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Evaluating Efficacy of Survey Research as a Quantitative Research Method

Surveys are a powerful research tool, capable of reaching a broad audience and yielding rich data. They also demand skillful use by the researcher in order to yield reliable results. When used properly, surveys can produce answers that are direct, to the point, and easily understandable. However, they are not without their own set of challenges that must be addressed before they can be utilized as an effective research method. They require skills that may not be immediately available to a researcher at first glance. In this article we will discuss the different types of surveys, explore their reliability and validity as well as their strengths and weaknesses as research methods. We will then outline how survey research can be effectively conducted to best yield accurate outcome findings.

What is a Survey?

A survey is a form of conducting questionnaires with questions related to a specific topic or topic area. Surveys are often observational research methods, meaning they rely on the gathering of data by observation. Depending on the research goals, surveys may be qualitative or quantitative. When conducting a survey, researchers use a questionnaire to gather data. The questionnaire can be as simple as a series of free-text questions or as complex as a multiple-choice or yes/no question. In either case, the survey questionnaire requires the respondent(s) to fill in information about themselves. Think of a survey as a conversation with yourself or your audience. You want to hear about how you do, what you like, what you don’t like, and what you’d like to see/experience in the future. One of the best ways to get your audience’s thoughts is via a survey.

Conducting a Survey

Planning- Do not just jump into the research, plan your procedure and how you will conduct it beforehand. This will ensure that you do not miss any key points or questions that you should be asking.
Population- In order to make sure that the results are accurate and applicable to your topic, it is important to select a sample group of people who represent the population as much as possible.
Power Analysis- A power analysis is used to determine if there are enough participants in your study for reliable data collection and analysis. The power analysis is based on your sample size and the desired level of accuracy in your findings.
Questionnaire Design- Your questionnaire needs to be designed very carefully so that it can gather relevant information from all participants in the study without bias or bias towards certain characteristics or demographic groups.
Data Analysis/Report Writing- After gathering all of your data from participants, it is time for analysis! This step involves looking at each piece of information collected by each participant and organizing them into categories based on their importance or relevance with regards to answering the research question.

How effective is survey research in collecting quantitative data?

The Survey Method has several strengths when it comes to conducting quantitative research. The first strength is that it allows you to focus on specific, manageable questions that can be answered quickly. Survey Method also allows you to aggregate your data in order to measure multiple variables at once. This can be particularly useful when trying to study complex topics, such as human behavior or social issues. For example, if you wanted to study how people felt about different types of entertainment, you could use a survey method by asking everyone in your area which type of entertainment they most enjoyed watching or listening to. Then, you could take all those responses and aggregate them into one number (number of people who enjoy watching or listening to this type of entertainment). Now you know how many people like this particular kind of entertainment and can compare their response with others who do not like this type. This simplistic example describes how surveys can help you to derive qualitative and quantitative information in an environment.

In conclusion, surveys are a powerful research tool capable of reaching a broad audience and yielding rich data. They also demand skillful use by the researcher in order to yield reliable results. When used properly, surveys can produce answers and quantitative information that are easily understandable. However, they are not without their own set of challenges that must be addressed before they can be utilized as an effective research method.

The Importance of Data Cleaning as a Subset of Data Preparation

The process of detecting and improving corrupt or inaccurate information or records to increase your data quality is called data cleaning. It includes the screening of the data set prior to analyzing to remove unwanted data entries or erroneous data that can prove to derail the study. The software for cleaning can be chosen depending on the type of data analysis that a study requires.

Types of data cleaning in research

Data cleaning is the process of identifying and removing invalid data from a data set. Erroneous data can cause major problems in research. They might lead to faulty conclusions, incorrect or misleading research findings, and even be impossible to remove. Other types of data entry errors include: –

Incorrect data type
Incorrect data value
Incorrect data format
Incorrect data entry procedure
Improper data management
Lack of attention to detail
Poor communication
Lack of effective feedback
Unwanted data
Over-interpreting observed data
Improper allocation of resources.

