Our Mission

Our mission is to provide high quality data

A commitment to quality

Designing questionnaire to maximize data quality

Scientific data collection (planning and design)

Data needs to be collected as a surely valid response in strictly controlled method. Anyone can collect data, but it always just ends up as “noise” without scientifically conducting . Then reliable results and findings are not provided.
Rather than letting anyone answer questions or just leaving surveys in the hands of the interviewers, we follow predetermined rules (scientific methods) covering areas such as the design of target respondents and questionnaire, sampling, and data collection control measures, including training and instructions for interviewers to get guaranteed usable data.

1. Designing questionnaire that accurately reflects objectives

We design questionnaire with analyzing the details at the root of the problem or issue (actions, awareness, etc.) and collect data on the precise status of respondents. We always take analysis into consideration and design questionnaire so as to ensure that no necessary questions are omitted.

2. Designing the flow of questions from the respondent’s point of view

We always design the flow of questions from the respondent’s point of view in order to improve the quality of each survey, including minimizing erroneous responses, reducing response time and improving the response rate.
Considering the response assumption of “when”,”who”and “what”, we group the questions and adequately indicate the subjects and conditions at the same time we create a question flow of “from awareness to action” or” from general statements to specifics” to solve the unnecessary confusion of respondents.

3. Carefully reviewing wording to eliminate noise and erroneous responses

We follow the principle of using simple expressions on our questionnaire and use precise wording so as to eliminate any ambiguity that can frequently result from specialist terminology or foreign terms (written in katakana in Japanese).
We analyze the meaning, contents and structure of all items so as to ensure that the choices are compiled without any omissions or overlapping meanings.

4. Respondent-friendly questionnaire

We design to simplify questions and items so as to make it easier for the respondent to understand each question, including reducing explanation which the respondent has to read and using underlining or bold text to highlight conditions and other points that should be clear.
We also select formats designed to facilitate responses, such as using a matrix if there is a sequence of similar questions for example.

5. Reappraising completed questionnaire in order to refine the question structure

After fieldwork has been completed, we review the completed questionnaire, carefully inspect any problems areas in relation to responses and summarize our findings for future reference.
We analyze aspects such as the percentage of respondents choosing “Other”, the distribution of missing responses and the number of incomplete questionnaire in an effort to produce questionnaire that allows respondents to complete easily.

Data processing

1. Accurate data compilation

Questionnaire and other responses on paper need to be converted into digital data so that they can be tabulated. In order to accurately input paper data, we conduct pre-input inspections on questionnaire in an effort to minimize input errors. As part of the input process itself, we carry out verification on all samples and use a programmed input screen to eliminate any input errors.

2. Data cleaning and editing

Input errors in survey data are checked by using a dedicated program. In addition to eliminating human error, this also enables us to carry out logic checks on the consistency and other aspects of respondents’ answers to questions.
If any errors are found in the data, we “clean” data, meaning that we go back to the questionnaire and make corrections, and edit data to enable it to be tabulated if necessary (data conversion, composition of new variables, etc.).

3. Data processing in line with analytical techniques

Experienced analysis personnel map out the best possible questions (measurement methods) in order to obtain the data required for the relevant analytical techniques. We also process and refine data in accordance with rules at the data editing stage, covering issues such as how to handle missing values (missing responses).
We also convert responses into the correct client's designated format (ASCII, EXCEL, SPSS, SAS, ASSUM, etc.).

4. Use of the latest analytical techniques

We provide output using multivariate analysis and a wide range of other data analysis techniques.
In the case of text data such as open ended questions, we use techniques such as text mining and indexing (keyword extraction).