example of data analysis plan in action research

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Data Analysis

Module 5: Data Analysis & Reciprocity

At this stage, you’re probably carrying out your planned intervention or action and gathering data to address your research question. Many newcomers to action research believe that analysis should only start after all the data has been collected.

An interim analysis is part of the continuous, ongoing data analysis. It is part of the ongoing reflective planning process of action research (Hendricks, 2013).

Your action research projects will typically involve both quantitative and qualitative data. The methods for simplifying quantitative data, such as  reporting, comparing , and  displaying data,  differ significantly from those used for qualitative data, which include  analyzing the data to identify patterns and themes.

New researchers often feel disappointed when their interventions don’t lead to the anticipated results. However, even in these situations, exploring the data to understand why things didn’t work as expected can provide valuable insights. This process can guide you in refining your intervention to achieve better results in the future.

Remember! Action research is an iterative process so what you learn from this cycle of your research project will inform your next iteration of action research.

Analysis of Quantitative Data: Reporting & Comparing

Quantitative data is usually gathered via:

• Test scores
• Rubric-scored work
• Tally sheets
• Behavioural scales
• Attitude scales
• Closed-ended survey items

For example:  Counting or averaging the number of responses for each item.

• Closed-ended responses (strong, average, weak) can reflect counts for the number of respondents who chose each response.
• For the behavioural scale item, which includes numerical responses, the actual number chosen for each item could be tallied and the numbers could be averaged to describe results (Hendricks, 2013).

Quick Tips to Analyze Quantitative Data

“According to Shank (ibid) “themes do not emerge from data. What emerges after much hard work and creative thought, is an awareness in the mind of the researcher that there are patterns of order that seem to cut across various aspects of the data. When these patterns become organized, and when they characterize different segments of data, then we can call them ‘themes’.”

(Hendricks 2013)

Analysis of Qualitative Data: Looking for Themes & Patterns

Analysis of qualitative data is a process of making meaning from data sources that can be interpreted in several ways and helps answer the  why questions .

These data sources can be explained and used to answer your research question only after they have been interpreted. This process requires a deeper analysis of data than those processes used to explain quantitative data sources (Hendricks 2013).

Verification

Verification is knowing when you “got it right.” Reaching valid conclusions in your study is a critical step in the action research cycle. Conclusions must be reasonable in light of the results obtained.

Quick Tips to Analyze Qualitative Data

Action Research Handbook Copyright © by Dr. Zabedia Nazim and Dr. Sowmya Venkat-Kishore is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Action Research Tutorials-CCAR

Action research tutorials, tutorial 8:  analyzing data - evidence, tutorial 8: analyzing date - evidence.

Action researchers use their practice as a site for systematic inquiry, by progressively transforming problems into questions, using the questions to shape action, collecting data, analyzing that data, and then reflecting on the results to create theories that frame new questions and actions (Coghlan and Brannick, 2005). Action researchers seek to understand how their actions are viewed from multiple perspectives. This knowledge is gained through the analysis of exchanges, interactions, and information. Through alignment of personal reflection with external evidence, the action researcher is able to make new predictions about actions that will lead to the desired state. In this tutorial, we will focus on sense-making strategies to use on the data that was collected in the previous cycle.

Tutorial 8 Video: From Data to Evidence

Consider the data that you collected, now is the time to analyze it. Your goal is to review, find relationships, condense, and display. This does not mean putting the collected data in the paper, it means making sense of the data and sharing the overview with your readers. You do the hard work of making sense of the data so that others can benefit. But you have to do it in a way that is reliable... that is in a way that if the reader invested the time and energy to follow your steps, they would come up with a similar summary.  It is best to keep very careful notes of exactly what you did as you will need to describe that steps. It should be detailed enough that you or someone else can repeat what you did. (Thinking of showing your work in math class).  This tutorial will suggest some possible steps or ideas to help you craft your process. 