Identifying and removing invalid data

When data cleaning is done well, it is possible to identify and remove invalid data entries from a data set. This includes the following: –

Identifying and removing erroneous data
Improving the quality of existing data
Scoring data for reliability and validity
Bias and gleaning from unanticipated events

Improving the quality of existing data

When data is not cleanly created or gathered, it is sometimes possible to improve the quality of existing data. The software for this can be chosen depending on the type of data analysis that a study requires. Any mix of the following steps can be used to improve the quality of existing data: – Identify all missing values. – Remove all zero values. – Calculate the average and standard deviation (aka the mean) for each value. – Divide the values into groups that are comparable. – Split a data set into groups with similar distributions and/or distributions that are not normally distributed. – Create and label key values. – Create legend boxes for each group. – Apply Statistics to distinguish data types. – Choose a final statistical technique based on the type of data. – Create a transformation matrix. – Apply transformation to clean data. – Generate new data. – Clean the new data. – Apply transformation to the cleaned data. – Apply transformation to the new data to create a new set with the same distribution as the original set. – Repeat the process for each missing value and each other item that is not normally distributed.

Scoring data for reliability and validity

When determining how to score a data set, you must take into account both the data quality and the application. For example, if you are scoring a set of blood pressure readings, it is important to note that each number reported is actually lower than average. However, the study also states that most users report having a reading of 100 or above on a regular basis. This means that the data set is actually able to provide a calculated value of only 91.5 or higher. The scoring method must take these differences into consideration when determining the actual value.

Bias and gleaning from unanticipated events

When processing data, it is important to maintain a high level of accuracy and thoroughness. However, occasional inaccuracies or even unintentional leniencies may occur in the data collection process. These may be referred to as “bias” events. The ideal is to find ways to reduce the likelihood of these kinds of events occurring in the first place by using appropriate procedures and tools. To reduce the likelihood of bias, you can: –

Use consistent data collection procedures
Limit the number of individuals involved in data collection
Use reliable and valid instruments for measuring variables
Follow standard formatting and data entry procedures
Follow standard labeling and disposal procedures
Follow good documentation and analysis processes
Follow up with a thorough data check to determine the cause of errors

Why is data cleaning important?

It is important to keep in mind that data cleaning is a routine process that happens after data collection. Data cleaning is not an “add on” function that is added to the data analysis workflow. Data cleaning is a prerequisite to the proper and accurate analysis of data. This includes the following: –

Ensuring the data is fully compliant with all relevant regulations and policies
Ensuring that the data is properly collected and deposited
Ensuring that the data collection and analysis process is fair and balanced
Ensuring that the data is properly transformed and cleansed
Ensuring the new data collected is not starkly different from the earlier findings
Ensuring that the new data meets the requirements of the model

Getting Started with SAS for Analyzing Statistical Data

The Statistical Analysis System (SAS) specializes in advanced analytics. The main purpose of SAS is to retrieve, report and analyze statistical data. It has the flexibility to incorporate data from numerous sources and facilitate descriptive visualization through graphs. This article brings you the essentials of SAS for researchers who require the software for their data analysis.

What is SAS?

SAS is a software package that enables you to analyze and interpret data gathered from different types of data sources. It can be used to analyze structured and unstructured data. It is a complete package that integrates data modeling, statistics, and report generation. It is commonly used by government agencies, businesses, and educational institutions to perform advanced analytics. SAS is also used in many applications, including marketing research, finance, and quality assurance.

How to read and write data using SAS

SAS can be used to read and write data in a variety of formats, including raw data files, delimited data files, data tables, and spreadsheet files. The read-only capability allows you to look through the data and extract the data you need for analysis. Data can be visualized as tables, graphs, charts, or reports. You can create new data types within the software to support various applications.

