Data Collected but What Should You Analyze First?

Analyzing data is not about collecting data. Researchers need to know the statistical analyses and techniques to follow a certain variety of collected data. The order in which analysis is conducted can lead to the unreliable result or difficult to understand interpretations.

Quick Summary:

Check and clean the dataset, then conduct descriptive statistics. After assessing the data, check the reliability and validity of the data and assumptions to be made to decide on what statistical techniques to follow.

What Should You Analyze First After Collecting Data?

The order should be data cleaning and quality checking followed by statistical analysis. Check the data for any missing values, duplicates, incorrect coding, or outliers. Once the data has been cleaned, understand the data using descriptive statistics.

A pragmatic sequence of analysis is:

• Data screening and cleaning

• Descriptive statistics

• Reliability and validity checks

• Assumption testing

• Inferential or advanced statistical analysis

• Interpretation and reporting

This sequence allows researchers to avoid performing advanced analyses on data that is contaminated or misunderstood.

Why Should Data Cleaning Come Before Statistical Analysis?

Data checking and cleaning finds errors that could bias the results. A survey could have missing responses or incorrectly coded variables.

Before analysis, check:

• Missing responses

• Duplicates

• Incorrectly coded variables

• Data-entry errors

• Extreme or unusual observations

• Appropriate measurement scales

• Consistency between the coding of variables

For example, gender is coded as Male as 1 and 2 for Female. If a record contains gender as 20, that record should be analyzed.

What Descriptive Statistics Should You Analyze First?

After data cleaning, understand the basic characteristics of your sample and variables using descriptive statistics.

For each variable, consider:

• Frequencies and percentages for categorical variables

• Means and SD for continuous variables

• The mid-point and spread for continuous variables

• The minimum and maximum value for a variable

• How responses are distributed

Before determining if two variables are related, you would need to know the distribution of responses for each variable.

Should You Check Reliability and Validity Before Hypothesis Testing?

Yes, if your study employs multi-item scales or measurement constructs. Reliability can tell us the level of inter-item consistency for a given measure. For studies that involve latent constructs, researchers may need to assess the level of measurement validity prior to interpreting the relationships among the constructs.

For a PhD study that is based on a survey, this step may be particularly important prior to moving to correlation and regression analysis, SEM, etc.

When Should You Test Statistical Assumptions?

Test the assumptions pertinent to the statistical method prior to explaining the results. Different methods have different prerequisites.

For instance:

• Regression is concerned with assumptions of linearity, residuals, and multicollinearity.

• Parametric methods are concerned with the distribution of the variables.

• ANOVA is concerned with assumptions of the residuals and the variance.

• A SEM analysis is concerned with the estimation framework and model.

DO NOT test them ALL. Choose the assumptions based on your research, variables, and proposed method.

What Should You Analyze After the Preliminary Checks?

Once a dataset is screened and understood, perform analysis that addresses your Research Question/Objective/Hypotheses directly.

Research Purpose

Possible Analysis

• Examine association

• Correlation

• Compare groups

• t-test or ANOVA

• Predict an outcome

• Regression

• Test relationships among latent constructs

• SEM / PLS-SEM

The appropriate method depends on the research design and characteristics of the data—note not the availability of software.

Who Should Follow This Analysis Sequence?

This approach is suited to PhD scholars and research students, faculty, purposefully designed academic researchers and staff working with survey, experimental or quantitative research data.

It is suited best before data analysis and before the publisth dissertation.

What Is a Simple Example of the Correct Order?

Assume a PhD student collected 300 completed questionnaires to assess if research training will encourage the intention to publish.

A proper sequence would include:

• Data cleaning

• Description of the respondents

• Check measurement reliability/ validity

• Check the assumptions

• Relationships/hypotheses testing

• Findings Interpretation

This will encourage the researcher to assess the dataset and measurement tools prior to regression or SEM.

The Overall Implication

Don’t start with the most complicated Statistical tools. Focus on the data. Clean the data, Understand the data characteristics and evaluate measurement where necessary. Check critical assumptions of the tool selected. Then pick the best tool that fits the research questions and the hypotheses.

Frequently Asked Questions

1. What is the first step after collecting research data?

The first step is data screening and cleaning. Check missing values, duplicate records, coding errors, unusual observations, and variable formats before conducting statistical tests. After cleaning, descriptive statistics can help you understand the dataset.

2. Should I run descriptive statistics before hypothesis testing?

Yes. Descriptive statistics provide an initial understanding of your sample and variables. They can reveal unusual values, distributions, and patterns that may influence your choice or interpretation of subsequent statistical analyses.

3. Which statistical test should I use for my research data?

There is no single test that works for every study. The appropriate analysis depends on your research questions, hypotheses, variable types, research design, measurement scales, and assumptions. Choose the method based on these factors rather than selecting a test solely because it is available in SPSS, AMOS, or another software.