research analysis plan

How to Create a Data Analysis Plan: A Detailed Guide

by Barche Blaise | Aug 12, 2020 | Writing

If a good research question equates to a story then, a roadmap will be very vita l for good storytelling. We advise every student/researcher to personally write his/her data analysis plan before seeking any advice. In this blog article, we will explore how to create a data analysis plan: the content and structure.

This data analysis plan serves as a roadmap to how data collected will be organised and analysed. It includes the following aspects:

Clearly states the research objectives and hypothesis
Identifies the dataset to be used
Inclusion and exclusion criteria
Clearly states the research variables
States statistical test hypotheses and the software for statistical analysis
Creating shell tables

1. Stating research question(s), objectives and hypotheses:

All research objectives or goals must be clearly stated. They must be Specific, Measurable, Attainable, Realistic and Time-bound (SMART). Hypotheses are theories obtained from personal experience or previous literature and they lay a foundation for the statistical methods that will be applied to extrapolate results to the entire population.

2. The dataset:

The dataset that will be used for statistical analysis must be described and important aspects of the dataset outlined. These include; owner of the dataset, how to get access to the dataset, how the dataset was checked for quality control and in what program is the dataset stored (Excel, Epi Info, SQL, Microsoft access etc.).

3. The inclusion and exclusion criteria :

They guide the aspects of the dataset that will be used for data analysis. These criteria will also guide the choice of variables included in the main analysis.

4. Variables:

Every variable collected in the study should be clearly stated. They should be presented based on the level of measurement (ordinal/nominal or ratio/interval levels), or the role the variable plays in the study (independent/predictors or dependent/outcome variables). The variable types should also be outlined. The variable type in conjunction with the research hypothesis forms the basis for selecting the appropriate statistical tests for inferential statistics. A good data analysis plan should summarize the variables as demonstrated in Figure 1 below.

Presentation of variables in a data analysis plan

5. Statistical software

There are tons of software packages for data analysis, some common examples are SPSS, Epi Info, SAS, STATA, Microsoft Excel. Include the version number, year of release and author/manufacturer. Beginners have the tendency to try different software and finally not master any. It is rather good to select one and master it because almost all statistical software have the same performance for basic and the majority of advance analysis needed for a student thesis. This is what we recommend to all our students at CRENC before they begin writing their results section .

6. Selecting the appropriate statistical method to test hypotheses

Depending on the research question, hypothesis and type of variable, several statistical methods can be used to answer the research question appropriately. This aspect of the data analysis plan outlines clearly why each statistical method will be used to test hypotheses. The level of statistical significance (p-value) which is often but not always <0.05 should also be written. Presented in figures 2a and 2b are decision trees for some common statistical tests based on the variable type and research question

A good analysis plan should clearly describe how missing data will be analysed.

How to choose a statistical method to determine association between variables

7. Creating shell tables

Data analysis involves three levels of analysis; univariable, bivariable and multivariable analysis with increasing order of complexity. Shell tables should be created in anticipation for the results that will be obtained from these different levels of analysis. Read our blog article on how to present tables and figures for more details. Suppose you carry out a study to investigate the prevalence and associated factors of a certain disease “X” in a population, then the shell tables can be represented as in Tables 1, Table 2 and Table 3 below.

Table 1: Example of a shell table from univariate analysis

Table 2: Example of a shell table from bivariate analysis

Table 3: Example of a shell table from multivariate analysis

aOR = adjusted odds ratio

Now that you have learned how to create a data analysis plan, these are the takeaway points. It should clearly state the:

Research question, objectives, and hypotheses
Dataset to be used
Variable types and their role
Statistical software and statistical methods
Shell tables for univariate, bivariate and multivariate analysis

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Can J Hosp Pharm
v.68(4); Jul-Aug 2015

Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study

There are three kinds of lies: lies, damned lies, and statistics. – Mark Twain 1

INTRODUCTION

Statistics represent an essential part of a study because, regardless of the study design, investigators need to summarize the collected information for interpretation and presentation to others. It is therefore important for us to heed Mr Twain’s concern when creating the data analysis plan. In fact, even before data collection begins, we need to have a clear analysis plan that will guide us from the initial stages of summarizing and describing the data through to testing our hypotheses.

The purpose of this article is to help you create a data analysis plan for a quantitative study. For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2 , 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to summarize study data, and a process to help identify relevant statistical tests. My intention here is to introduce the main elements of data analysis and provide a place for you to start when planning this part of your study. Biostatistical experts, textbooks, statistical software packages, and other resources can certainly add more breadth and depth to this topic when you need additional information and advice.

TERMS AND CONCEPTS USED IN DATA ANALYSIS

When analyzing information from a quantitative study, we are often dealing with numbers; therefore, it is important to begin with an understanding of the source of the numbers. Let us start with the term variable , which defines a specific item of information collected in a study. Examples of variables include age, sex or gender, ethnicity, exercise frequency, weight, treatment group, and blood glucose. Each variable will have a group of categories, which are referred to as values , to help describe the characteristic of an individual study participant. For example, the variable “sex” would have values of “male” and “female”.

Although variables can be defined or grouped in various ways, I will focus on 2 methods at this introductory stage. First, variables can be defined according to the level of measurement. The categories in a nominal variable are names, for example, male and female for the variable “sex”; white, Aboriginal, black, Latin American, South Asian, and East Asian for the variable “ethnicity”; and intervention and control for the variable “treatment group”. Nominal variables with only 2 categories are also referred to as dichotomous variables because the study group can be divided into 2 subgroups based on information in the variable. For example, a study sample can be split into 2 groups (patients receiving the intervention and controls) using the dichotomous variable “treatment group”. An ordinal variable implies that the categories can be placed in a meaningful order, as would be the case for exercise frequency (never, sometimes, often, or always). Nominal-level and ordinal-level variables are also referred to as categorical variables, because each category in the variable can be completely separated from the others. The categories for an interval variable can be placed in a meaningful order, with the interval between consecutive categories also having meaning. Age, weight, and blood glucose can be considered as interval variables, but also as ratio variables, because the ratio between values has meaning (e.g., a 15-year-old is half the age of a 30-year-old). Interval-level and ratio-level variables are also referred to as continuous variables because of the underlying continuity among categories.

As we progress through the levels of measurement from nominal to ratio variables, we gather more information about the study participant. The amount of information that a variable provides will become important in the analysis stage, because we lose information when variables are reduced or aggregated—a common practice that is not recommended. 4 For example, if age is reduced from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 years) we lose the ability to make comparisons across the entire age range and introduce error into the data analysis. 4

A second method of defining variables is to consider them as either dependent or independent. As the terms imply, the value of a dependent variable depends on the value of other variables, whereas the value of an independent variable does not rely on other variables. In addition, an investigator can influence the value of an independent variable, such as treatment-group assignment. Independent variables are also referred to as predictors because we can use information from these variables to predict the value of a dependent variable. Building on the group of variables listed in the first paragraph of this section, blood glucose could be considered a dependent variable, because its value may depend on values of the independent variables age, sex, ethnicity, exercise frequency, weight, and treatment group.

Statistics are mathematical formulae that are used to organize and interpret the information that is collected through variables. There are 2 general categories of statistics, descriptive and inferential. Descriptive statistics are used to describe the collected information, such as the range of values, their average, and the most common category. Knowledge gained from descriptive statistics helps investigators learn more about the study sample. Inferential statistics are used to make comparisons and draw conclusions from the study data. Knowledge gained from inferential statistics allows investigators to make inferences and generalize beyond their study sample to other groups.

Before we move on to specific descriptive and inferential statistics, there are 2 more definitions to review. Parametric statistics are generally used when values in an interval-level or ratio-level variable are normally distributed (i.e., the entire group of values has a bell-shaped curve when plotted by frequency). These statistics are used because we can define parameters of the data, such as the centre and width of the normally distributed curve. In contrast, interval-level and ratio-level variables with values that are not normally distributed, as well as nominal-level and ordinal-level variables, are generally analyzed using nonparametric statistics.

METHODS FOR SUMMARIZING STUDY DATA: DESCRIPTIVE STATISTICS

The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data.

Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable. Data for nominal-level and ordinal-level variables may be interpreted using a pie graph or bar graph . Both options allow us to examine the relative number of participants within each category (by reporting the percentages within each category), whereas a bar graph can also be used to examine absolute numbers. For example, we could create a pie graph to illustrate the proportions of men and women in a study sample and a bar graph to illustrate the number of people who report exercising at each level of frequency (never, sometimes, often, or always).

Interval-level and ratio-level variables may also be interpreted using a pie graph or bar graph; however, these types of variables often have too many categories for such graphs to provide meaningful information. Instead, these variables may be better interpreted using a histogram . Unlike a bar graph, which displays the frequency for each distinct category, a histogram displays the frequency within a range of continuous categories. Information from this type of figure allows us to determine whether the data are normally distributed. In addition to pie graphs, bar graphs, and histograms, many other types of figures are available for the visual representation of data. Interested readers can find additional types of figures in the books recommended in the “Further Readings” section.

Figures are also useful for visualizing comparisons between variables or between subgroups within a variable (for example, the distribution of blood glucose according to sex). Box plots are useful for summarizing information for a variable that does not follow a normal distribution. The lower and upper limits of the box identify the interquartile range (or 25th and 75th percentiles), while the midline indicates the median value (or 50th percentile). Scatter plots provide information on how the categories for one continuous variable relate to categories in a second variable; they are often helpful in the analysis of correlations.

In addition to using figures to present a visual description of the data, investigators can use statistics to provide a numeric description. Regardless of the measurement level, we can find the mode by identifying the most frequent category within a variable. When summarizing nominal-level and ordinal-level variables, the simplest method is to report the proportion of participants within each category.

The choice of the most appropriate descriptive statistic for interval-level and ratio-level variables will depend on how the values are distributed. If the values are normally distributed, we can summarize the information using the parametric statistics of mean and standard deviation. The mean is the arithmetic average of all values within the variable, and the standard deviation tells us how widely the values are dispersed around the mean. When values of interval-level and ratio-level variables are not normally distributed, or we are summarizing information from an ordinal-level variable, it may be more appropriate to use the nonparametric statistics of median and range. The first step in identifying these descriptive statistics is to arrange study participants according to the variable categories from lowest value to highest value. The range is used to report the lowest and highest values. The median or 50th percentile is located by dividing the number of participants into 2 groups, such that half (50%) of the participants have values above the median and the other half (50%) have values below the median. Similarly, the 25th percentile is the value with 25% of the participants having values below and 75% of the participants having values above, and the 75th percentile is the value with 75% of participants having values below and 25% of participants having values above. Together, the 25th and 75th percentiles define the interquartile range .

PROCESS TO IDENTIFY RELEVANT STATISTICAL TESTS: INFERENTIAL STATISTICS

One caveat about the information provided in this section: selecting the most appropriate inferential statistic for a specific study should be a combination of following these suggestions, seeking advice from experts, and discussing with your co-investigators. My intention here is to give you a place to start a conversation with your colleagues about the options available as you develop your data analysis plan.

