How to Analyze Survey Data

Survey data is the resultant information collected from a sample that has been surveyed from target people based on a particular topic. Therefore, analyzing the survey data is the critical sorting of data based on the information gathered. Analyzing is done to ensure it reaches the objective of the research. The steps illustrated are the ones to follow in analyzing the collected survey data. There are; 

Review of the analysis plan. It entails an understanding of the most popular survey research questions. The questions should be in relation to the research conducted (Ehwerhemuepha & Feaster, 2020). The information gathered from the questions will help the researchers in future development based on the survey. Results obtained are filtered using the cross-tabulation. This technique is used to analyze the relationship between multiple variables. It is performed on categorical data i.e., data that has been divided into mutually exclusive groups. It comes in relation of understanding the category of the targeted group in the audience and their thoughts (Farhat & Robb, 2018). The filtering of the result includes complying with the paper survey in an electronic file, merging the data, and checking for any errors that may be presented. Evaluating the derived numbers; Analyzing the data gathered is complex. Because for instance, after conducting a survey, the researchers need to understand the audience's feelings toward the survey. It is due to knowing if the audience will participate in the plans of the future (Pazzaglia, Stafford & Rodriguez, 2016). The facts are sorted from the results obtained. Drawing Conclusions; The report will be drafted based on the survey. The survey's intention is known, and how does the survey help in the objective of the research? Also, at this step, one needs to understand and develop accurate, conclusive results. Apart from the steps above, analysis of the survey data involves; Calculating survey weights. The weight is used to make the correct inference from the sample collected from the population. The surveyed weight adjusts for the fact that in complex tallying of samples, each respondent could have a different probability of being sampled (Farhat & Robb, 2018). Data reduction, helps multiple questions measure a single measure either by one wanting to reduce the dimension of the raw data into smaller and more manageable in analyzing or due to the population characteristic of interest. The above dilemma can be analyzed using Principle Components Analysis (PCA) and Factor analysis, respectively. Factor analysis is designed to identify substantively meaningful groups for questions that can be significant in conducting the survey and communicating the results. Univariate and Multivariate analysis modeling surveys based on the objective (Ehwerhemuepha & Feaster, 2020). Lastly, Present the results. It involves the ability to communicate thoughtful insights to the stakeholders in a logical and concise manner that can negate the hard work engaged in conducting the survey. The main aim is to communicate to the decision-makers how the survey data address the original objective. Cross-tabulation: This method uses a basic tabulation framework to make sense of the data. The method helps evaluate data into easily understandable rows and columns; this helps draws parallels between different research parameters (Anderson & Fricker, 2016). It contains data that is connected to other people. Trend Analysis: This statistical analysis method provides the ability to look at the surveyed data over a long period. It helps gather the response data over time, allowing us to draw a trend line of the change if there is any about a shared variable. MaxDiff analysis: Is used to gauge what a customer prefers in products or services across multiple parameters. It can also be called the Best-worst method. Conjoint analysis: This analysis method is similar to MaxDiff analysis; the only difference is the complexity and the ability to collect, and analyze each parameter behind a person's purchasing behavior (Pazzaglia, Stafford & Rodriguez, 2016). This method is possible to understand what exactly is significant to a customer and the aspect that is evaluated before the purchase is made. TURF Analysis: Total Unduplicated Reach and Frequency analysis is a research methodology that asses the total market niche of a product/service or even both. The methodology is used mainly by organizations to understand the frequency and the avenues at which their messaging reaches customers. This help the company juggle its go-to-market strategies. Gap Analysis: It uses a side-by-side matrix question type to evaluate the difference between the expected and actual performance. This method for survey data helps understand the things that have to be done to move performance from actual to planned performance. SWOT Analysis: This method helps identify the strengths, weaknesses, opportunities, and threats of an organization's product and services. It provides a holistic picture of the competitive advantage and helps to create effective business strategies. 

Text Analysis: It is an advanced analytical tool that makes sense of the unstructured data that has been collected from the survey. 

With the steps and methodology discussed above, analyzing the survey data comes with complexity. In the next paragraphs, the difficulties will be outlined (Revilla, 2017). The aspects of survey data are adjusting for the differential representation of sample observations and assessing the loss or gain in the exact results obtained from the survey. Bayesian networks can be used to analyze survey data. The primary goal is to provide continuous information about the significant social fields of population, housing, education, employment, health, and income. This technique of the Bayesian network is known for providing a compact and easy-to-use representation of probabilistic information (Ehwerhemuepha & Feaster, 2020). The Bayesian network has two components, namely, a directed acyclic graph and a probability distribution. The directed acyclic graph majorly provides a summary of the dependency structure relating to the variables. The Probability distribution graph is an effective way to describe the overall dependency structure of a large number of variables, thus removing the limitation of examining the pair-wise association of variables. The problems of analysis of survey data are categorized into three major areas. The nature of the data and the locality it emerges. The following factors need to be kept in mind when analyzing data. First, there is a wide variety of information about each person being interviewed. Human behavior is motivated by different things. And the richness of the collected data from people creates a lot of problems concerning how the researchers can handle the information obtained. Also, the classification of data. For instance, the age of the audience is not constant; it varies occasionally. Thirdly, errors in measures, with little evidence to the size of the damage to the survey. Fourthly, there is sample variability piled on top of measurement error. All survey samples are tallied, and this leads to problems of the proper application of statistical techniques (Revilla, 2017). The statistical test usually uses simple random samples rather than probability samples. Fifthly, there are inter-correlation between many factors to be used in the analysis. Hence, it is difficult to assess the importance of different factors, since the inter-correlation keeps getting in the way. The technical factors are not the same as the factors we can measure; this affects the analysis techniques and focuses attention on creating importation interactions effects to represent the construct factors. The analysis technique should be carefully selected because the samples are classified there; they need to be converted into binary numbers for straightforward interpretation and presentation of the results. The conclusion is that, for analyzing survey data to be effective, the survey should be done to the letter. It will help to identify which methodology will be used for statistical analysis. 

Reference 

Anderson, L. A., & Fricker Jr, R. D. (2016). Understanding Public Opinion toward Violent Extremists.  Modeling Sociocultural Influences on Decision Making: Understanding Conflict, Enabling Stability, 423. 

Ehwerhemuepha, L., & Feaster, W. (2020).  Survey Data Analysis: Analyzing Patient Experience Using Combined Survey Data and Clinical/Psychosocial Outcomes Data. SAGE Publications Ltd. 

Farhat, J., & Robb, A. (2018). Analyzing complex survey data: the Kauffman Firm Survey.  Small Business Economics,  50 (3), 657-670. 

Pazzaglia, A.M., Stafford, E.T., & Rodriguez, S.M (2016). Survey methods for educators: Analysis and reporting of survey data ( part 3 of 3) (REL 2016-164) 

Revilla, M. (2017). Analyzing survey characteristics, participation, and evaluation across 186 surveys in an online opt-in panel in Spain.  methods, data, analyses,  11 (2), 28.