Tutorial 8:  Data Analysis Activities

A. Explore - Organize Your Data Into Your Storyline B. Analyze - Examine your Data to Find your Story C. Visualizing - Display your Data to Tell Your Story

D. Writing - Action Research Report: Cycle 1 

E. Forum Discussion - Sharing your Results

Tutorial 8:  Resources

A. Understanding your Data

B. Organizing Your Data -- What is your storyline?

C. Exploring your Data -- What is your story?

D. Displaying your Data --How will you tell your story?

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5 Collecting Data in Your Classroom

ESSENTIAL QUESTIONS

• What sort of methodological considerations are necessary to collect data in your educational context?
• What methods of data collection will be most effective for your study?
• What are the affordances and limitations associated with your data collection methods?
• What does it mean to triangulate data, and why is it necessary?

As you develop an action plan for your action research project, you will be thinking about the primary task of conducting research, and probably contemplating the data you will collect. It is likely you have asked yourself questions related to the methods you will be using, how you will organize the data collection, and how each piece of data is related within the larger project. This chapter will help you think through these questions.

Data Collection

The data collection methods used in educational research have originated from a variety of disciplines (anthropology, history, psychology, sociology), which has resulted in a variety of research frameworks to draw upon. As discussed in the previous chapter, the challenge for educator-researchers is to develop a research plan and related activities that are focused and manageable to study. While human beings like structure and definitions, especially when we encounter new experiences, educators-as-researchers frequently disregard the accepted frameworks related to research and rely on their own subjective knowledge from their own pedagogical experiences when taking on the role of educator-researcher in educational settings. Relying on subjective knowledge enables teachers to engage more effectively as researchers in their educational context. Educator-researchers especially rely on this subjective knowledge in educational contexts to modify their data collection methodologies. Subjective knowledge negotiates the traditional research frameworks with the data collection possibilities of their practice, while also considering their unique educational context. This empowers educators as researchers, utilizing action research, to be powerful agents for change in educational contexts.

Thinking about Types of Data

Whether the research design is qualitative, quantitative or mixed-methods, it will determine the methods or ways you use to collect data. Qualitative research designs focus on collecting data that is relational, interpretive, subjective, and inductive; whereas a typical quantitative study, collects data that are deductive, statistical, and objective.

In contrast, qualitative data is often in the form of language, while quantitative data typically involves numbers. Quantitative researchers require large numbers of participants for validity, while qualitative researchers use a smaller number of participants, and can even use one (Hatch, 2002). In the past, quantitative and qualitative educational researchers rarely interacted, sometimes holding contempt for each other’s work; and even published articles in separate journals based on having distinct theoretical orientations in terms of data collection. Overall, there is a greater appreciation for both quantitative and qualitative approaches, with scholars finding distinct value in each approach, yet in many circles the debate continues over which approach is more beneficial for educational research and in educational contexts.

The goal of qualitative data collection is to build a complex and nuanced description of social or human problems from multiple perspectives. The flexibility and ability to use a variety of data collection techniques encompasses a distinct stance on research. Qualitative researchers are able to capture conversations and everyday language, as well as situational attitudes and beliefs. Qualitative data collection is able to be fitted to the study, with the goal of collecting the most authentic data, not necessarily the most objective. To researchers who strictly use quantitative methods, qualitative methods may seem wholly unstructured, eclectic, and idiosyncratic; however, for qualitative researchers these characteristics are advantageous to their purpose. Quantitative research depends upon structure and is bounded to find relationship among variables and units of measurement. Quantitative research helps make sense of large amounts of data. Both quantitative and qualitative research help us address education challenges by better identifying what is happening, with the goal of identifying why it is happening, and how we can address it.

Most educator-researchers who engage in research projects in schools and classrooms utilize qualitative methodologies for their data collection. Educator-researchers also use mixed methods that focus on qualitative methods, but also use quantitative methods, such as surveys, to provide a multidimensional approach to inquiring about their topic. While qualitative methods may feel more comfortable, there is a methodological rationale for using quantitative research.

Research methodologists use two distinct forms of logic to describe research: induction and deduction. Inductive approaches are focused on developing new or emerging theories, by explaining the accumulation of evidence that provides meaning to similar circumstances. Deductive approaches move in the opposite direction, and create meaning about a particular situation by reasoning from a general idea or theory about the particular circumstances. While qualitative approaches are inductive – observe and then generate theories, for example – qualitative researchers will typically initiate studies with some preconceived notions of potential theories to support their work.