There are 3 key questions to consider when selecting an appropriate inferential statistic for a study: What is the research question? What is the study design? and What is the level of measurement? It is important for investigators to carefully consider these questions when developing the study protocol and creating the analysis plan. The figures that accompany these questions show decision trees that will help you to narrow down the list of inferential statistics that would be relevant to a particular study. Appendix 1 provides brief definitions of the inferential statistics named in these figures. Additional information, such as the formulae for various inferential statistics, can be obtained from textbooks, statistical software packages, and biostatisticians.

What Is the Research Question?

The first step in identifying relevant inferential statistics for a study is to consider the type of research question being asked. You can find more details about the different types of research questions in a previous article in this Research Primer series that covered questions and hypotheses. 5 A relational question seeks information about the relationship among variables; in this situation, investigators will be interested in determining whether there is an association ( Figure 1 ). A causal question seeks information about the effect of an intervention on an outcome; in this situation, the investigator will be interested in determining whether there is a difference ( Figure 2 ).

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Decision tree to identify inferential statistics for an association.

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Decision tree to identify inferential statistics for measuring a difference.

What Is the Study Design?

When considering a question of association, investigators will be interested in measuring the relationship between variables ( Figure 1 ). A study designed to determine whether there is consensus among different raters will be measuring agreement. For example, an investigator may be interested in determining whether 2 raters, using the same assessment tool, arrive at the same score. Correlation analyses examine the strength of a relationship or connection between 2 variables, like age and blood glucose. Regression analyses also examine the strength of a relationship or connection; however, in this type of analysis, one variable is considered an outcome (or dependent variable) and the other variable is considered a predictor (or independent variable). Regression analyses often consider the influence of multiple predictors on an outcome at the same time. For example, an investigator may be interested in examining the association between a treatment and blood glucose, while also considering other factors, like age, sex, ethnicity, exercise frequency, and weight.

When considering a question of difference, investigators must first determine how many groups they will be comparing. In some cases, investigators may be interested in comparing the characteristic of one group with that of an external reference group. For example, is the mean age of study participants similar to the mean age of all people in the target group? If more than one group is involved, then investigators must also determine whether there is an underlying connection between the sets of values (or samples ) to be compared. Samples are considered independent or unpaired when the information is taken from different groups. For example, we could use an unpaired t test to compare the mean age between 2 independent samples, such as the intervention and control groups in a study. Samples are considered related or paired if the information is taken from the same group of people, for example, measurement of blood glucose at the beginning and end of a study. Because blood glucose is measured in the same people at both time points, we could use a paired t test to determine whether there has been a significant change in blood glucose.

What Is the Level of Measurement?

As described in the first section of this article, variables can be grouped according to the level of measurement (nominal, ordinal, or interval). In most cases, the independent variable in an inferential statistic will be nominal; therefore, investigators need to know the level of measurement for the dependent variable before they can select the relevant inferential statistic. Two exceptions to this consideration are correlation analyses and regression analyses ( Figure 1 ). Because a correlation analysis measures the strength of association between 2 variables, we need to consider the level of measurement for both variables. Regression analyses can consider multiple independent variables, often with a variety of measurement levels. However, for these analyses, investigators still need to consider the level of measurement for the dependent variable.

Selection of inferential statistics to test interval-level variables must include consideration of how the data are distributed. An underlying assumption for parametric tests is that the data approximate a normal distribution. When the data are not normally distributed, information derived from a parametric test may be wrong. 6 When the assumption of normality is violated (for example, when the data are skewed), then investigators should use a nonparametric test. If the data are normally distributed, then investigators can use a parametric test.

ADDITIONAL CONSIDERATIONS

What is the level of significance.

An inferential statistic is used to calculate a p value, the probability of obtaining the observed data by chance. Investigators can then compare this p value against a prespecified level of significance, which is often chosen to be 0.05. This level of significance represents a 1 in 20 chance that the observation is wrong, which is considered an acceptable level of error.

What Are the Most Commonly Used Statistics?

In 1983, Emerson and Colditz 7 reported the first review of statistics used in original research articles published in the New England Journal of Medicine . This review of statistics used in the journal was updated in 1989 and 2005, 8 and this type of analysis has been replicated in many other journals. 9 – 13 Collectively, these reviews have identified 2 important observations. First, the overall sophistication of statistical methodology used and reported in studies has grown over time, with survival analyses and multivariable regression analyses becoming much more common. The second observation is that, despite this trend, 1 in 4 articles describe no statistical methods or report only simple descriptive statistics. When inferential statistics are used, the most common are t tests, contingency table tests (for example, χ 2 test and Fisher exact test), and simple correlation and regression analyses. This information is important for educators, investigators, reviewers, and readers because it suggests that a good foundational knowledge of descriptive statistics and common inferential statistics will enable us to correctly evaluate the majority of research articles. 11 – 13 However, to fully take advantage of all research published in high-impact journals, we need to become acquainted with some of the more complex methods, such as multivariable regression analyses. 8 , 13

What Are Some Additional Resources?

As an investigator and Associate Editor with CJHP , I have often relied on the advice of colleagues to help create my own analysis plans and review the plans of others. Biostatisticians have a wealth of knowledge in the field of statistical analysis and can provide advice on the correct selection, application, and interpretation of these methods. Colleagues who have “been there and done that” with their own data analysis plans are also valuable sources of information. Identify these individuals and consult with them early and often as you develop your analysis plan.

Another important resource to consider when creating your analysis plan is textbooks. Numerous statistical textbooks are available, differing in levels of complexity and scope. The titles listed in the “Further Reading” section are just a few suggestions. I encourage interested readers to look through these and other books to find resources that best fit their needs. However, one crucial book that I highly recommend to anyone wanting to be an investigator or peer reviewer is Lang and Secic’s How to Report Statistics in Medicine (see “Further Reading”). As the title implies, this book covers a wide range of statistics used in medical research and provides numerous examples of how to correctly report the results.

CONCLUSIONS

When it comes to creating an analysis plan for your project, I recommend following the sage advice of Douglas Adams in The Hitchhiker’s Guide to the Galaxy : Don’t panic! 14 Begin with simple methods to summarize and visualize your data, then use the key questions and decision trees provided in this article to identify relevant statistical tests. Information in this article will give you and your co-investigators a place to start discussing the elements necessary for developing an analysis plan. But do not stop there! Use advice from biostatisticians and more experienced colleagues, as well as information in textbooks, to help create your analysis plan and choose the most appropriate statistics for your study. Making careful, informed decisions about the statistics to use in your study should reduce the risk of confirming Mr Twain’s concern.

Appendix 1. Glossary of statistical terms * (part 1 of 2)

1-way ANOVA: Uses 1 variable to define the groups for comparing means. This is similar to the Student t test when comparing the means of 2 groups.
Kruskall–Wallis 1-way ANOVA: Nonparametric alternative for the 1-way ANOVA. Used to determine the difference in medians between 3 or more groups.
n -way ANOVA: Uses 2 or more variables to define groups when comparing means. Also called a “between-subjects factorial ANOVA”.
Repeated-measures ANOVA: A method for analyzing whether the means of 3 or more measures from the same group of participants are different.
Freidman ANOVA: Nonparametric alternative for the repeated-measures ANOVA. It is often used to compare rankings and preferences that are measured 3 or more times.
Fisher exact: Variation of chi-square that accounts for cell counts < 5.
McNemar: Variation of chi-square that tests statistical significance of changes in 2 paired measurements of dichotomous variables.
Cochran Q: An extension of the McNemar test that provides a method for testing for differences between 3 or more matched sets of frequencies or proportions. Often used as a measure of heterogeneity in meta-analyses.
1-sample: Used to determine whether the mean of a sample is significantly different from a known or hypothesized value.
Independent-samples t test (also referred to as the Student t test): Used when the independent variable is a nominal-level variable that identifies 2 groups and the dependent variable is an interval-level variable.
Paired: Used to compare 2 pairs of scores between 2 groups (e.g., baseline and follow-up blood pressure in the intervention and control groups).

Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of Physicians; 2006.

Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003.

Plichta SB, Kelvin E. Munro’s statistical methods for health care research . 6th ed. Philadelphia (PA): Wolters Kluwer Health/ Lippincott, Williams & Wilkins; 2013.

This article is the 12th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.
Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.
Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.
Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.
Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.
Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.
Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.
Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.
Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.
Sutton J, Austin Z. Qualitative research: data collection, analysis, and management. Can J Hosp Pharm . 2014;68(3):226–31.
Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm. 2014;68(3):232–7.

Competing interests: None declared.

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Guide to the statistical analysis plan

Affiliations.

1 Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.
2 Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania.
PMID: 30609103
DOI: 10.1111/pan.13576

Biomedical research has been struck with the problem of study findings that are not reproducible. With the advent of large databases and powerful statistical software, it has become easier to find associations and form conclusions from data without forming an a-priori hypothesis. This approach may yield associations without clinical relevance, false positive findings, or biased results due to "fishing" for the desired results. To improve reproducibility, transparency, and validity among clinical trials, the National Institute of Health recently updated its grant application requirements, which mandates registration of clinical trials and submission of the original statistical analysis plan (SAP) along with the research protocol. Many leading journals also require the SAP as part of the submission package. The goal of this article and the companion article detailing the SAP of an actual research study is to provide a practical guide on writing an effective SAP. We describe the what, why, when, where, and who of a SAP, and highlight the key contents of the SAP.

Keywords: SAP; reproducibility; research methodology; statistical analysis; transparency; validity.

Publication types

Biomedical Research / standards*
Data Interpretation, Statistical
Databases, Factual
Reproducibility of Results
Research Design
Statistics as Topic / standards*

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense.

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words.

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

Fraud: To ensure an actual human being records each response to the survey or the questionnaire
Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
Procedure: To ensure ethical standards were maintained while collecting the data sample
Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

Count, Percent, Frequency
It is used to denote home often a particular event occurs.
Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

Mean, Median, Mode
The method is widely used to demonstrate distribution by various points.
Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

Range, Variance, Standard deviation
Here the field equals high/low points.
Variance standard deviation = difference between the observed score and mean
It is used to identify the spread of scores by stating intervals.
Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

Percentile ranks, Quartile ranks
It relies on standardized scores helping researchers to identify the relationship between different scores.
It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie.

Here are two significant areas of inferential statistics.

Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables. Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.
Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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2.3 Data management and analysis

Learning objectives.

