Data Preparation and Preliminary Data Analysis by
Jordan Beckett, Leo He, Ellie Kamkar, Leah Toth, Ranran Xie, Jiaqi Yu, Ruobing Zheng
Introduction – Data Preparation and Analysis
Data preparation and analysis is an important part of the conducting market research process. In reality, it is the point of doing market research in the first place in order to collect the raw data obtained by the research process, format it in an understandable and meaningful way, and correctly interpret the results. Data
preparation and analysis is a multistep process, in which each step commands equal amounts of attention and time in order to emphasize the significance of the entire operation.
Data must be first collected, and then most appropriate collection method is determined. There are number of different options, as each chosen method will depend on the type of information needed. Data must then be organized and presented in a logical and concise manner with outliers and faulty logic. Preliminary
analysis of the data can take place after, where techniques such as standard deviation and skewness add some degree of substantive and quantitative opinions to that. Researchers can then begin to formulate their overall opinion on the data and form a qualitative opinion on the research. Eventually, if the research is conducted successfully and properly, the original question or questions the researchers set out to answer will be answered. Ideally enough contexts are presented in the findings so that a proper conclusion could be determined, and the appropriate responses and reactions could then take place.
This chapter will examine and outline in more detail about the exact processes and methodologies that should be undertaken in the data preparation and analysis stage of the marketing research.
Data Collection
One of purposes of marketing research is to use the collected information for predicting and making decisions. Thus, data collection is the first step of success and usually takes place in the early stage of the project improvement. Basically, a data collection plan includes the following activities:
1. Pre-collection activity: Estimate goals, Select and fieldworker, and prepare guidelines.
2. Data Collections: Interviews 3. Present findings: Data Analysis
Before collecting data, pre-collection is one the most vital steps in the whole process. It is always decent to plan everything in advance. Otherwise, the interview will be discounted as the consequence of poor
arrangement. After the setup work is completed, the rest of process can be carried out in a structured, systematic, and scientific way. (Business Marketing Research and Data Collection)
Once all the information has been collected, researchers need to put all the information together and make further analysis. The key of data preparation is to convert raw data into a usable data for analysis. The analysis and results rely on the quality of data. The first step is to enter all of the data into the database.
Therefore, they are all formatted in the same pattern and are organized properly. Well-organized data can effectively save a lot of time and avoid any mistake and confusion. For majority of the research, information comes from different sources at different time, such as face-to-face interview, observation testing, and on-line survey. The appropriate processes of measurement include: data entry, coding, editing, checking and update correction. Once data has been collected, researchers need to enter them into the data file. Most people use computerized database program such as Microsoft Excel, and statistical programs such as SPSS.
The collected data is better kept for a reasonable period of time since the data analysis always needs to trace
research analysis. (Data Preparation)
Once all of the information has been entered, it needs to be verified, checked, processed, and tabulated.
Checking the accuracy of data can be time consuming; nevertheless, it affects the overall quality of
subsequent analysis. Otherwise, wrong information may lead to incorrect decisions and increased workload.
The traditional way of verifying accuracy can be accomplished by spot-checking a random group of data.
The better method of checking accuracy is using specialized computer program that will cross-check and eliminate data that are entered twice. Unfortunately, utilizing these computer programs requires a substantial period of time for training.
Data preparation process
Our researchers spend a lot of time to collect the data through face-to-face interview, telephone interview, and email interview. We need to make sure that the data is useful for the plan, so the appropriate instrument is carefully crafted to generate the data that can be ultimately be transformed into the knowledge. Generally, the data preparation process can be described as the following seven steps:
In addition, not each step will require in every marketing research, and it will base on the situation.
Preliminary data analysis
Descriptive statistics is the first stage of data analysis. It describes not only the characteristics of the data, but also provides the initial analysis of any kind of violation. In addition, descriptive statistics allows researchers to point out specific research questions. Most of the advanced statistical tests are sensitive to violations in the data. That is why this particular analysis is extremely useful and important. Researchers can obtain where and how the violations is occurring within the database by using the descriptive test result. There are many terms that measure the descriptive statistics, such as standard deviation, skewness, range of scores, kurtosis, and mean. Furthermore, this type of statistics can be determined by descriptive, frequencies, or SPSS command. SPSS (Statistical Product and Service Solutions, SPSS Statistics) is a widely used statistical analysis software package. Moreover, SAS, Stata, Minitab are also popular statistic analysis technique software.
The primary scales of measurement include nominal, ordinal, interval, and ratio. All measurements are separated into two different categories: categorical variables (non-metric data) and continuous variables (metric data). Categorical variables include nominal and ordinal, contain gender, marital status and other basic variables. On the other hand, continuous variables include interval and ratio, contain length, distance, height, and temperature. It is important to classify the data into either categorical or continuous variables, because we need to use the appropriate test for analysis. The technique for analyzing categorical data that is most frequently used is the chi-square test for independence. For statistical analysis of continuous data, there are some tests such as hypothesis test, sign test, and analysis of variance.