Flexible Research Design

A researcher’s decisions about data collection and activities involve a personal choice, yet the choice of data sources must be responsive to the proposed project and topic. Logically, researchers will use whatever validated methods help them to address the issue they are researching and will develop a research plan around activities to implement those methods. While a research plan is important to conducting valid research in schools and classrooms, a research plan should also be flexible in design to allow data to emerge and find the best data to address research questions. In this way, a research plan is recommended, but data collection methods are not always known in advance. As you, the educator-researcher, interacts with participants, you may find it necessary to continue the research with additional data sources to better address the question at the center of your research. When educators are researchers and a participant in their study, it is especially important to keep an open mind to the wide range of research methodologies. All-in-all educator-researchers should understand that there are varied and multiple paths to move from research questions to addressing those questions.

Mixed Methods

As mentioned above, mixed methods is the use of both qualitative and quantitative methods. Researchers generally use mixed methods to clarify findings from the initial method of data collection. In mixed-methods research, the educator-researcher has increased flexibility in data collection. Mixed methods studies often result in a combination of precise measurements (e.g., grades, test scores, survey, etc.) along with in-depth qualitative data that provide meaningful detail to those measurements. The key advantage of using mixed methods is that quantitative details enhance qualitative data sources that involve conclusions and use terms such as usually, some, or most which can be substituted with a number or quantity, such as percentages or averages, or the mean, the median, and/or the mode. One challenge to educator-researchers is that mixed methods require more time and resources to complete the study, and more familiarity about both qualitative and quantitative data collection methods.

Mixed methods in educator research, even if quantitative methods are only used minimally, provide an opportunity to clarify findings, fill gaps in understanding, and cross-check data. For example, if you are looking at the use of math journals to better engage students and improve their math scores, it would be helpful to understand their abilities in math and reading before analyzing the math journals. Therefore, looking at their test scores might give you some nuanced understanding of why some students improved more than others after using the math journals. Pre- and post-surveys would also provide valuable information in terms of students’ attitudes and beliefs about math and writing. In line with thinking about pre- and post-surveys, some researchers suggest using either qualitative or quantitative approaches in different phases of the research process. In the previous example, pre- and post test scores may quantitatively demonstrate growth or improvement after implementing the math journal; however, the qualitative data would provide detailed evidence as to why the math journals contributed to growth or improvement in math. Quantitative methods can establish relationships among variables, while qualitative methods can explain factors underlying those same relationships.

I caution the reader at this point to not simply think of qualitative methodologies as anecdotal details to quantitative reports. I only highlight mixed methods to introduce the strength of such studies, and to aid in moving educational research methodology away from the binary thinking of quantitative vs. qualitative. In thinking about data collection, possible data sources include questionnaires or surveys, observations (video or written notes), collaboration (meetings, peer coaching), interviews, tests and records, pictures, diaries, transcripts of video and audio recordings, personal journals, student work samples, e-mail and online communication, and any other pertinent documents and reports. As you begin to think about data collection you will consider the available materials and think about aspects discussed in the previous chapter: who, what, where, when, and how. Specifically:

• Who are the subjects or participants for the study?
• What data is vital evidence for this study?
• Where will the data be collected?
• When will the data be collected?
• How will the data be collected?

If you find you are having trouble identifying data sources that support your initial question, you may need to revise your research question – and make sure what you are asking is researchable or measurable. The research question can always change throughout the study, but it should only be in relation the data being collected.

Participant Data

As an educator, your possible participants selection pool is narrower than most researchers encounter – however, it is important to be clear about their role in the data design and collection. A study can involve one participant or multiple participants, and participants often serve as the primary source of data in the research process. Most studies by educator-researchers utilize purposeful sampling, or in other words, they select participants who will be able to provide the most relevant information to the study. Therefore, the study design relies upon the participants and the information they can provide. The following is a description of some data collection methods, which include: surveys or questionnaires, individual or group interviews, observations, field notes or diaries, narratives, documents, and elicitation.