Learners will be able to…

Define and construct a data analysis plan
Define key quantitative data management terms—variable name, data dictionary, and observations/cases
Differentiate between univariate and bivariate quantitative analysis
Explain when we might use quantitative bivariate analysis in social work research
Identify how your qualitative research question, research aim, and type of data may influence your choice of analytic methods
Outline the steps you will take in preparation for conducting qualitative data analysis

After you have your raw data, whether this is secondary data or data you collected yourself, you will need to analyze it. While the specific steps to follow in quantitative or qualitative data analysis are beyond the scope of this chapter, we are going to address some basic concepts in this section to help you create a data analysis plan. A data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. If you look back at Table 2.1, you will see that creating a data analysis plan is a part of the study design process. The data analysis plan flows from the research question, is integral to the study design, and should be well conceptualized prior to beginning data collection. In this section, we will walk through the basics of quantitative and qualitative data analysis to help you understand the fundamentals of creating a data analysis plan.

Quantitative Data: Management

When considering what data you might want to collect as part of your project, there are two important considerations that can create dilemmas for researchers. You might only get one chance to interact with your participants, so you must think comprehensively in your planning phase about what information you need and collect as much relevant data as possible. At the same time, though, especially when collecting sensitive information, you need to consider how onerous the data collection is for participants and whether you really need them to share that information. Just because something is interesting to us doesn’t mean it’s related enough to our research question to chase it down. Work with your research team and/or faculty early in your project to talk through these issues before you get to this point. And if you’re using secondary data, make sure you have access to all the information you need in that data before you use it.

Once you’ve collected your quantitative data, you need to make sure it is well-organized in a database in a way that’s actually usable. “Database” can be kind of a scary word, but really, it can be as simple as an Excel spreadsheet or a data file in whatever program you’re using to analyze your data. You may want to avoid Excel and use a formal database such as Microsoft Access or MySQL if you’ve got a large or complicated data set. But if your data set is smaller and you plan to keep your analyses simple, you can definitely get away with Excel. A typical data set is organized with variables as columns and observations/cases as rows. For example, let’s say we did a survey on ice cream preferences and collected the following information in Table 2.3:

There are a few key data management terms to understand:

Variable name : Just what it sounds like—the name of your variable. Make sure this is something useful, short and, if you’re using something other than Excel, all one word. Most statistical programs will automatically rename variables for you if they aren’t one word, but the names can be a little ridiculous and long.
Observations/cases : The rows in your data set. In social work, these are often your study participants (people), but can be anything from census tracts to black bears to trains. When we talk about sample size, we’re talking about the number of observations/cases. In our mini data set, each person is an observation/case.
Data dictionary (also called a code book or metadata) : This is the document where you list your variable names, what the variables actually measure or represent, what each of the values of the variable mean if the meaning isn’t obvious (i.e., if there are numbers assigned to gender), the level of measurement and anything special to know about the variables (for instance, the source if you mashed two data sets together). If you’re using secondary data, the researchers sharing the data should make the data dictionary available.

Let’s take that mini data set we’ve got up above and we’ll show you what your data dictionary might look like in Table 2.4.

Quantitative Data: Univariate Analysis

As part of planning for your research, you should come up with a data analysis plan. Remember, a data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. A basic data analysis plan might look something like what you see in Table 2.5. Don’t panic if you don’t yet understand some of the statistical terms in the plan; we’re going to delve into some of them in this section, and others will be covered in more depth in your statistics courses. Note here also that this is what operationalizing your variables and moving through your research with them looks like on a basic level. We will cover operationalization in more depth in Chapter 10.

An important point to remember is that you should never get stuck on using a particular statistical method because you or one of your co-researchers thinks it’s cool or it’s the hot thing in your field right now. You should certainly go into your data analysis plan with ideas, but in the end, you need to let your research question guide what statistical tests you plan to use. Be prepared to be flexible if your plan doesn’t pan out because the data is behaving in unexpected ways.

You’ll notice that the first step in the quantitative data analysis plan is univariate and descriptive statistics. Univariate data analysis is a quantitative method in which a variable is examined individually to determine its distribution , or the way the scores are distributed across the levels, or values, of that variable. When we talk about levels , what we are talking about are the possible values of the variable—like a participant’s age, income or gender. (Note that this is different from levels of measurement , which will be discussed in Chapter 11, but the level of measurement of your variables absolutely affects what kinds of analyses you can do with it.) Univariate analysis is non-relational , which just means that we’re not looking into how our variables relate to each other. Instead, we’re looking at variables in isolation to try to understand them better. For this reason, univariate analysis is used for descriptive research questions.

So when do you use univariate data analysis? Always! It should be the first thing you do with your quantitative data, whether you are planning to move on to more sophisticated statistical analyses or are conducting a study to describe a new phenomenon. You need to understand what the values of each variable look like—what if one of your variables has a lot of missing data because participants didn’t answer that question on your survey? What if there isn’t much variation in the gender of your sample? These are things you’ll learn through univariate analysis.

Quantitative Data: Bivariate Analysis

Did you know that ice cream causes shark attacks? It’s true! When ice cream sales go up in the summer, so does the rate of shark attacks. So you’d better put down that ice cream cone, unless you want to make yourself look more delicious to a shark.

Photo of shark with open mouth emerging from water

Ok, so it’s quite obviously not true that ice cream causes shark attacks. But if you looked at these two variables and how they’re related, you’d notice that during times of the year with high ice cream sales, there are also the most shark attacks. This is a classic example of the difference between correlation and causation. Despite the fact that the conclusion we drew about causation was wrong, it’s nonetheless true that these two variables appear related, and researchers figured that out through the use of bivariate analysis.

Bivariate analysis consists of a group of statistical techniques that examine the association between two variables. We could look at how anti-depressant medications and appetite are related, whether there is a relation between having a pet and emotional well-being, or if a policy-maker’s level of education is related to how they vote on bills related to environmental issues.

Bivariate analysis forms the foundation of multivariate analysis, which we don’t get to in this book. All you really need to know here is that there are steps beyond bivariate analysis, which you’ve undoubtedly seen in scholarly literature already! But before we can move forward with multivariate analysis, we need to understand the associations between the variables in our study.

Throughout your PhD program, you will learn much more about quantitative data analysis techniques, including more sophisticated multivariate analysis methods. Hopefully this section has provided you with some initial insights into how data is analyzed, and the importance of creating a data analysis plan prior to collecting data. Next, we will discuss some basic strategies for creating a qualitative data analysis plan.

Resources for Quantitative Data Analysis

While you are affiliated with a university, it is likely that you will have access to some kind of commercial statistics software. Examples in the previous section uses SPSS, the most common one our authoring team has seen in social work education. Like its competitors SAS and STATA, SPSS is expensive and your license to the software must be renewed every year (like a subscription). Even if you are able to install commercial statistics software on your computer, once your license expires, your program will no longer work. We believe that forcing students to learn software they will never use is wasteful and contributes to the (accurate, in many cases) perception from students that research class is unrelated to real-world practice. SPSS is more accessible due to its graphical user interface and does not require researchers to learn basic computer programming, but it is prohibitively costly if a student wanted to use it to measure practice data in their agency post-graduation.

Instead, we suggest getting familiar with JASP Statistics , a free and open-source alternative to SPSS developed and supported by the University of Amsterdam. It has a similar user interface as SPSS, and should be similarly easy to learn. Moreover, usability upgrades from SPSS like generating APA formatted tables make it a compelling option. While a great many of my students will rely on statistical analyses of their programs and practices in reports to funders, it is unlikely that any will use SPSS. Browse JASP’s how-to guide or consult this textbook Learning Statistics with JASP: A Tutorial for Psychology Students and Other Beginners , written by Danielle J. Navarro , David R. Foxcroft , and Thomas J. Faulkenberry .

Another open source statistics software package is R (a.k.a. The R Project for Statistical Computing ). R uses a command line interface, so you will need some coding knowledge in order to use it. Luckily, R is the most commonly used statistics software in the world, and the community of support and guides for using R are omnipresent online. For beginning researchers, consult the textbook Learning Statistics with R: A tutorial for psychology students and other beginners by Danielle J. Navarro .

While statistics software is sometimes needed to perform advanced statistical tests, most univariate and bivariate tests can be performed in spreadsheet software like Microsoft Excel, Google Sheets, or the free and open source LibreOffice Calc . Microsoft includes a ToolPak to perform complex data analysis as an add-on to Excel. For more information on using spreadsheet software to perform statistics, the open textbook Collaborative Statistics Using Spreadsheets by Susan Dean, Irene Mary Duranczyk, Barbara Illowsky, Suzanne Loch, and Janet Stottlemyer.

Statistical analysis is performed in just about every discipline, and as a result, there are a lot of openly licensed, free resources to assist you with your data analysis. We have endeavored to provide you the basics in the past few chapters, but ultimately, you will likely need additional support in completing quantitative data analysis from an instructor, textbook, or other resource. Browse the Open Textbook Library for statistics resources or look for video tutorials from reputable instructors like this video textbook on statistics by Bryan Koenig .

Qualitative Data: Management

Qualitative research often involves human participants and qualitative data can include of recordings or transcripts of their words, photographs or images, or diaries and documents. The personal nature of qualitative data poses the challenge of recognizability of sensitive information on individuals, communities, and places. If you choose this methodology for your research, you should familiarize yourself with policies, procedures, and rules to ensure safety and security of data in the documentation and dissemination process.

In any research involving primary data, a researcher is not only entrusted with the responsibility of upholding privacy of their participants but also accountable to them, making confidentiality and human subjects’ protection front and center of qualitative data management. Data such as audiotapes, videotapes, transcripts, notes, and other records should be stored and secured in locations where only authorized persons have access to them.

Sometimes in qualitative research, you will learn intimate details about people’s lives. Often, qualitative data contain personal identifiers. A helpful practice to ensure that participants confidentiality is to replace personal information in transcripts with pseudonyms or descriptive language (e.g., “[the participant’s sister]” instead of the sister’s name). Once audio and video recordings have been accurately transcribed with the de-identification of personal identifiers, the original recordings should be destroyed.

Qualitative Data: Analysis

There are many different types of qualitative data, including transcripts of interviews and focus groups, observational data, documents and other artifacts, and more. Your qualitative data analysis plan should be anchored in the type of data collected and the purpose of your study. Qualitative research can serve a range of purposes. Below is a brief list of general purposes we might consider when using a qualitative approach.

Are you trying to understand how a particular group is affected by an issue?
Are you trying to uncover how people arrive at a decision in a given situation?
Are you trying to examine different points of view on the impact of a recent event?
Are you trying to summarize how people understand or make sense of a condition?
Are you trying to describe the needs of your target population?

If you don’t see the general aim of your research question reflected in one of these areas, don’t fret! This is only a small sampling of what you might be trying to accomplish with your qualitative study. Whatever your aim, you need to have a plan for what you will do once you have collected your data.

Iterative or Linear

Some qualitative research is linear , meaning it follows more of a traditionally quantitative process: create a plan, gather data, and analyze data; each step is completed before we proceed to the next. You can think of this like how information is presented in this book. We discuss each topic, one after another.

