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Memos, Memoing
Memos are researchers' records of analysis. Although they take time to write, they are worth doing. Memos are a way of keeping the researcher aware and the research honest. All too often, researchers take shortcuts to the analytic process, writing notes in margins of transcripts, trusting their memories to fill in the details later when writing. Unfortunately, memories often fail, and the “golden nuggets” of insight that emerged during analysis are lost. Furthermore, memos are useful for trying out analytic ideas and working out the logic of the emergent findings. As memos grow in depth and breath, researchers can use them to note areas that need further investigation to round out the findings. When working in teams, memos can be used to keep everyone informed and to share ideas (Huberman & Miles, 1994). During the writing stage, researchers only have to turn to their memos to work out an outline.
There are no hard-and-fast rules for writing memos. However, here are a few suggestions for improving the quality of memos and enhancing their usefulness. Memos should be titled and dated. They should be analytic rather than descriptive, that is, focused on concepts that emerged during the analysis and not filled with raw data (Dey, 1993). With the computer programs that are available, one can mark the raw data from which concepts emerged and refer back to the items when necessary (Richards & Richards, 1994). Memos should ask questions of the data, such as, “Where do I go from here to get more data on this concept, or what should I be thinking about when doing my next observation or interview?” As more data about a concept emerge, memos should explore concepts in terms of their properties and dimensions, such as who, what, when, where, how, why, and so on. The researcher should keep a piece of paper and a pencil available at all times, even by the bedside. One never knows when that sudden insight will appear, when the pieces of the puzzle will fall into place. Analysts should not let a lot of time elapse between doing the analysis and writing the memos. Not only do thoughts get lost, so does momentum. Analysts should not be afraid to be creative and write down those insights, even if they seem far-fetched at the time. One can always toss them out if they are not verified by data. Finally, memos can be used to keep an account of one's own response to the research process, including the personal biases and assumptions that inevitably shape the tone and outcome of analysis. This record keeping will prove useful later when writing.
Writing memos stimulates thinking and allows one to interact with data in ways that foster creativity while staying grounded at the same time. At the beginning of a research project, memos will appear more exploratory and directive than analytic. Often, they seem foolish to the researcher when looking back. This is to be expected. Doing research is progressive and an act of discovery, a process one can only keep track of in memos.
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- Analysis of Variance
- Association and Correlation
- Association
- Association Model
- Asymmetric Measures
- Biserial Correlation
- Canonical Correlation Analysis
- Correlation
- Correspondence Analysis
- Intraclass Correlation
- Multiple Correlation
- Part Correlation
- Partial Correlation
- Pearson's Correlation Coefficient
- Semipartial Correlation
- Simple Correlation (Regression)
- Spearman Correlation Coefficient
- Strength of Association
- Symmetric Measures
- Basic Qualitative Research
- Basic Statistics
- F Ratio
- N(n)
- t-Test
- X¯
- Y Variable
- z-Test
- Alternative Hypothesis
- Average
- Bar Graph
- Bell-Shaped Curve
- Bimodal
- Case
- Causal Modeling
- Cell
- Covariance
- Cumulative Frequency Polygon
- Data
- Dependent Variable
- Dispersion
- Exploratory Data Analysis
- Frequency Distribution
- Histogram
- Hypothesis
- Independent Variable
- Measures of Central Tendency
- Median
- Null Hypothesis
- Pie Chart
- Regression
- Standard Deviation
- Statistic
- Causal Modeling
- Discourse/Conversation Analysis
- Econometrics
- Epistemology
- Ethnography
- Evaluation
- Event History Analysis
- Experimental Design
- Factor Analysis and Related Techniques
- Feminist Methodology
- Generalized Linear Models
- Historical/Comparative
- Interviewing in Qualitative Research
- Latent Variable Model
- Life History/Biography
- Log-Linear Models (Categorical Dependent Variables)
- Longitudinal Analysis
- Mathematics and Formal Models
- Measurement Level
- Measurement Testing and Classification
- Multilevel Analysis
- Multiple Regression
- Qualitative Data Analysis
- Sampling in Qualitative Research
- Sampling in Surveys
- Scaling
- Significance Testing
- Simple Regression
- Survey Design
- Time Series
- ARIMA
- Box-Jenkins Modeling
- Cointegration
- Detrending
- Durbin-Watson Statistic
- Error Correction Models
- Forecasting
- Granger Causality
- Interrupted Time-Series Design
- Intervention Analysis
- Lag Structure
- Moving Average
- Periodicity
- Serial Correlation
- Spectral Analysis
- Time-Series Cross-Section (TSCS) Models
- Time-Series Data (Analysis/Design)
- Trend Analysis
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