Surveys, or questionnaires, are a research instrument frequently used to receive data about participants’ feelings, beliefs, and attitudes in regard to the research topic or activities. Surveys are often used for large sample sizes with the intent of generalizing from a sample population to a larger population. Surveys are used with any number of participants and can be administered at different times during the study, such as pre-activity and post-activity, with the same participants to determine if changes have occurred over the course of the activity time, or simply change over time. Researchers like surveys and questionnaires as an instrument because they can be distributed and collected easily – especially with all of the recent online application possibilities (e.g., Google, Facebook, etc.). Surveys come in several forms, closed-ended, open-ended, or a mix of the two. Closed-ended surveys are typically multiple-choice questions or scales (e.g. 1-5, most likely–least likely) that allow participants to rate or select a response for each question. These responses can easily be tabulated into meaningful number representations, like percentages. For example, Likert scales are often used with a five-point range, with options such as strongly agree, agree, neutral, disagree, and strongly disagree. Open-ended surveys consist of prompts for participants to add their own perspectives in short answer or limited word responses. Open-ended surveys are not always as easy to tabulate, but can provide more detail and description.

Interviews and Focus Groups

Interviews are frequently used by researchers because they often produce some of the most worthwhile data. Interviews allow researchers to obtain candid verbal perspectives through structured or semi-structured questioning. Interview questions, either structured or semi-structured, are related to the research question or research activities to gauge the participants’ thoughts, feelings, motivations, and reflections. Some research relies on interviewing as the primary data source, but most often interviews are used to strengthen and support other data sources. Interviews can be time consuming, but interviews are worthwhile in that you can gather richer and more revealing information than other methods that could be utilized (Koshy, 2010). Lincoln and Guba (1985) identified five outcomes of interviewing:

Outcomes of Interviewing

• Here and now explanations;
• Reconstructions of past events and experiences;
• Projections of anticipated experiences;
• Verification of information from other sources;
• Verification of information (p. 268).

As mentioned above, interviews typically take two forms: structured and semi-structured. In terms of interviews, structured means that the researcher identifies a certain number of questions, in a prescribed sequence, and the researcher asks each participant these questions in the same order. Structured interviews qualitatively resemble surveys and questionnaires because they are consistent, easy to administer, provide direct responses, and make tabulation and analysis more consistent. Structured interviews use an interview protocol to organize questions, and maintain consistency.

Semi-structured interviews have a prescribed set of questions and protocol, just like structured interviews, but the researcher does not have to follow those questions or order explicitly. The researcher should ask the same questions to each participant for comparison reasons, but semi-structured interviews allow the researcher to ask follow-up questions that stray from the protocol. The semi-structured interview is intended to allow for new, emerging topics to be obtained from participants. Semi-structured questions can be included in more structured protocols, which allows for the participant to add additional information beyond the formal questions and for the researcher to return to preplanned formal questions after the participant responds. Participants can be interviewed individually or collectively, and while individual interviews are time-consuming, they can provide more in-depth information.

When considering more than two participants for an interview, researchers will often use a focus group interview format. Focus group interviews typically involve three to ten participants and seek to gain socially dependent perspectives or organizational viewpoints. When using focus group interviews with students, researchers often find them beneficial because they allow student reflection and ideas to build off of each other. This is important because often times students feel shy or hesitant to share their ideas with adults, but once another student sparks or confirms their idea, belief, or opinion they are more willing to share. Focus group interviews are very effective as pre- and post-activity data sources. Researchers can use either a structured or semi-structured interview protocol for focus group interviews; however, with multiple participants it may be difficult to maintain the integrity of a structured protocol.

Observations

One of the simplest, and most natural, forms of data collection is to engage in formal observation. Observing humans in a setting provides us contextual understanding of the complexity of human behavior and interrelationships among groups in that setting. If a researcher wants to examine the ways teachers approach a particular area of pedagogical practice, then observation would be a viable data collection tool. Formal observations are truly unique and allow the researcher to collect data that cannot be obtained through other data sources. Ethnography is a qualitative research design that provides a descriptive account based on researchers’ observations and explorations to examine the social dynamics present in cultures and social systems – which includes classrooms and schools. Taken from anthropology, the ethnographer uses observations and detailed note taking, along with other forms of mapping or making sense of the context and relationships within. For Creswell (2007), several guidelines provide structure to an observation:

Structuring Observations

• Identify what to observe
• Determine the role you will assume — observer or participant
• Design observational protocol for recording notes
• Record information such as physical situation, particular events and activities
• Thank participants and inform them of the use of and their accessibility to the data (pp. 132– 134)