However, many times qualitative research is iterative , or evolving in cycles. An iterative approach means that once we begin collecting data, we also begin analyzing data as it is coming in. This early and ongoing analysis of our (incomplete) data then impacts our continued planning, data gathering and future analysis. Again, coming back to this book, while it may be written linear, we hope that you engage with it iteratively as you design and conduct your own research. By this we mean that you will revisit previous sections so you can understand how they fit together and you are in continuous process of building and revising how you think about the concepts you are learning about.

As you may have guessed, there are benefits and challenges to both linear and iterative approaches. A linear approach is much more straightforward, each step being fairly defined. However, linear research being more defined and rigid also presents certain challenges. A linear approach assumes that we know what we need to ask or look for at the very beginning of data collection, which often is not the case. Figure 2.1 contrasts the two approaches.

Comparison of linear and iterative systematic approaches. Linear approach box is a series of boxes with arrows between them in a line. The first box is "create a plan", then "gather data", ending with "analyze data". The iterative systematic approach is a series of boxes in a circle with arrows between them, with the boxes labeled "planning", "data gathering", and "analyzing the data".

With iterative research, we have more flexibility to adapt our approach as we learn new things. We still need to keep our approach systematic and organized, however, so that our work doesn’t become a free-for-all. As we adapt, we do not want to stray too far from the original premise of our study. It’s also important to remember with an iterative approach that we may risk ethical concerns if our work extends beyond the original boundaries of our informed consent and institutional review board agreement (IRB; see Chapter 3 for more on IRBs). If you feel that you do need to modify your original research plan in a significant way as you learn more about the topic, you can submit an addendum to modify your original application that was submitted. Make sure to keep detailed notes of the decisions that you are making and what is informing these choices. This helps to support transparency and your credibility throughout the research process.

Acquainting yourself with your data

As you begin your analysis, you need to get to know your data. This often means reading through your data prior to any attempt at breaking it apart and labeling it. You might read through a couple of times, in fact. This helps give you a more comprehensive feel for each piece of data and the data as a whole, again, before you start to break it down into smaller units or deconstruct it. This is especially important if others assisted us in the data collection process. We often gather data as part of team and everyone involved in the analysis needs to be very familiar with all of the data.

Capturing your emerging understanding of the data

During your reviewing you will start to develop and evolve your understanding of what the data means. Coding is a part of the qualitative data analysis process where we begin to interpret and assign meaning to the data. It represents one of the first steps as we begin to filter the data through our own subjective lens as the researcher. This understanding of the data should be dynamic and flexible, but you want to have a way to capture this understanding as it evolves. You may include this as part of your qualitative codebook where you are tracking the main ideas that are emerging and what they mean. Table 2.6 is an example of how your thinking might change about a code and how you can go about capturing it.

There are a variety of different approaches to qualitative analysis, including thematic analysis, content analysis, grounded theory, phenomenology, photovoice, and more. The specific steps you will take to code your qualitative data, and to generate themes from these codes, will vary based on the analytic strategy you are employing. In designing your qualitative study, you would identify an analytical approach as you plan out your project. The one you select would depend on the type of data you have and what you want to accomplish with it. In Chapter 19, we will go into more detail about various types of qualitative data analysis. Each qualitative approach has specific techniques and methods that take substantial study and practice to master.

Key Takeaways

Getting organized at the beginning of your project with a data analysis plan will help keep you on track. Data analysis plans should include your research question, a description of your data, and a step-by-step outline of what you’re going to do with it. [chapter 14.1]
Be flexible with your data analysis plan—sometimes data surprises us and we have to adjust the statistical tests we are using. [chapter 14.1]
Always make a data dictionary or, if using secondary data, get a copy of the data dictionary so you (or someone else) can understand the basics of your data. [chapter 14.1]
Bivariate analysis is a group of statistical techniques that examine the relationship between two variables. [chapter 15.1]
You need to conduct bivariate analyses before you can begin to draw conclusions from your data, including in future multivariate analyses. [chapter 15.1]
There are a lot of high quality and free online resources to learn and perform statistical analysis.
Qualitative research analysis requires preparation and careful planning. You will need to take time to familiarize yourself with the data in a general sense before you begin analyzing. [chapter 19.3]
The specific steps you will take to code your qualitative data and generate final themes will depend on the qualitative analytic approach you select.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

Make a data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you’re going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. You can do this exercise whether you’re using quantitative or qualitative data! The same principles apply.
Make a data dictionary for the data you are proposing to collect as part of your study. You can use the example above as a template.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are researching the impact of your city’s recent harm reduction interventions for intravenous drug users (e.g., sterile injection kits, monitored use, overdose prevention, naloxone provision, etc.).

Make a draft quantitative data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you’re going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. It’s okay if you don’t yet have a complete idea of the types of statistical analyses you might use.

An ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact analyses, step-by-step, that you plan to run to answer your research question.

The name of your variable.

The rows in your data set. In social work, these are often your study participants (people), but can be anything from census tracts to black bears to trains.

This is the document where you list your variable names, what the variables actually measure or represent, what each of the values of the variable mean if the meaning isn't obvious.

process by which researchers spell out precisely how a concept will be measured in their study

A group of statistical techniques that examines the relationship between at least three variables

Univariate data analysis is a quantitative method in which a variable is examined individually to determine its distribution.

the way the scores are distributed across the levels of that variable.

Chapter Outline

Practical and ethical considerations ( 14 minute read)
Raw data (10 minute read)
Creating a data analysis plan (?? minute read)
Critical considerations (3 minute read)

Content warning: Examples in this chapter discuss substance use disorders, mental health disorders and therapies, obesity, poverty, gun violence, gang violence, school discipline, racism and hate groups, domestic violence, trauma and triggers, incarceration, child neglect and abuse, bullying, self-harm and suicide, racial discrimination in housing, burnout in helping professions, and sex trafficking of indigenous women.

2.1 Practical and ethical considerations

Learners will be able to...

Identify potential stakeholders and gatekeepers
Differentiate between raw data and the results of scientific studies
Evaluate whether you can feasibly complete your project

Pre-awareness check (Knowledge)

Similar to practice settings, research has ethical considerations that must be taken to ensure the safety of participants. What ethical considerations were relevant to your practice experience that may have impacted the delivery of services?

As a PhD student, you will have many opportunities to conduct research. You may be asked to be a part of a research team led by the faculty at your institution. You will also conduct your own research for your dissertation. As you will learn, research can take many forms. For example, you may want to focus qualitatively on individuals’ lived experiences, or perhaps you will quantitatively assess the impact of interventions on research subjects. You may work with large, already-existing datasets, or you may create your own data. Though social work research can vary widely from project to project, researchers typically follow the same general process, even if their specific research questions and methodologies differ. Table 2.1 outlines the major components of the research process covered in this textbook, and indicates the chapters where you will find more information on each subject. You will notice that your research paradigm is an organizing framework that guides each component of the research process.

Table 2.1 Components of the Research Process

Feasibility

Feasibility refers to whether you can practically conduct the study you plan to do, given the resources and ethical obligations you have. In this chapter, we will review some important practical and ethical considerations researchers should start thinking about from the beginning of a research project. These considerations apply to all research, but it is important to also consider the context of research and researchers when thinking about feasibility.

For example, as a doctoral student, you likely have a unique set of circumstances that inspire and constrain your research. Some students have the ability to engage in independent studies where they can gain skills and expertise in specialized research methods to prepare them for a research-intensive career. Others may have reasons, such as a limited amount of funding or family concerns, that encourage them to complete their dissertation research as quickly as possible. These circumstances relate to the feasibility of a research project. Regardless of the potential societal importance of a 10-year longitudinal study, it’s not feasible for a student to conduct it in time to graduate! Your dissertation chair, doctoral program director, and other faculty mentors can help you navigate the many decisions you will face as a doctoral student about conducting independent research or joining research projects.

The context and role of the researcher continue to affect feasibility even after a doctoral student graduates. Many will continue in their careers to become tenure track faculty with research expectations to obtain tenure. Some funders expect faculty members to have a track record of successful projects before trusting them to lead expensive or long-term studies. Realistically, these expectations will influence what research is feasible for a junior faculty member to conduct. Just like for doctoral students, mentorship is incredibly valuable for junior faculty to make informed decisions about what research to conduct. Senior faculty, associate deans of research, chairs, and deans can help junior faculty decide what projects to pursue to ensure they meet the expectations placed on them without losing sight of the reasons they became a researcher in the first place.

As you read about other feasibility considerations such as gaining access, consent, and collecting data, consider the ways in which context and roles also influence feasibility.

Access, consent, and ethical obligations

One of the most important feasibility issues is gaining access to your target population. For example, let’s say you wanted to better understand middle-school students who engaged in self-harm behaviors. That is a topic of social importance, but what challenges might you face in accessing this population? Let's say you proposed to identify students from a local middle school and interview them about self-harm. Methodologically, that sounds great since you are getting data from those with the most knowledge about the topic, the students themselves. But practically, that sounds challenging. Think about the ethical obligations a social work practitioner has to adolescents who are engaging in self-harm (e.g., competence, respect). In research, we are similarly concerned mostly with the benefits and harms of what you propose to do as well as the openness and honesty with which you share your project publicly.

Gatekeepers

If you were the principal at your local middle school, would you allow researchers to interview kids in your schools about self-harm? What if the results of the study showed that self-harm was a big problem that your school was not addressing? What if the researcher's interviews themselves caused an increase in self-harming behaviors among the children? The principal in this situation is a gatekeeper . Gatekeepers are the individuals or organizations who control access to the population you want to study. The school board would also likely need to give consent for the research to take place at their institution. Gatekeepers must weigh their ethical questions because they have a responsibility to protect the safety of the people at their organization, just as you have an ethical obligation to protect the people in your research study.

For vulnerable populations, it can be a challenge to get consent from gatekeepers to conduct your research project. As a result, researchers often conduct research projects in places where they have established trust with gatekeepers. In the case where the population (children who self-harm) are too vulnerable, researchers may collect data from people who have secondary knowledge about the topic. For example, the principal may be more willing to let you talk to teachers or staff, rather than children.

Stakeholders

In some cases, researchers and gatekeepers partner on a research project. When this happens, the gatekeepers become stakeholders . Stakeholders are individuals or groups who have an interest in the outcome of the study you conduct. As you think about your project, consider whether there are formal advisory groups or boards (like a school board) or advocacy organizations who already serve or work with your target population. Approach them as experts and ask for their review of your study to see if there are any perspectives or details you missed that would make your project stronger.

There are many advantages to partnering with stakeholders to complete a research project together. Continuing with our example on self-harm in schools, in order to obtain access to interview children at a middle school, you will have to consider other stakeholders' goals. School administrators also want to help students struggling with self-harm, so they may want to use the results to form new programs. But they may also need to avoid scandal and panic if the results show high levels of self-harm. Most likely, they want to provide support to students without making the problem worse. By bringing in school administrators as stakeholders, you can better understand what the school is currently doing to address the issue and get an informed perspective on your project's questions. Negotiating the boundaries of a stakeholder relationship requires strong meso-level practice skills.