Figure 7.1: Descriptive Analysis Process
The mainstream views of the stages in preliminary data analysis include exploratory analysis, deriving the main findings, and archiving.
Exploratory data analysis (EDA) is the stage where anomalies become evident. It usually overlaps with data cleaning and shows up limitations in contingent questions. EDA allows the subsequent deriving stage of the main findings to be relatively quick, uncontroversial, and well organized.
Furthermore, the purpose of deriving the main findings is to clean the version of the data. Analysis files can also be divided into consistencies and inconsistencies. On the other hand, deriving the main findings can generate the summary findings, relationships, models, interpretations and narratives, as well as the first recommendations for research users.
The third stage is archiving. Usually surveys are complicated due to the analysis that covers only a fraction of a different content. In the achieving stage, data processors can keep all the non-ephemeral material relating to their efforts to acquire information.
Data analysis techniques
Plainly stated, data analysis is the process of taking raw data and turning it into useful information. There are several ways that data can be analysed; however, some techniques work better with certain studies than others. Data analysis ranges from very simple studies such as simple deviations, which includes using
techniques like the mean, median and mode to extremely intricate analysis methods such as mathematical modelling, which includes methods such as linear regression.
The main methods of data analysis include:
Simple and complex deviations Data models
Social indicators
Mathematical modelling
Simple and complex deviations include methods such as defining the mean compared to a more complex deviation of determining the expectation of life. Data models include such graphs that would show the
population estimation and other items being studied. Data analysis using social indicators that include the use of such techniques such as unemployment rate, ageing population and life expectancy. Lastly, mathematical modelling is used to display more complex findings such as factor analysis.
Data can be analysed using methods that include univariate, bivariate and multivariate analysis. Univariate analysis is used when a single variable is analysed on its own. It is used for more simple testing analysis by employing techniques such as averages and variances. Multivariate analysis is the most common method for analysing data because it utilizes multiple analysis techniques to examine a set of data. This technique involves comparing two or more variables to see if there is a relationship among them. Techniques that are used to assist in multivariate analysis include degree of relationships such as collations.
It is important to note that when analysing the data from the studies conducted, it is common to require secondary statistical data. Usually, data and information are taken from the National Statistics Offices (NSO).
The NSO collects and compiles an assortment of statistical and information. The examples are: StatsCanada for Canada, Central Statistics Office for Ireland, and U.S. Bureau of Labor Statistics for the United States.
Figure 7.2: Classification of Univariate and Multivariate Techniques (Shukla)
Conclusion
In conclusion, data preparation and analysis is the critical process when conducting the marketing research.
By collecting the raw information and processing the data through the research process, we can logically comprehend the facts and correctly substantiate the results. It is now understood that every step in the marketing research, data preparation and analysis, has its own steps in each stage. This multi-step process is used to predict market trends. It also allows people, companies, and organizations to make decision with foresight.
As observed in this report, data collection is the first step where we set up pre-collection activity, conduct the data collection methods, and present its findings. After the work setup is completed, the rest of process can be carried out in a structured, systematic, and scientific way. (Business Marketing Research and Data Collection). Data preparation is an indication of converting raw data into usable data for analysis. Data comes from a variation of sources at random times through the observation testing, face to face interviews, phone interviews, surveys, and online questionnaires for some examples. Finally, the data is entered, checked for errors, verified, processed, charted and then computerized using Microsoft Excel, SPSS, or other
statistical programs.
With successful completion of the steps above, we achieve the means to determine, and describe the
characteristics of the found data providing analysis, as well as allowing researchers to point out the specific research questions. By using descriptive test results, we recognize where and how violations occur within the database, and measure the descriptive statistic through standard deviation, skewness, range of scores,
kurtosis, and mean. EDA (Exploratory Data Analysis), allows the subsequent stage of deriving the main findings to be relatively quick, uncontroversial, and well organized.
In the stage of EDA, anomalies overlap with data providing limitations on contingent questions. As surveys are complicated and covering only a fraction of different subjects, naturally the final stage of descriptive statistics is archived in places like the National Statistics Office for future comparison and analysis.
Fundamentally, data preparation and analysis in its final stage can be analysed using methods that include univariate, bivariate and multivariate analysis. As outlined, the main methods of data analysis used for determining results are, data models, social indicators, mathematical modelling, and simple and complex deviations.
In summary, in this chapter we outlined details of data preparation, discussed exact processes in analysis, and presented alternative methodologies that should be undertaken in the data preparation and analysis stage of marketing research.
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