Understanding Audio Recording Formats

Date: January 7, 2019 Author: Isaac Comments: 1 Reply

…or why I always recommend using the LPCM audio recording format to capture your research data. And there are many audio recording formats, but we are going to focus on the mp3 and the WAV/LPCM recording formats. Why? Because these two format are currently the most popular audio recording format that you’ll find on most digital voice recorders. Also WAV and mp3 recording formats are great, ubiquitous examples of compressed and uncompressed audio recording formats. But before we get to that. Let’s first lay the foundation for understanding audio formats. And for that we need to understand 3 key features of recorded audio; sampling rate, bit depth, and bit rate. And since this is a basic guide to understanding audio recording format, I’ll also briefly touch on audio channels (mono vs stereo) and digital sound quantization. Audio Channels; Mono vs Stereo The easiest way to understand audio channels is to think of tracks or traffic… Continue reading…

7 Steps for Managing Qualitative Data

Date: August 25, 2015 Author: Isaac Comments: Leave a reply

Here’s a more concise recent post on how to manage qualitative data. This post presents an overview of the unique challenges for managing a qualitative research database and includes 7 steps for aiding the creation of an orderly and coherent database. These steps will assist greatly in ensuring that your research study is successful, that analysis is credible, and data is not compromised. Quantitative databases versus qualitative databases Quantitative data is cleaned up and recorded before analysis takes place. However, with qualitative data cleanup and analysis proceeds in tandem. In small research projects the primary researcher often handles transcription and achieving this tandem process is less problematic. In large-scale research, however, there is a division of labor involved which means data preparation is separated from analysis. Thus, developing a rigorous database management protocol is essential for two reasons. First, standardized transcripts prevent the production of incompatible data products. Second, it reduces the chance that analysis… Continue reading…

Intelligent Verbatim Transcription

Date: June 9, 2015 Author: Isaac Comments: 1 Reply

“[A] transcript is a text that ‘re’-presents an event; it is not the event itself. Following this logic, what is re-presented is data constructed by a researcher for a particular purpose, not just talk written down.” (GREEN et al. 1997, p.172) Intelligent verbatim transcription, also known as clean transcripts, is a popular style of transcription that represents recorded speech (here is a great post on recording your interviews) into text while editing out the fillers and repetitions that may distract from getting at the content of the interview. And this is because interview subjects do not speak coherently in normal speech. We repeat words, have a lot of filler expressions such as ah, um, pause to think and use words like yeah, you know as a way to express ourselves or creating emphasis. An intelligent verbatim transcript attempts to capture what was said rather than how it was said. The main priority being content and… Continue reading…

7 Principles for Developing Transcription Rules

Date: September 18, 2013 Author: Isaac Comments: 2 Replies

Poor quality transcriptions can significantly delay the progress of a research project, so it is essential that best practices are kept in mind at all times. This post explores the intricacies of developing transcription rules that work for one’s specific research project before outlining seven principles that help guide researchers in this endeavor. Developing Rules That Meet your Specific Needs There is no universal transcription protocol that will work for all research projects, theoretical frameworks or data collection approaches. However, there are a few practical considerations that can help guide the development of rules that fit your specific research project. Firstly, transcription rules should facilitate the systematic organization of qualitative data, thereby allowing systematic and orderly Qualitative Data Analysis (QDA). This is the primary goal of establishing transcription protocols and applies regardless of what specific analytical tools are being used. The aim of establishing transcription rules is to ensure a high level of confidence that… Continue reading…