Of course, partnering with administrators probably sounds quite a bit easier than bringing on board the next group of stakeholders—parents. It's not ethical to ask children to participate in a study without their parents' consent. We will review the parameters of parental and child consent in Chapter 5 . Parents may be understandably skeptical of a researcher who wants to talk to their child about self-harm, and they may fear potential harm to the child and family from your study. Would you let a researcher you didn't know interview your children about a very sensitive issue?

Social work research must often satisfy multiple stakeholders. This is especially true if a researcher receives a grant to support the project, as the funder has goals it wants to accomplish by funding the research project. Your university is also a stakeholder in your project. When you conduct research, it reflects on your school. If you discover something of great importance, your school looks good. If you harm someone, they may be liable. Your university likely has opportunities for you to share your research with the campus community, and may have incentives or grant programs for researchers. Your school also provides you with support and access to resources like the library and data analysis software.

Target population

So far, we've talked about access in terms of gatekeepers and stakeholders. Let's assume all of those people agree that your study should proceed. But what about the people in the target population? They are the most important stakeholder of all! Think about the children in our proposed study on self-harm. How open do you think they would be to talking to you about such a sensitive issue? Would they consent to talk to you at all?

Maybe you are thinking about simply asking clients on your caseload. As we talked about before, leveraging existing relationships created through field work can help with accessing your target population. However, they introduce other ethical issues for researchers. Asking clients on your caseload or at your agency to participate in your project creates a dual relationship between you and your client. What if you learn something in the research project that you want to share with your clinical team? More importantly, would your client feel uncomfortable if they do not consent to your study? Social workers have power over clients, and any dual relationship would require strict supervision in the rare case it was allowed.

Resources and scope

Let's assume everyone consented to your project and you have adequately addressed any ethical issues with gatekeepers, stakeholders, and your target population. That means everything is ready to go, right? Not quite yet. As a researcher, you will need to carry out the study you propose to do. Depending on how big or how small your proposed project is, you’ll need a little or a lot of resources.

One thing that all projects need is raw data . Raw data can come in may forms. Very often in social science research, raw data includes the responses to a survey or transcripts of interviews and focus groups, but raw data can also include experimental results, diary entries, art, or other data points that social scientists use in analyzing the world. Primary data is data you have collected yourself. Sometimes, social work researchers do not collect raw data of their own, but instead use secondary data analysis to analyze raw data that has been shared by other researchers. Secondary data is data someone else has collected that you have permission to use in your research. For example, you could use data from a local probation program to determine if a shoplifting prevention group was reducing the rate at which people were re-offending. You would need data on who participated in the program and their criminal history six months after the end of their probation period. This is secondary data you could use to determine whether the shoplifting prevention group had any effect on an individual's likelihood of re-offending. Whether a researcher should use secondary data or collect their own raw data is an important choice which we will discuss in greater detail in section 2.2. Collecting raw data or obtaining secondary data can be time consuming or expensive, but without raw data there can be no research project.

Time is an important resource to consider when designing research projects. Make sure that your proposal won't require you to spend more time than you have to collect and analyze data. Think realistically about the timeline for your research project. If you propose to interview fifty mental health professionals in their offices in your community about your topic, make sure you can dedicate fifty hours to conduct those interviews, account for travel time, and think about how long it will take to transcribe and analyze those interviews.

What is reasonable for you to do in your timeframe?
How many hours each week can the research team dedicate to this project?

One thing that can delay a research project is receiving approval from the institutional review board (IRB), the research ethics committee at your university. If your study involves human subjects , you may have to formally propose your study to the IRB and get their approval before gathering your data. A well-prepared study is likely to gain IRB approval with minimal revisions needed, but the process can take weeks to complete and must be done before data collection can begin. We will address the ethical obligations of researchers in greater detail in Chapter 5 .

Most research projects cost some amount of money. Potential expenses include wages for members of the research team, incentives for research participants, travel expenses, and licensing costs for standardized instruments. Most researchers seek grant funding to support the research. Grant applications can be time consuming to write and grant funding can be competitive to receive.

Knowledge, competence, and skills

For social work researchers, the social work value of competence is key in their research ethics.

Clearly, researchers need to be skilled in working with their target population in order to conduct ethical research. Some research addresses this challenge by collecting data from competent practitioners or administrators who have second-hand knowledge of target populations based on professional relationships. Members of the research team delivering an intervention also need to have training and skills in the intervention. For example, if a research study examines the effectiveness of dialectical behavioral therapy (DBT) in a particular context, the person delivering the DBT must be certified in DBT. Another idea to keep in mind is the level of data collection and analysis skills needed to complete the project. Some assessments require training to administer. Analyses may be complex or require statistical consultation or advanced training.

In summary, here are a few questions you should ask yourself about your project to make sure it's feasible. While we present them early on in the research process (we're only in Chapter 2), these are certainly questions you should ask yourself throughout the proposal writing process. We will revisit feasibility again in Chapter 9 when we work on finalizing your research question .

Do you have access to the data you need or can you collect the data you need?
Will you be able to get consent from stakeholders, gatekeepers, and your target population?
Does your project pose risk to individuals through direct harm, dual relationships, or breaches in confidentiality?
Are you competent enough to complete the study?
Do you have the resources and time needed to carry out the project?
People will have to say “yes” to your research project. Evaluate whether your project might have gatekeepers or potential stakeholders. They may control access to data or potential participants.
Researchers need raw data such as survey responses, interview transcripts, or client charts. Your research project must involve more than looking at the analyses conducted by other researchers, as the literature review is only the first step of a research project.
Make sure you have enough resources (time, money, and knowledge) to complete your research project.

Post-awareness check (Emotion)

What factors have created your passion toward assisting your target population? How can this connection enhance your ability to receive a “yes” from potential participants? What are the anticipated challenges to receiving a “yes” from potential participants?

Think about how you might answer your question by collecting your own data.

Identify any gatekeepers and stakeholders you might need to contact.
How can you increase the likelihood you will get access to the people or records you need for your study?

Describe the resources you will need for your project.

Do you have concerns about feasibility?

TRACK 2 (IF YOU AREN'T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are researching the impact of your city's recent harm reduction interventions for intravenous drug users (e.g., sterile injection kits, monitored use, overdose prevention, naloxone provision, etc.).

Thinking about the services related to this issue in your own city, identify any gatekeepers and stakeholders you might need to contact.
How might you approach these gatekeepers and stakeholders? How would you explain your study?

2.2 Raw data

Identify potential sources of available data
Weigh the challenges and benefits of collecting your own data

In our previous section, we addressed some of the challenges researchers face in collecting and analyzing raw data. Just as a reminder, raw data are unprocessed, unanalyzed data that researchers analyze using social science research methods. It is not just the statistics or qualitative themes in journal articles. It is the actual data from which those statistical outputs or themes are derived (e.g., interview transcripts or survey responses).

There are two approaches to getting raw data. First, students can analyze data that are publicly available or from agency records. Using secondary data like this can make projects more feasible, but you may not find existing data that are useful for answering your working question. For that reason, many students gather their own raw data. As we discussed in the previous section, potential harms that come from addressing sensitive topics mean that surveys and interviews of practitioners or other less-vulnerable populations may be the most feasible and ethical way to approach data collection.

Using secondary data

Within the agency setting, there are two main sources of raw data. One option is to examine client charts. For example, if you wanted to know if substance use was related to parental reunification for youth in foster care, you could look at client files and compare how long it took for families with differing levels of substance use to be reunified. You will have to negotiate with the agency the degree to which your analysis can be public. Agencies may be okay with you using client files for a class project but less comfortable with you presenting your findings at a city council meeting. When analyzing data from your agency, you will have to manage a stakeholder relationship.

Another great example of agency-based raw data comes from program evaluations. If you are working with a grant funded agency, administrators and clinicians are likely producing data for grant reporting. The agency may consent to have you look at the raw data and run your own analysis. Larger agencies may also conduct internal research—for example, surveying employees or clients about new initiatives. These, too, can be good sources of available data. Generally, if the agency has already collected the data, you can ask to use them. Again, it is important to be clear on the boundaries and expectations of the agency. And don't be angry if they say no!

Some agencies, usually government agencies, publish their data in formal reports. You could take a look at some of the websites for county or state agencies to see if there are any publicly available data relevant to your research topic. As an example, perhaps there are annual reports from the state department of education that show how seclusion and restraint is disproportionately applied to Black children with disabilities , as students found in Virginia. In another example, one student matched public data from their city's map of criminal incidents with historically redlined neighborhoods. For this project, she is using publicly available data from Mapping Inequality , which digitized historical records of redlined housing communities and the Roanoke, VA crime mapping webpage . By matching historical data on housing redlining with current crime records, she is testing whether redlining still impacts crime to this day.

Not all public data are easily accessible, though. The student in the previous example was lucky that scholars had digitized the records of how Virginia cities were redlined by race. Sources of historical data are often located in physical archives, rather than digital archives. If your project uses historical data in an archive, it would require you to physically go to the archive in order to review the data. Unless you have a travel budget, you may be limited to the archival data in your local libraries and government offices. Similarly, government data may have to be requested from an agency, which can take time. If the data are particularly sensitive or if the department would have to dedicate a lot of time to your request, you may have to file a Freedom of Information Act request. This process can be time-consuming, and in some cases, it will add financial cost to your study.

Another source of secondary data is shared by researchers as part of the publication and review process. There is a growing trend in research to publicly share data so others can verify your results and attempt to replicate your study. In more recent articles, you may notice links to data provided by the researcher. Often, these have been de-identified by eliminating some information that could lead to violations of confidentiality. You can browse through the data repositories in Table 2.1 to find raw data to analyze. Make sure that you pick a data set with thorough and easy to understand documentation. You may also want to use Google's dataset search which indexes some of the websites below as well as others in a very intuitive and easy to use way.

Ultimately, you will have to weigh the strengths and limitations of using secondary data on your own. Engel and Schutt (2016, p. 327) [1] propose six questions to ask before using secondary data:

What were the agency’s or researcher’s goals in collecting the data?
What data were collected, and what were they intended to measure?
When was the information collected?
What methods were used for data collection? Who was responsible for data collection, and what were their qualifications? Are they available to answer questions about the data?
How is the information organized (by date, individual, family, event, etc.)? Are identifiers used to indicate different types of data available?
What is known about the success of the data collection effort? How are missing data indicated and treated? What kind of documentation is available? How consistent are the data with data available from other sources?

In this section, we've talked about data as though it is always collected by scientists and professionals. But that's definitely not the case! Think more broadly about sources of data that are already out there in the world. Perhaps you want to examine the different topics mentioned in the past 10 State of the Union addresses by the President. Or maybe you want to examine whether the websites and public information about local health and mental health agencies use gender-inclusive language. People share their experiences through blogs, social media posts, videos, performances, among countless other sources of data. When you think broadly about data, you'll be surprised how much you can answer with available data.