As an educator-researcher, you may take on a role that exceeds that of an observer and participate as a member of the research setting. In this case, the data sources would be called participant observation to clearly identify the degree of involvement you have in the study. In participant observation, the researcher embeds themselves in the actions of the participants. It is important to understand that participant observation will provide completely different data, in comparison to simply observing someone else. Ethnographies, or studies focused completely on observation as a data source, often extend longer than other data sources, ranging from several months to even years. Extended time provides the researcher the ability to obtain more detailed and accurate information, because it takes time to observe patterns and other details that are significant to the study. Self-study is another consideration for educators, if they want to use observation and be a participant observer. They can use video and audio recordings of their activities to use as data sources and use those as the source of observation.

Field Diaries and Notes

Utilizing a field dairy, or keeping field notes, can be a very effective and practical data collection method. In purpose, a field diary or notes keep a record of what happens during the research activities. It can be useful in tracking how and why your ideas and the research process evolved. Many educators keep daily notes about their classes, and in many ways, this is a more focused and narrower version of documenting the daily happenings of a class. A field diary or notes can also serve as an account of your reflections and commentary on your study, and can be a starting place for your data analysis and interpretations. A field diary or notes are typically valuable when researchers begin to write about their project because it allows them to draw upon their authentic voice. The reflective process that represents a diary can also serve as an additional layer of professional learning for researchers. The format and length of a field diary or notes will vary depending on the researching and the topic; however, the ultimate goal should be to facilitate data collection and analysis.

Data narratives and stories are a fairly new form of formalized data. While researchers have collected bits and pieces of narratives in other forms of data, asking participants to compose a narrative (either written, spoken, or performed) as a whole allows researchers to examine how participants embrace the complexities of the context and social interactions. Humans are programmed to engage with and share narratives to develop meaningful and experiential knowledge. Educator autobiographies bring to life personal stories shaped by knowledge, values, and feelings that developed from their classroom experiences. Narrative data includes three primary areas: temporality, sociality, and place (Clandinin & Conolley, 2000). In terms of temporality, narratives have a past, present, and future because stories are time-based and transitional. Sociality highlights the social relationships in narratives as well as the personal and moral dispositions. Place includes the spaces where the narratives happen. Furthermore, bell hooks (1991) notes that narratives, or storytelling, as inquiry can be a powerful way to study how contexts are influenced by power structures, often linking and intersecting the structural dynamics of social class, race, and gender to highlight the struggle.

Documents provide a way to collect data that is unobtrusive to the participant. Documents are unobtrusive data because it is collected without modifying or distracting the research context when gathered. Educational settings maintain records on all sorts of activities in schools: content standards, state mandates, student discipline records, student attendance, student assessments, performance records, parental engagement, records of how teachers spend PTO money, etc. Documents often provide background and contextual material providing a snapshot of school policies, demographic information, ongoing records over a period of time, and contextual details from the site of the research study. Documents can be characterized similarly to historical research, as primary and secondary. Examples of primary materials are first-hand sources from someone in the educational context, such as minutes from a school board or faculty meeting, photographs, video recordings, and letters. Examples of secondary sources typically include analysis or interpretations of a primary source by others, such as texts, critiques, and reviews. Both types of sources are especially valuable in action research.

Elicitation Methods

We have talked about several methods of data collection that each have useful ways of documenting, inquiring, and thinking about the research question. However, how does a researcher engage participants in ways that allow them to demonstrate what they know, feel, think, or believe? Asking participants directly about their thinking, feeling, or beliefs will only take you so far depending on the comfort and rapport the participant has with the researcher. There are always a variety of hurdles in extracting participants’ knowledge. Even the manner in which questions are framed and the way researchers use materials in the research process are equally important in getting participants to provide reliable, comparable, and valid responses. Furthermore, all individuals who participate in research studies vary in their ability to recall and report what they know, and this affects the value of traditional data collection, especially structured and semi-structured interviewing. In particular, participants’ knowledge or other thinking of interest may be implicit and difficult for them to explicate in simple discussion.

