The data preparation process is the critical step of transforming raw, unanalyzed data into an organized format ready for statistical analysis. This complex process involves coding and combining multiple variables—such as demographics, independent and dependent variables, and manipulation checks—for each participant. It also requires identifying and handling any missing, incorrect, or suspicious responses.

Data Preparation Process

Before beginning data analysis, researchers must ensure that raw data and consent forms are stored securely to protect participant confidentiality. Data should not include identifying information. Secure storage options include a locked room or a password-protected computer. It is also recommended to create backups or photocopies of the data, storing them in a separate secure location until the project is complete, and retaining them for several years in case questions arise.

Securing Raw Data

Once data is secured, researchers should review the raw data for completeness and accuracy. This involves looking for illegible or missing responses, as well as obvious misunderstandings or suspicious entries (e.g., a response of $$12$$ on a $$1$$-to-$$10$$ rating scale). Identifying these issues early is crucial for determining if a participant's data is usable for statistical analysis.

Checking Raw Data

A data file is a structured digital format used for statistical analysis, typically created in a spreadsheet program (like Microsoft Excel) or specialized statistical software (like SPSS). In the most common layout, each row represents a single participant, and each column represents a specific variable, with the variable name placed at the top of the column.

Data File

What does the term 'raw data' refer to in the context of psychological research?

Raw data, also referred to as unanalyzed data, is the initial information collected during a research study before any organization, coding, or cleaning has occurred. In psychological research, this data can take several different forms, such as completed paper-and-pencil questionnaires, digital files containing numbers or text, video recordings, or written observational notes.

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Data encompasses facts, figures, or information gathered for analysis, reference, or computation. It can be qualitative or quantitative and serves as the foundation for reasoning, discussion, or calculation across fields like scientific research, business, and technology. Data exists in diverse forms, including numbers, text, images, or sounds, and is processed to extract meaningful insights. In computing, data is the foundational element; all interactions revolve around reading, modifying, and creating it. Fundamentally, all computer data is stored as uniform sequences of bits, making it inherently similar regardless of its perceived form.

Data (Information)

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KPU Research Methods in Psychology - 4th American Edition

Structured data is comprised of clearly defined data types whose pattern makes them easily searchable. Data may be human or machine generated as long as the data is created within an RDBMS structure. This format is eminently searchable both with human generated queries and via algorithms using type of data and field names, such as alphabetical or numeric, currency or date.

Structured Data

Unstructured data either does not have a pre-defined data model or is not organized in a pre-defined manner. It has internal structure but is not structured via pre-defined data models or schema. Unstructured data may be textual or non-textual, and human- or machine-generated. It may also be stored within a non-relational database like NoSQL.

Unstructured Data

For CNNs, the data used usually has several channels (single channel or multichannel)  with different dimensionalities (1-D, 2-D, or 3-D). Each of these channels represent an observation of a different quantity at some point in space or time.

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