Key concepts in SAS

There are a number of key concepts in SAS that you should understand before moving forward with your data analysis. These are: Data Modeling: This is the process of decomposing an object or data set into smaller pieces to make the data more tractable to analysis. The data modeling is done to create an organized view of the data. Data Extraction: Data extraction is the process of taking the data modeling and transforming it into forms that are easily accessible for analysis. Data Reduction: Data reduction is the process of removing or reducing non-value-added information from the data. Data Importance: Data Importance is the measure of importance or relevance of the data to the analysis. Data Population: Data Population is the set of values that make up the data. Data Type: Data type is the properties that define the data type. For example, number, character, financial data type, etc.

Why analyze with SAS

SAS supports all major statistical methods, including analysis of variance, regression modeling, time series, multivariate analysis and graphics. SAS also has a large collection of user-written routines that allow you to perform common statistical tasks without writing code. Data can be entered into a SAS program using a variety of means: keyboard entry or file transfer via a communications port; data can also be imported from other programs such as SPSS or Stata. The program is also able to read in data from disk files (e.g., text files) or databases (e.g., relational databases). One of its main features is its ability to handle very large datasets with ease. Even if a dataset has hundreds or thousands of records, SAS will process each record efficiently so that you don’t have to deal with processing issues on your own. It also offers various ways of analyzing data such as regression modeling, variance analysis, descriptive statistics, correlation analysis and much more.

Wrapping up

SAS provides a wide variety of reports that look similar to the original data set. These reports can be used to analyze the data to determine the cause of an anomaly, examine the importance of a given variable in the data, or generate highlights for a chart or graphs. The reports generated by SAS can help you answer specific questions such as what happened in a data set and understand the “why” of the changes.

An Effective Guide to Choosing a Qualitative Research Approach

Qualitative research is the systematic inquiry into social phenomena in natural settings. It is a study of how people experience the world with focus on analyzing descriptive and conceptual findings. How this data is collected depends on the approach of the scholar to his or her study. Each qualitative research approach uses different data collection methods to avail the necessary data for analysis and interpretation.

Grounded theory

In grounded theory research, the focus is on the cultural setting, the people living in that setting, and the problems being formulated by them. The theory is more about the culture and how it reflects the wider world around it. It is more about the “why” of things rather than the “how” of them. The theory guides the research questions, the data collection strategies, and the analytical method used. With the growth of social research, and with it, a greater emphasis on data analysis, many scholars have turned to grounded theory to enrich the possibilities for the study of social phenomena. Research with a core of grounded theory is often descriptive in nature, gathering information about various aspects of a topic. Fundamental to the approach of grounded theory is the study of the local culture.

Phenomenological research

Phenomenological research is the study of phenomena rather than people. It is based on the assumption that people are real and have a personal, subjective experience that can be measured and quantified. Phenomenological research is often descriptive, in that it looks at the way people experience things. It is also comparative, giving an overview of various ways people experience things. The advantages of phenomenological research are that it is descriptive, open to a wide range of possibilities, and does not require an analysis of data.

Narrative research

Narrative research is the study of how people tell stories. It relies on qualitative methods such as interviews, focus groups, and qualitative mapping. The tales people tell and the topics they choose to tell them about are usually dependent on the audience and the purpose of the research. Narrative research is often interdisciplinary, drawing on a variety of research methodologies. It is especially useful in the social sciences, where it offers a broad approach to the study of human experiences.

Field Research

Field research is an old-fashioned method that involves staying in the community where the phenomena to be studied exists. It is often descriptive in nature, gathering information about various aspects of a topic. The advantage of field research is that it is highly focused, enabling the researcher to get to know the setting and the people living in it.

Case Study

Case study research is a descriptive method that offers information about a person, rather than a phenomenon. This kind of research is often useful in business and economics contexts, looking at the lives of people as a whole, rather than focusing on individuals. The advantages of case study research are that it is descriptive, has few limits on the types of information collected, and is quick and easy to conduct.

The techniques and methodologies developed during the formative period were primarily descriptive, based on the use of metrics to describe how people feel and interact with one another. With the growth of social research, and with it, a greater emphasis on data analysis, many scholars have turned to the above approaches to enrich the possibilities for the study of people and their experiences, behaviors and opinions.

