There are five steps that a student conducting research can follow in administering and analyzing survey data and presenting results in a simple way that is useful to the stakeholders. When the student follows these steps accurately and appropriately, the reporting procedure becomes timely and more informative. The five steps include review and analysis of the plan, preparation and checking of files to be used, calculation of response rate, calculation of summary statistics, and presentation of results (Steven t al., 2017).
These steps are divided into three major parts: survey development, sample collection, and survey administration. The activities in each process may differ depending on the nature of the data collected and the study objectives (Pazzaglia et al., 2016). In the survey administration process, it is crucial to understand how to use the survey data by correctly analyzing and presenting them useful.
Analysis and Reporting of the Survey Data
The researchers must develop a viable analysis plan during the development stages before the administration of the survey. The analysis plan should provide the survey structure and describe the process of data mining, analysis, and presentation. Furthermore, it ensures that the data collection and analysis process aligns with the objectives to avoid the processing of unnecessary information (Steven et al., 2017). Researchers are mandated to calculate summary statistics such as measures of central tendencies and tests of relationships using analysis software such as STATA, R-studio, excel spreadsheet, or SPSS. Similarly, the sample size, sampling techniques, and response rate determine the conclusion’s strength (Pazzaglia et al., 2016). Based on the psychology survey data, it will be essential to decide on the correlation between church-going and the level of happiness.
Step 1: Review of the Analysis Plan
The survey analysis plan highlights the survey questions concerning the study title and suggests the possible ways of analysis and presentation prepared during the development process. Once the researcher administers the survey, the team can review the analysis plan and update it to meet the study objectives. The updates are based on the problems encountered during data collection, uncaptured responses, and missing data (Steven et al., 2017).
For example, some respondents may choose to skip or ignore some questions because of personal issues not within the study scope. The researcher can choose to include or ignore these discrepancies during the data analysis process. Updating the analysis plan ensures that the survey data answer the original questions and inform the policy intended to address (Pazzaglia et al., 2016). Before analyzing the survey data, it is essential to discuss the survey’s summary statistics with the instructor, including specific changes to improve the collected data.
Step 2: Preparing and Checking Data Files
This process involves quality checks and data cleaning. The researcher compiles files into electronic formats, merges data, checks for duplicated data, errors during data entry, and determines a coding procedure. In the section, the demographics are collected, including the participant’s age, sex, emails, names, or study unique identifiers (Steven et al., 2017). The sections also describe showing that data collected will be kept secure from a security breach. Microsoft Excel is the best tool for data compilation and linking using duplicate data using consistent variables to match the exact cross files. After merging the files, it is essential to countercheck by opening a new file to ensure that the files reached correctly before data analysis (Pazzaglia et al., 2016). The open-ended question is also coded into categorical variables that are easy to compute statistical inferences from them.
Step 3: Calculation of the Response Rates
Before statistical computation, the survey team needs to discuss whether the data collected is enough to conclude and represent the target population. In addition, it is essential to remind the unit if the data is based on probability or non-probability samples. In non-probability instances, the results cannot inform the general target population; thus, this step is omitted (Steven et al., 2017). On the contrary, probability sampling helps to calculate two types of response rates: survey and items nonresponse bias, which can skew the results in a particular way. If the team discovers nonresponse bias, it can consider an extension of survey administration to increase the response rates.
Otherwise, bias data is not good to conclude the entire population; however, it can determine a trend about the respondents (Pazzaglia et al., 2016). To calculate the response rate, divide the number of participants who responded by the number of total participants recruited. If the response rate is more than 85%, it is sufficient to assume that no more correspondence analysis is needed and vice versa for data with less than an 85% response rate.
Step 4: Calculating the Summary Statistics
Under the directions of the researcher and the instructor, the data team should calculate the statistical summary that is relevant for the study and which is understandable by the target audience. First, the researcher can use specialized statistical software to calculate the statistics (Pazzaglia et al., 2016). Next, he will use SPSS to analyze data and clean it to ensure that the statistics are correctly computed and reproducible. After that, the researcher should transcribe the data to provide conclusive information about the topic and the study population. Finally, the team will calculate the statistics using SPSS software (Pazzaglia et al., 2016). As described in the analysis plan, the calculated percentage of respondents will be a standard error for multiple choices and rated based on traditional scales.
Step 5: Presentation of Results
Data presentation is a critical step in survey administration because it helps the audience to understand the project, interpret the findings, and determine the impact of the study on society. The statistics in psychology course survey is guided by the analysis plan where tables and figures made SPSS and Microsoft word will help compute different summary statistics (Steven et al., 2017). The table will include meaning, standard errors, and standard errors presented in bar graphs, pie charts, and box plots. To ensure that stakeholders understand the study results, the student will submit the preliminary data, including tables and figures (Pazzaglia et al., 2016). The stakeholders will advise on the best method of presentation of the survey project. In addition, the student will generate multiple reports for a different audience and different.
When administering a lab report survey, it is because they attract essential to consider that the downstream analysis and how the data will be presented to pass the message. For instance, the student should figure out how to use tables and figures available in the analysis platforms such as excel spreadsheets to ensure consistency in formatting. In addition, consider using visuals such as graphs and charts because they attract the reader’s attention and present the information in a way that is easy to understand. Similarly, ensure that the tables have all the information required to deliver the message without referencing the main text. Infographics are other important ways of data presentation where they highlight the key points and take away notes.
Pazzaglia M, A., Rodriguez M, S., & Stafford T, E. (2016). Survey Methods for Educators: Analysis and Reporting of Survey Data (Part 3 of 3). REL 2016-164. ERIC. Web.
Steven G., H., Patricia A., B., & Brady T., W. (2017). Applied Survey Data Analysis (2nd ed). Chapman and Hall/CRC.