Collecting your own raw data

The primary benefit of collecting your own data is that it allows you to collect and analyze the specific data you are looking for, rather than relying on what other people have shared. You can make sure the right questions are asked to the right people. Your early research projects may be smaller in scope. This isn't necessarily a limitation. Early projects are often the first step in a long research trajectory in which the same topic is studied in increasing detail and sophistication over time.

Student researchers often propose to survey or interview practitioners. The focus of these projects should be about the practice of social work and the study will uncover how practitioners understand what they do. Surveys of practitioners often test whether responses to questions are related to each other. For example, you could propose to examine whether someone's length of time in practice was related to the type of therapy they use or their level of burnout. Interviews or focus groups can also illuminate areas of practice. One student proposed to conduct focus groups of individuals in different helping professions in order to understand how they viewed the process of leaving an abusive partner. She suspected that people from different disciplines would make unique assumptions about the survivor's choices.

It's worth remembering here that you need to have access to practitioners, as we discussed in the previous section. Resourceful researchers will look at publicly available databases of practitioners, draw from agency and personal contacts, or post in public forums like Facebook groups. Consent from gatekeepers is important, and as we described earlier, you and your agency may be interested in collaborating on a project. Bringing your agency on board as a stakeholder in your project may allow you access to company email lists or time at staff meetings as well as access to practitioners. One student partnered with her internship placement at a local hospital to measure the burnout that nurses experienced in their department. Her project helped the agency identify which departments may need additional support.

Another possible way you could collect data is by partnering with your agency on evaluating an existing program. Perhaps they want you to evaluate the early stage of a program to see if it's going as planned and if any changes need to be made. Maybe there is an aspect of the program they haven't measured but would like to, and you can fill that gap for them. Collaborating with agency partners in this way can be a challenge, as you must negotiate roles, get stakeholder buy-in, and manage the conflicting time schedules of field work and research work. At the same time, it allows you to make your work immediately relevant to your specific practice and client population.

In summary, many early projects fall into one of the following categories. These aren't your only options! But they may be helpful in thinking about what research projects can look like.

Analyzing charts or program evaluations at an agency
Analyzing existing data from an agency, government body, or other public source
Analyzing popular media or cultural artifacts
Surveying or interviewing practitioners, administrators, or other less-vulnerable groups
Conducting a program evaluation in collaboration with an agency
All research projects require analyzing raw data.
Research projects often analyze available data from agencies, government, or public sources. Doing so allows researchers to avoid the process of recruiting people to participate in their study. This makes projects more feasible but limits what you can study to the data that are already available to you.
Think through the potential harm of discussing sensitive topics when surveying or interviewing clients and other vulnerable populations. Since many social work topics are sensitive, researchers often collect data from less-vulnerable populations such as practitioners and administrators.

Post-awareness check (Environment)

In what environment are you most comfortable in data collection (phone calls, face to face recruitment, etc)? Consider your preferred method of data collection that may align with both your personality and your target population.

Describe the difference between raw data and the results of research articles.
Consider browsing around the data repositories in Table 2.1.
Identify a common type of project (e.g., surveys of practitioners) and how conducting a similar project might help you answer your working question.
What kind of raw data might you collect yourself for your study?

2.3 Creating a data analysis plan

Define and construct a data analysis plan.
Define key quantitative data management terms—variable name, data dictionary, primary and secondary data, observations/cases.
Differentiate between univariate and bivariate quantitative analysis.
Explain when we might use quantitative bivariate analysis in social work research.
Identify how your qualitative research question, research aim, and type of data may influence your choice of analytic methods.
Outline the steps you will take in preparation for conducting qualitative data analysis.

After you have your raw data , whether this is secondary data or data you collected yourself, you will need to analyze it. While the specific steps to follow in quantitative or qualitative data analysis are beyond the scope of this chapter, we are going to address some basic concepts in this section to help you create a data analysis plan. A data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. If you look back at Table 2.1, you will see that creating a data analysis plan is a part of the study design process. The data analysis plan flows from the research question, is integral to the study desig n, and should be well conceptualized prior to beginning data collection. In this section, we will walk through the basics of quantitative and qualitative data analysis to help you understand the fundamentals of creating a data analysis plan.

When considering what data you might want to collect as part of your project, there are two important considerations that can create dilemmas for researchers. You might only get one chance to interact with your participants, so you must think comprehensively in your planning phase about what information you need and collect as much relevant data as possible. At the same time, though, especially when collecting sensitive information, you need to consider how onerous the data collection is for participants and whether you really need them to share that information. Just because something is interesting to us doesn't mean it's related enough to our research question to chase it down. Work with your research team and/or faculty early in your project to talk through these issues before you get to this point. And if you're using secondary data, make sure you have access to all the information you need in that data before you use it.

Once you've collected your quantitative data, you need to make sure it is well- organized in a database in a way that's actually usable. "Database" can be kind of a scary word, but really, it can be as simple as an Excel spreadsheet or a data file in whatever program you're using to analyze your data. You may want to avoid Excel and use a formal database such as Microsoft Access or MySQL if you've got a large or complicated data set. But if your data set is smaller and you plan to keep your analyses simple, you can definitely get away with Excel. A typical data set is organized with variables as columns and observations/cases as rows. For example, let's say we did a survey on ice cream preferences and collected the following information in Table 2.3:

Variable name : Just what it sounds like—the name of your variable. Make sure this is something useful, short and, if you're using something other than Excel, all one word. Most statistical programs will automatically rename variables for you if they aren't one word, but the names can be a little ridiculous and long.
Observations/cases : The rows in your data set. In social work, these are often your study participants (people), but can be anything from census tracts to black bears to trains. When we talk about sample size, we're talking about the number of observations/cases. In our mini data set, each person is an observation/case.
Data dictionary (sometimes called a code book or metadata) : This is the document where you list your variable names, what the variables actually measure or represent, what each of the values of the variable mean if the meaning isn't obvious (i.e., if there are numbers assigned to gender), the level of measurement and anything special to know about the variables (for instance, the source if you mashed two data sets together). If you're using secondary data, the researchers sharing the data should make the data dictionary available .

Let's take that mini data set we've got up above and we'll show you what your data dictionary might look like in Table 2.4.

As part of planning for your research, you should come up with a data analysis plan. Remember, a data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. A basic data analysis plan might look something like what you see in Table 2.5. Don't panic if you don't yet understand some of the statistical terms in the plan; we're going to delve into some of them in this section, and others will be covered in more depth in your statistics courses. Note here also that this is what operationalizing your variables and moving through your research with them looks like on a basic level. We will cover operationalization in more depth in Chapter 11.

An important point to remember is that you should never get stuck on using a particular statistical method because you or one of your co-researchers thinks it's cool or it's the hot thing in your field right now. You should certainly go into your data analysis plan with ideas, but in the end, you need to let your research question guide what statistical tests you plan to use. Be prepared to be flexible if your plan doesn't pan out because the data is behaving in unexpected ways.

You'll notice that the first step in the quantitative data analysis plan is univariate and descriptive statistics. Univariate data analysis is a quantitative method in which a variable is examined individually to determine its distribution , or the way the scores are distributed across the levels, or values, of that variable. When we talk about levels , what we are talking about are the possible values of the variable—like a participant's age, income or gender. (Note that this is different from levels of measurement , which will be discussed in Chapter 11, but the level of measurement of your variables absolutely affects what kinds of analyses you can do with it.) Univariate analysis is n on-relational , which just means that we're not looking into how our variables relate to each other. Instead, we're looking at variables in isolation to try to understand them better. For this reason, univariate analysis is used for descriptive research questions.

So when do you use univariate data analysis? Always! It should be the first thing you do with your quantitative data, whether you are planning to move on to more sophisticated statistical analyses or are conducting a study to describe a new phenomenon. You need to understand what the values of each variable look like—what if one of your variables has a lot of missing data because participants didn't answer that question on your survey? What if there isn't much variation in the gender of your sample? These are things you'll learn through univariate analysis.

Did you know that ice cream causes shark attacks? It's true! When ice cream sales go up in the summer, so does the rate of shark attacks. So you'd better put down that ice cream cone, unless you want to make yourself look more delicious to a shark.

Ok, so it's quite obviously not true that ice cream causes shark attacks. But if you looked at these two variables and how they're related, you'd notice that during times of the year with high ice cream sales, there are also the most shark attacks. Despite the fact that the conclusion we drew about the relationship was wrong, it's nonetheless true that these two variables appear related, and researchers figured that out through the use of bivariate analysis. (You will learn about correlation versus causation in Chapter 8 .)

Bivariate analysis consists of a group of statistical techniques that examine the association between two variables. We could look at how anti-depressant medications and appetite are related, whether there is a relation between having a pet and emotional well-being, or if a policy-maker's level of education is related to how they vote on bills related to environmental issues.

Bivariate analysis forms the foundation of multivariate analysis, which we don't get to in this book. All you really need to know here is that there are steps beyond bivariate analysis, which you've undoubtedly seen in scholarly literature already! But before we can move forward with multivariate analysis, we need to understand the associations between the variables in our study .

[MADE THIS UP] Throughout your PhD program, you will learn more about quantitative data analysis techniques. Hopefully this section has provided you with some initial insights into how data is analyzed, and the importance of creating a data analysis plan prior to collecting data. Next, we will discuss some basic strategies for creating a qualitative data analysis plan.

If you don't see the general aim of your research question reflected in one of these areas, don't fret! This is only a small sampling of what you might be trying to accomplish with your qualitative study. Whatever your aim, you need to have a plan for what you will do once you have collected your data.

Iterative or linear

Some qualitative research is linear , meaning it follows more of a tra ditionally quantitative process: create a plan, gather data, and analyze data; each step is completed before we proceed to the next. You can think of this like how information is presented in this book. We discuss each topic, one after another.

With iterative research, we have more flexibility to adapt our approach as we learn new things. We still need to keep our approach systematic and organized, however, so that our work doesn't become a free-for-all. As we adapt, we do not want to stray too far from the original premise of our study. It's also important to remember with an iterative approach that we may risk ethical concerns if our work extends beyond the original boundaries of our informed consent and institutional review board agreement (IRB; see Chapter 6 for more on IRBs). If you feel that you do need to modify your original research plan in a significant way as you learn more about the topic, you can submit an addendum to modify your original application that was submitted. Make sure to keep detailed notes of the decisions that you are making and what is informing these choices. This helps to support transparency and your credibility throughout the research process.

As y ou begin your analysis, y ou need to get to know your data. This often means reading through your data prior to any attempt at breaking it apart and labeling it. You mig ht read through a couple of times, in fact. This helps give you a more comprehensive feel for each piece of data and the data as a whole, again, before you start to break it down into smaller units or deconstruct it. This is especially important if others assisted us in the data collection process. We often gather data as part of team and everyone involved in the analysis needs to be very familiar with all of the data.

