JENIS DATA PENELITIAN Data kualitatif (qualitative data) Data kuantitatif (quantitative data) January 11, 2019 Indawan Syahri

Peneliti adalah instrumen kunci DATA KUALITATIF Peneliti adalah instrumen kunci FIELDNOTES (catatan lapangan) TRANSCRIPTS from TAPED INTERVIEWS (transkip wawancara) DOCUMENTS (dokummen) PHOTOGRAPHIES (gambar/foto) Bogdan & Biklen (2000) January 11, 2019 Indawan Syahri

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. January 11, 2019 Indawan Syahri

ISI FIELDNOTES (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 January 11, 2019 Indawan Syahri

ISI FIELDNOTES (2) 2. REFLECTIVE FIELDNOTES - observer’s comment “O.C.” Refleksi analisis Refleksi metode Refleksi dilema dan konflik Klarifikasi January 11, 2019 Indawan Syahri

Transkrip – data utama wawancara Transkrip - cakupan: 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”. January 11, 2019 Indawan Syahri

DOKUMEN Dokumen personal Dokumen Resmi Dokumen Populer diaries personal letters autobiography Dokumen Resmi Dokumen internal Komunikasi eksternal Raport siswa Arsip personalia Dokumen Populer Video Film Majalah Televisi Iklan January 11, 2019 Indawan Syahri

GAMBAR /FOTO GAMBAR TERSEDIA (Found photographs) Memperlihatkan sosok individu atau kejadian yang diteliti Memberikan informasi faktual Memberikan gambaran sejarah GAMBAR YANG DIAMBIL LANGSUNG (Research-produced photographs) simplify the collection of factual information better grasp population distribution January 11, 2019 Indawan Syahri

DATA KUANTITATIF Statistik Deskriptif Statistik Inferensial January 11, 2019 Indawan Syahri

MEDIAN (the middle score) MEAN (the average of all scores) MEASURE 11/01/2019 MEASURE OF CENTRAL TENDENCY MODE (the most frequently occurring scores) MEDIAN (the middle score) MEAN (the average of all scores) Indawan Syahri

Measures of variability 11/01/2019 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 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. Indawan Syahri

MEASURES OF VARIABILITY 11/01/2019 MEASURES OF VARIABILITY RANGE (the highest minus the lowest score) VARIANCE (the square of Standard deviation STANDARD DEVIATION (the square root of variance Indawan Syahri

Range Range = X highest – X lowest 11/01/2019 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 = Q3 – Q1 / 2 Q3 is the score at the 75th percentile and Q1 is the score at the 25th percentile. E.g., the score of the toefl score at the 75th percentile is 560 and 470 is the score at the 25th percentile. SIQR is 560 – 470 / 2 = 45 Indawan Syahri

Variance To see how close the scores are to the average for the test. 11/01/2019 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: Compute the mean: X Subtract the mean from each score to obtain the individual deviation scores x = X – X. Square each individual deviation and add: ∑ x² Divide by N – 1: ∑ x²/ N - 1 Indawan Syahri

Statistik inferensial (1) Statistik – verifikasi teori– uji hipotesis Statistik parametrik Uji hubungan Contoh: Korelasi product moment Korelasi regresi Uji beda Tes-t Anova January 11, 2019 Indawan Syahri

Statistik inferensial (2) Statistik non-parametrik Uji hubungan Contoh: Korelasi tata jenjang Korelasi biserial Uji beda Chi square January 11, 2019 Indawan Syahri