Wilcoxon Rank-Sum test : A nonparametric alternative to two sample t-test

Statistics, a scientific approach to analysing numerical data, is employed to discover relationships among the phenomena to describe, predict and control their occurrence.

Statistics helps the researcher to acquire precise, steadfast and dependable findings. Although there are several statistical tests such as ANOVA, independent t-test, etc. to arrive at the right result, one must choose the test according to the type of study.

For instance, if one wants to investigate if the means of two or more groups are different from each other, then he/she must use the ANOVA test. On the other hand, if a researcher wants to test the relationship between categorical variables, Chi-square test is to be used. Similarly, for the comparison of means of two independent groups, two-sample t-test is used. However, if the t-test doesn’t satisfy the requirements for two independent samples, then Wilcoxon Rank-Sum is used as it can offer the two independent samples drawn from populations with an ordinal distribution. This test does not assume known distributions, does not deal with parameters, and hence it is considered as a non-parametric test.

Wilcoxon Rank-Sum test also known as Mann-Whitney U test, makes two important assumptions. That is the assumption of independence and equal variance. These assumptions are sufficient for determining if the two populations are different. Additionally, if we assume that the two populations are identical (except for a difference in location), then Wilcoxon Rank-Sum can be utilised as a test of equal means or medians.

Power calculation for Wilcoxon Rank-Sum test

Power is nothing but the probability of rejecting the null hypothesis when it is false. The power calculation for the Wilcoxon Rank-Sum or Mann-Whitney U test is similar to that of the two sample equal-variance t-test except a few modifications are made to the sample size based on the assumed data distribution.

The sample size ni| is equal to ni|= ni/𝑊,

where 𝑊 is known as the Wilcoxon adjustment factor, which is based on the assumed data distribution.

In general, the valid range for the probability of accepting a false null hypothesis is 0 to 1. However, different domains have different standards for setting power.

Sample size conditions

While solving for sample size, the researcher must choose a condition that describes the constraints either on N1 or N2 or both.

Equal (N1 = N2) – This condition is utilised when a researcher has equal sample sizes in each group. Since both sample sizes are solved at once, no additional sample size parameters are required here.
Include N1, solve for N2 – This condition is chosen to fix N1 at some value, and then solve only for N2. However, for some values of N1, N2 value that is large enough to acquire the desired power may be absent.
Enter N2, solve for N1– In case a researcher wants to fix N2 at some value, and then solve only for N1, this condition is used. In this case too, N1 that is large enough to get the desired power might be absent for some values of N2.
Enter R = N2/N1, solve for N1 & N2 – To choose this condition, one must set a suitable value for the ratio of N2 to N1. This is followed by the determination of required N1 & N2 to obtain the desired power using PASS approach. An equivalent representation of R is N2 = R * N1.
Include percentage in group 1, solve for N1 & N2 – Here, the researcher must set a definite value for the percentage of the total sample size in group1. Next, PASS determines the required N1 and N2 with the value of percentage entered to acquire the desired power.
N1 (sample size, group 1) – This condition is used if group allocation = “Enter N1, solve for N2.” Where N1 is the number of individuals sampled from the group 1 population and must be equal or greater than 2. Here a single or a series of values can be entered.
N2 (sample size, group 2) – If group allocation = “Enter N2, solve for N1,” this condition is utilised. Here N2 is the number of individuals sampled from the group 2 population and must be greater or equal to 2. A single or a series of values can be entered in this condition.

The Wilcoxon Rank-Sum test is less sensitive to outliers when compared to that of the two-sample t-test and valid for data from any distribution. However, it reacts to other differences between the distributions such as differences in shape, especially if the focus is on the differences in location between the two distributions. This is considered as the major disadvantage of the Wilcoxon test. Also, when the assumptions of the two-sample t-test hold, this test is less likely to detect a location shift in comparison with the t-test.