During your reviewing you will start to develop and evolve your understanding of what the data means. Coding is a part of the qualitative data analysis process where we begin to interpret and assign meaning to the data. It represents one of the first steps as we begin to filter the data through our own subjective lens as the researcher. This understanding of the data should be dynamic and flexible, but you want to have a way to capture this understanding as it evolves. You may include this as part of your qualitative codebook where you are tracking the main ideas that are emerging and what they mean. Figure 2.2 is an example of how your thinking might change about a code and how you can go about capturing it.

Getting organized at the beginning of your project with a data analysis plan will help keep you on track. Data analysis plans should include your research question, a description of your data, and a step-by-step outline of what you're going to do with it. [chapter 14.1]

Exercises [from chapter 14.1]

Make a data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you're going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. You can do this exercise whether you're using quantitative or qualitative data! The same principles apply.
Make a draft quantitative data analysis plan for your project. Remember this should include your research question, a description of the data you will use, and a step-by-step outline of what you're going to do with your data once you have it, including statistical tests (non-relational and relational) that you plan to use. It's okay if you don't yet have a complete idea of the types of statistical analyses you might use.

2.4 Critical considerations

Critique the traditional role of researchers and identify how action research addresses these issues

So far in this chapter, we have presented the steps of research projects as follows:

Find a topic that is important to you and read about it.
Pose a question that is important to the literature and to your community.
Propose to use specific research methods and data analysis techniques to answer your question.
Carry out your project and report the results.

These were depicted in more detail in Table 2.1 earlier in this chapter. There are important limitations to this approach. This section examines those problems and how to address them.

Whose knowledge is privileged?

First, let's critically examine your role as the researcher. Following along with the steps in a research project, you start studying the literature on your topic, find a place where you can add to scientific knowledge, and conduct your study. But why are you the person who gets to decide what is important? Just as clients are the experts on their lives, members of your target population are the experts on their lives. What does it mean for a group of people to be researched on, rather than researched with? How can we better respect the knowledge and self-determination of community members?

A different way of approaching your research project is to start by talking with members of the target population and those who are knowledgeable about that community. Perhaps there is a community-led organization you can partner with on a research project. The researcher's role in this case would be more similar to a consultant, someone with specialized knowledge about research who can help communities study problems they consider to be important. The social worker is a co-investigator, and community members are equal partners in the research project. Each has a type of knowledge—scientific expertise vs. lived experience—that should inform the research process.

The community focus highlights something important: they are localized. These projects can dedicate themselves to issues at a single agency or within a service area. With a local scope, researchers can bring about change in their community. This is the purpose behind action research.

Action research

Action research is research that is conducted for the purpose of creating social change. When engaging in action research, scholars collaborate with community stakeholders to conduct research that will be relevant to the community. Social workers who engage in action research don't just go it alone; instead, they collaborate with the people who are affected by the research at each stage in the process. Stakeholders, particularly those with the least power, should be consulted on the purpose of the research project, research questions, design, and reporting of results.

Action research also distinguishes itself from other research in that its purpose is to create change on an individual and community level. Kristin Esterberg puts it quite eloquently when she says, “At heart, all action researchers are concerned that research not simply contribute to knowledge but also lead to positive changes in people’s lives” (2002, p. 137). [2] Action research has multiple origins across the globe, including Kurt Lewin’s psychological experiments in the US and Paulo Friere’s literacy and education programs (Adelman, 1993; Reason, 1994). [3] Over the years, action research has become increasingly popular among scholars who wish for their work to have tangible outcomes that benefit the groups they study.

A traditional scientist might look at the literature or use their practice wisdom to formulate a question for quantitative or qualitative research, as we suggested earlier in this chapter. An action researcher, on the other hand, would consult with people in the target population and community to see what they believe the most pressing issues are and what their proposed solutions may be. In this way, action research flips traditional research on its head. Scientists are not the experts on the research topic. Instead, they are more like consultants who provide the tools and resources necessary for a target population to achieve their goals and to address social problems using social science research.

According to Healy (2001), [4] the assumptions of participatory-action research are that (a) oppression is caused by macro-level structures such as patriarchy and capitalism; (b) research should expose and confront the powerful; (c) researcher and participant relationships should be equal, with equitable distribution of research tasks and roles; and (d) research should result in consciousness-raising and collective action. Consistent with social work values, action research supports the self-determination of oppressed groups and privileges their voice and understanding through the conceptualization, design, data collection, data analysis, and dissemination processes of research. We will return to similar ideas in Part 4 of the textbook when we discuss qualitative research methods, though action research can certainly be used with quantitative research methods, as well.

Traditionally, researchers did not consult target populations and communities prior to formulating a research question. Action research proposes a more community-engaged model in which researchers are consultants that help communities research topics of import to them.

Post- awareness check (Knowledge)

Based on what you know of your target population, what are a few ways to receive their “buy-in” to participate in your proposed research study?

Apply the key concepts of action research to your project. How might you incorporate the perspectives and expertise of community members in your project?

The level that describes how data for variables are recorded. The level of measurement defines the type of operations can be conducted with your data. There are four levels: nominal, ordinal, interval, and ratio.

Referring to data analysis that doesn't examine how variables relate to each other.

a group of statistical techniques that examines the relationship between two variables

A research process where you create a plan, you gather your data, you analyze your data and each step is completed before you proceed to the next.

An iterative approach means that after planning and once we begin collecting data, we begin analyzing as data as it is coming in. This early analysis of our (incomplete) data, then impacts our planning, ongoing data gathering and future analysis as it progresses.

Part of the qualitative data analysis process where we begin to interpret and assign meaning to the data.

A document that we use to keep track of and define the codes that we have identified (or are using) in our qualitative data analysis.

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Writing the Data Analysis Plan

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You and your project statistician have one major goal for your data analysis plan: You need to convince all the reviewers reading your proposal that you would know what to do with your data once your project is funded and your data are in hand. The data analytic plan is a signal to the reviewers about your ability to score, describe, and thoughtfully synthesize a large number of variables into appropriately-selected quantitative models once the data are collected. Reviewers respond very well to plans with a clear elucidation of the data analysis steps – in an appropriate order, with an appropriate level of detail and reference to relevant literatures, and with statistical models and methods for that map well into your proposed aims. A successful data analysis plan produces reviews that either include no comments about the data analysis plan or better yet, compliments it for being comprehensive and logical given your aims. This chapter offers practical advice about developing and writing a compelling, “bullet-proof” data analytic plan for your grant application.

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Panter, A.T. (2010). Writing the Data Analysis Plan. In: Pequegnat, W., Stover, E., Boyce, C. (eds) How to Write a Successful Research Grant Application. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1454-5_22

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Data analysis plan

Data analysis plan refers to a roadmap for how the data will be organized and analyzed and how results will be presented. A data analysis plan should be established when planning a research study (i.e., before data collection begins). Among other things, the data analysis plan should describe: (a) the data to be collected; (b) the analyses to be conducted to address the research objectives, including assumptions required by said analyses; (c) data cleaning and management procedures; (d) data transformations, if applicable; and (e) how the study results will be presented (e.g., graphs, tables).

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A Research Guide
Research Paper Guide

How to Write a Research Plan

Research plan definition
Purpose of a research plan
Research plan structure
Step-by-step writing guide

Tips for creating a research plan

Research plan examples

Research plan: definition and significance

What is the purpose of a research plan.

Bridging gaps in the existing knowledge related to their subject.
Reinforcing established research about their subject.
Introducing insights that contribute to subject understanding.

Research plan structure & template

Introduction.

What is the existing knowledge about the subject?
What gaps remain unanswered?
How will your research enrich understanding, practice, and policy?

Literature review

Expected results.

Express how your research can challenge established theories in your field.
Highlight how your work lays the groundwork for future research endeavors.
Emphasize how your work can potentially address real-world problems.

5 Steps to crafting an effective research plan

Step 1: define the project purpose, step 2: select the research method, step 3: manage the task and timeline, step 4: write a summary, step 5: plan the result presentation.

Brainstorm Collaboratively: Initiate a collective brainstorming session with peers or experts. Outline the essential questions that warrant exploration and answers within your research.
Prioritize and Feasibility: Evaluate the list of questions and prioritize those that are achievable and important. Focus on questions that can realistically be addressed.
Define Key Terminology: Define technical terms pertinent to your research, fostering a shared understanding. Ensure that terms like “church” or “unreached people group” are well-defined to prevent ambiguity.
Organize your approach: Once well-acquainted with your institution’s regulations, organize each aspect of your research by these guidelines. Allocate appropriate word counts for different sections and components of your research paper.

Research plan example

Writing a Research Paper
Research Paper Title
Research Paper Sources
Research Paper Problem Statement
Research Paper Thesis Statement
Hypothesis for a Research Paper
Research Question
Research Paper Outline
Research Paper Summary
Research Paper Prospectus
Research Paper Proposal
Research Paper Format
Research Paper Styles
AMA Style Research Paper
MLA Style Research Paper
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APA Style Research Paper
Research Paper Structure
Research Paper Cover Page
Research Paper Abstract
Research Paper Introduction
Research Paper Body Paragraph
Research Paper Literature Review
Research Paper Background
Research Paper Methods Section
Research Paper Results Section
Research Paper Discussion Section
Research Paper Conclusion
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Research Paper Types
What is Qualitative Research

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

Interview transcripts
Surveys with open-ended questions
Contact center transcripts
Texts and documents
Audio and video recordings
Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

Gathering and collecting your qualitative data
Organizing and connecting into your qualitative data
Coding your qualitative data
Analyzing the qualitative data for insights
Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues. It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope, helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered. Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis. Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them. It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations. The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

G2.com reviews of the product Airtable. You could pull reviews from G2 for your analysis.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.

The benefits of using computer-assisted qualitative data analysis software:

Assists in the organizing of your data
Opens you up to exploring different interpretations of your data analysis
Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

The user interface of CAQDAS software 'NVivo'

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:

Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Some of qualitative data integrations offered by Thematic

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably. For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
Keep repeating step 2, adding new codes and revising the code description as often as necessary. Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Example of a hierarchical coding frame in qualitative data analysis

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy. Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

Demographic
And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

Calculate overall NPS
Calculate NPS in the subset of responses that do not contain that theme
Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

Visualizing qualitative data: Calculating the impact of a code on your score

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs. This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Two examples of qualitative data visualizations within Thematic

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable. Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding. Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places. And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Content of a statistical analysis plan

Developing a statistical analysis plan (SAP) is a critical component of designing and conducting research studies which influences collection, analysis, and interpretation of data. A well written SAP prevents research waste, provides transparency, can be published on its own, allows replication of the study by independent researchers, can help when writing the manuscript.

