the distribution of interval data, the measure of central tendency will not suffice. • To describe the data more accurately, we have to measure the degree of variability of the data from the measure of central tendency. • There are 3 ways to show the data are spread out from the point, i. e. range, variance, and standard deviation. 11

MEASURES OF VARIABILITY RANGE (the highest minus the lowest score) VARIANCE (the square of Standard deviation STANDARD DEVIATION (the square root of variance 12

Range • Range = X highest – X lowest • E. g. The youngest student is 17 and the oldest is 42, Range = 42 – 17 = 15 The age range in this class is 25. • If range is so unstable, some researchers prefer to stabilize it by using the semi-interquartile range (SIQR) SIQR = Q 3 – Q 1 / 2 Q 3 is the score at the 75 th percentile and Q 1 is the score at the 25 th percentile. • E. g. , the score of the toefl score at the 75 th percentile is 560 and 470 is the score at the 25 th percentile. SIQR is 560 – 470 / 2 = 45 13

Variance • • To see how close the scores are to the average for the test. E. g. , if the mean score on the exam was 93. 5 and a student got 89, the deviation of the score from the mean is 4. 5. If we want a measure that takes the distribution of all scores into account, it is variance. To compute variance, we begin with the deviation of the individual scores from the mean. Stages: 1. Compute the mean: X 2. Subtract the mean from each score to obtain the individual deviation scores x = X – X. 3. Square each individual deviation and add: ∑ x² 4. Divide by N – 1: ∑ x²/ N - 1 14

Statistik inferensial (1) Statistik – verifikasi teori– uji hipotesis 1. Statistik parametrik a. Uji hubungan Contoh: • Korelasi product moment • Korelasi regresi b. Uji beda Contoh: • Tes-t • Anova November 23, 2020 Indawan Syahri 15

Statistik inferensial (2) 2. Statistik non-parametrik a. Uji hubungan Contoh: • Korelasi tata jenjang • Korelasi biserial b. Uji beda Contoh: • November 23, 2020 Chi square Indawan Syahri 16