Learning objectives:

Understand that the SAP promotes rigor and reproducibility.
Understand the contents of an SAP (administrative information, background, study level and variable level information, analysis methods, amendments).
Learn about structured reporting and estimands

This seminar repeats the topics from the workshop "Developing your own statistical analysis plan" and thus is intended for participants who had not taken the earlier workshop.

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Research Roundtable: ClinicalTrials.gov FAQ — Results Reporting (Part 2)

Research Roundtable: ClinicalTrials.gov FAQ — Results Reporting (Part 2)

Published May 13, 2024

Part two in a series of articles highlighting frequently asked questions to comply with results reporting for ClinicalTrials.gov includes guidance on the Quality Control review process, protocol and statistical analysis plans, and appendices. (See part one here .)

ClinicalTrials.gov has an extensive FAQ section ranging from general to investigation-specific. Many of these questions frequently arise not only at the University at Buffalo but also at many other institutions that are part of the Clinical Trials Registration and Results Reporting Taskforce . See additional ClinicalTrials.gov FAQs here .

How long does the Quality Control review process take?

After the Responsible Party releases (submits) information to ClinicalTrials.gov, the information undergoes a manual quality control review to identify possible errors, deficiencies, or inconsistencies that are not detected automatically during data entry. The Responsible Party will be notified of any issues that need correction, usually within a few days after release of the protocol information. Review of records with results is prioritized for the studies that appear to be Applicable Clinical Trials (ACT) or are NIH-funded and can take up to 30 days or longer. The review process for non-ACTs and studies that are not funded by NIH can be lengthier.

Is a protocol and statistical analysis plan (SAP) required to be submitted?

The regulations require a copy of the protocol and SAP (if not included in the protocol) to be submitted as part of clinical trial results information. The Responsible Party may redact names, addresses, and other personally identifiable information, as well as any trade secrets and/or confidential commercial information.

If there is more than one IRB-approved version of a consent form, how many must be posted?

The Revised Common Rule requires that the version of the consent document that is posted must a) be IRB-approved and b) have been used to enroll a participant in the clinical trial. The version of the consent that should be posted is the most recent IRB-approved version that was used to enroll a participant.

Are appendices required to be included in the uploaded study protocol?

ClinicalTrials.gov considers the protocol appendices that contain a “description of the clinical trial, including objective(s), design, and methods,” and any “relevant scientific background and statistical considerations,” to be part of the full protocol and as such they must be included with the uploaded protocol.

When posting a protocol with many add-on sub-studies, which version of the protocol should be posted?

Post the version of the protocol that was originally approved and is relevant to the study at hand. If sub-studies answer the same research questions, register under the originally approved protocol. If sub-studies are clearly different from the main study, each sub-study should be registered separately in ClinicalTrials.gov.

How can I get ClinicalTrials.gov help at UB?

For UB assistance with ClinicalTrials.gov registration and reporting requirements, contact the UB ClinicalTrials.gov team ( [email protected] ) or ClinicalTrials.gov Protocol Registration and Results System (PRS) Administrators Lynn Jagodzinski ( [email protected] ), CTSI Clinical Research Regulatory Administrator, and Urmo “Mo” Jaanimägi ( [email protected] ), CTSI Quality Assurance Specialist.

“Research Roundtable” is a section in the University at Buffalo Clinical and Translational Science Institute (CTSI) Translational Spotlight newsletter. Add your email to the newsletter mailing list here .

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COMMENTS

How to Create a Data Analysis Plan: A Detailed Guide
In this blog article, we will explore how to create a data analysis plan: the content and structure. This data analysis plan serves as a roadmap to how data collected will be organised and analysed. It includes the following aspects: Clearly states the research objectives and hypothesis. Identifies the dataset to be used.
Creating a Data Analysis Plan: What to Consider When Choosing
The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data. Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable.
PDF Creating an Analysis Plan
Analysis Plan and Manage Data. The main tasks are as follows: 1. Create an analysis plan • Identify research questions and/or hypotheses. • Select and access a dataset. • List inclusion/exclusion criteria. • Review the data to determine the variables to be used in the main analysis. • Select the appropriate statistical methods and ...
PDF Developing a Quantitative Data Analysis Plan
A Data Analysis Plan (DAP) is about putting thoughts into a plan of action. Research questions are often framed broadly and need to be clarified and funnelled down into testable hypotheses and action steps. The DAP provides an opportunity for input from collaborators and provides a platform for training. Having a clear plan of action is also ...
Design the analysis plan
Get Help. Designing an analysis plan ensures data collection methods meet the needs of the research question, and that the study is accurately powered to produce meaningful results. Based on investigator affiliations and the type of analysis, consultative services are available to discuss statistical methods, analysis software, or potential ...
PDF DATA ANALYSIS PLAN
analysis plan: example. • The primary endpoint is free testosterone level, measured at baseline and after the diet intervention (6 mo). • We expect the distribution of free T levels to be skewed and will log-transform the data for analysis. Values below the detectable limit for the assay will be imputed with one-half the limit.
Data Analysis Plan: Examples & Templates
A data analysis plan is a roadmap for how you're going to organize and analyze your survey data—and it should help you achieve three objectives that relate to the goal you set before you started your survey: Answer your top research questions. Use more specific survey questions to understand those answers. Segment survey respondents to ...
(PDF) Guide to the Statistical Analysis Plan
An analysis plan is a description of the steps of the analyses that will be used to understand study objectives (Yuan et al., 2019). The analysis plan is a part of the collaborative process ...
Writing the Data Analysis Plan
22.1 Writing the Data Analysis Plan. Congratulations! You have now arrived at one of the most creative and straightforward, sections of your grant proposal. You and your project statistician have one major goal for your data analysis plan: You need to convince all the reviewers reading your proposal that you would know what to do with your data ...
Data Analysis Plan: Examples & Templates
One example of this is Sentiment Analysis, which is a way of identifying the emotion behind people's comments. When this functionality is enabled in SurveyMonkey, your survey answers will be categorised as Positive, Neutral, Negative or Undetected. Meanwhile, grounded theory involves looking at the qualitative data to explain a pattern.
Guide to the statistical analysis plan
To improve reproducibility, transparency, and validity among clinical trials, the National Institute of Health recently updated its grant application requirements, which mandates registration of clinical trials and submission of the original statistical analysis plan (SAP) along with the research protocol. Many leading journals also require the ...
Statistical Analysis Plan: What is it & How to Develop it
The SAP (statistical analysis plan) will direct us from the beginning to the conclusion, help us summarize and describe the data, and test our hypotheses. The statistical analysis plan (SAP) describes the intended clinical trial analysis. The SAP is a technical document that describes the statistical methods of research analysis, as opposed to ...
How to Write a Research Proposal
Finalize sampling methods and data analysis methods; 13th February: 3. Data collection and preparation: Recruit participants and send out questionnaires; ... The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher ...
Data Analysis in Research: Types & Methods
Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. Three essential things occur during the data ...
2.3 Data management and analysis
A data analysis plan is an ordered outline that includes your research question, a description of the data you are going to use to answer it, and the exact step-by-step analyses that you plan to run to answer your research question. If you look back at Table 2.1, you will see that creating a data analysis plan is a part of the study design process.
The Beginner's Guide to Statistical Analysis
Step 1: Write your hypotheses and plan your research design. To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design. Writing statistical hypotheses. The goal of research is often to investigate a relationship between variables within a population. You start with a prediction ...
PDF PLANNING AND PREPARING THE ANALYSIS
It is driven by the analytic objectives and is a key part of the analysis plan. In its essence, it is a process of data selection by source (e.g., individual, group, observation event), domain of inquiry, and/or question asked. Here we illustrate this process with two examples from our research.
PDF Chapter 22 Writing the Data Analysis Plan
W. Pequegnat et al. (eds.), How to Write a Successful Research Grant Application: A Guide for Social and Behavioral Scientists, DOI 10.1007/978-1-4419-1454-5_22, ... and selecting your data analysis plan clarifies the variables that will be included versus others that may not be. Mapping your conceptual model to your analyses
Learning to Do Qualitative Data Analysis: A Starting Point
For many researchers unfamiliar with qualitative research, determining how to conduct qualitative analyses is often quite challenging. Part of this challenge is due to the seemingly limitless approaches that a qualitative researcher might leverage, as well as simply learning to think like a qualitative researcher when analyzing data. From framework analysis (Ritchie & Spencer, 1994) to content ...
Data analysis plan
A data analysis plan should be established when planning a research study (i.e., before data collection begins). Among other things, the data analysis plan should describe: (a) the data to be collected; (b) the analyses to be conducted to address the research objectives, including assumptions required by said analyses; (c) data cleaning and ...
How to Write a Research Plan
Step 4: Write a summary. Prepare a project summary that serves as your research project guide. This invaluable tool aids recruitment interviews, meetings, and field studies. With a well-structured summary, you can stay on track during interactions, ensuring you address key project aspects.
Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Qualitative Data Analysis: Step-by-Step Guide (Manual vs ...
Step 1: Gather your qualitative data and conduct research (Conduct qualitative research) The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.
Content of a statistical analysis plan
12:00 PM - 1:00 PM. Developing a statistical analysis plan (SAP) is a critical component of designing and conducting research studies which influences collection, analysis, and interpretation of data. A well written SAP prevents research waste, provides transparency, can be published on its own, allows replication of the study by independent ...
Research Roundtable: ClinicalTrials.gov FAQ
Part two in a series focused on frequently asked questions to comply with results reporting for ClinicalTrials.gov includes guidance on the Quality Control review process, protocol and statistical analysis plans, and appendices. Part one of a two-part series focused on frequently asked questions to ...
Resource Instructions: Collection Analysis Plan (CAP) Template
Analysis Plan, it is a good practice to coordinate with organizational leadership for additional feedback and to identify key areas of focus or priorities. Collection Priorities ... Research questions are used to guide the information collection to meet the objective.
PDF 2024-2028 NIH-Wide Strategic Plan for Research on the Health of Women
This strategic plan is driven by three guiding principles: Consider the complex intersection among multiple factors that afect the health of women. Include diverse populations of women in clinical research. Integrate perspectives from a diverse workforce of scientists with difering skills, knowledge, and experience.
Analysis: Taiwan's Domestic Communications Satellite Plan
Analysis: Taiwan's Domestic Communications Satellite Plan - TaiwanPlus News. Taiwan plans to build its own communication satellite program, which some have compared to SpaceX's Starlink system ...
How Much U.S. Aid Is Going to Ukraine?
It's important to note that of the $175 billion total, only $107 billion directly aids the government of Ukraine. Most of the remainder is funding various U.S. activities associated with the war ...
From barren to bountiful: India's plan for green hydrogen from biomass
From barren to bountiful: India's plan for green hydrogen from biomass. The meeting gathered representatives from key government ministries, knowledge partners, and research institutes to discuss the utilization of barren lands for biomass cultivation. New Delhi: Principal Scientific Adviser, Prof Ajay Kumar Sood chaired the inaugural meeting ...