Elicitation methods help researchers uncover unarticulated participant knowledge through a potential variety of activities. Researchers will employ elicitation methods and document the participants’ actions and typically the description of why they took those particular actions. Educators may be able to relate the process of elicitation methods to a “think aloud” activity in which the researcher wants to record or document the activity. Elicitation methods can take many forms. What follows are some basic ideas and formats for elicitation methods.

Brainstorming/Concept Map

Most educators are probably familiar with the process of brainstorming or creating a concept map. These can be very effective elicitation methods when the researcher asks the participant to create a concept map or representation of brainstorming, and then asks the participant to explain the connections between concepts or ideas on the brainstorming or concept map.

Sorting provides an engaging way to gather data from your participants. Sorting, as you can imagine, involves participants sorting, grouping, or categorizing objects or photographs in meaningful ways. Once participants have sorted the objects or photographs, the researcher records or documents the participant explaining why they sorted or grouped the objects or photographs in the way that they did. As a former history teacher, I would often use sorting to assess my students’ understanding of related concepts and events in a world history class. I would use pictures too as the means for students to sort and demonstrate what they understood from the unit. For broader discussion of elicitation techniques in history education see Barton (2015).

Listing/ Ranking

Listing can be an effective way to examine participants’ thinking about a topic. Researchers can have participants construct a list in many different ways to fit the focus of the study and then have the participants explain their list. For example, if an educator was studying middle school student perceptions of careers, they could ask them to complete three lists: Careers in Most Demand; Careers with Most Education/Training; Careers of most Interest.

Then, once participants have filled out the lists, the most important part is documenting them explaining their thinking, and why they filled out the lists the way they did. As you may imagine, in this example, every participant would have a list that is different based on their personal interests.

Researchers can also elicit responses by simply giving participants a prompt, and then asking them to recall whatever they know about that prompt. Researchers will have the participants do this in some sort of demonstrative activity. For example, at the end of a world history course, I might ask students to explain what “culture” means to them and to explain their thinking.

Re-articulation (writing or drawing)

A unique way to engage participants in elicitation methods is to have them write about, rewrite, or draw visual representations of either life experiences or literature that they have read. For example, you could ask them to rewrite a part of the literature they did not like, add a part they thought should be there, or simply extend the ending. Participants can either write or draw these re-articulations. I find that drawing works just as well because, again, the goal is to have participant describe their thinking based on the activity.  

Scenario Decision-Making

Elicitation methods can also examine skills. Researchers can provide participants scenarios and ask them to make decisions. The researchers can document those decisions and analyze the extent to which the participant understands the skill.

  Document, Photograph, or Video Analysis

This is the most basic elicitation in which the researcher provides a document, photograph, or video for the participant to examine. Then, the researcher asks questions about the participants interpretations of the document, photograph, or video. One method that would support this sort of elicitation is to ask the participants to provide images from their everyday words. For example, asking students to document the literacy examples in their homes (i.e., pictures of calendars, bookshelves etc.).  With the availability of one-to-one tech, and iPads, participant documentation is easier.

There are many more methods of data collection also, as well as many variations of the methods described above. The goal for you is to find the data collection methods that are going to give you the best data to answer your research question. If you are unsure, there is nothing wrong with collecting more data than you need to make sure you use effective methods – the only thing you have to lose is time!

Use of Case Studies

Case studies are a popular way for studying phenomena in settings using qualitative methodology. Case studies typically encompass qualitative studies which look closely at what happens when researchers collect data, analyze the data, and present the results. Case studies can focus on a single case or examine a phenomenon across multiple cases. Case studies frame research in a way that allows for rich description of data and depth of analysis.

An advantage of using case study design is that the reader often identifies with the case or phenomena, as well as the participants in the study. Yin (2003) describes case study methodology as inquiry that investigates a contemporary phenomenon within its authentic context. Case studies are particularly appropriate when the boundaries and relationship between the phenomenon and the context are not clear. Case studies relate well with the processes involved in action research. Critics of action research case studies sometimes criticize the inevitable subjectivity, just like general criticisms of action research. Case studies provide researchers opportunities to explore both the how and the why of phenomena in context, while being both exploratory and descriptive.

We want to clarify the differences between methodologies and methods of research. There are methodologies of research, like case study and action research, and methods of data collection. Methodologies like ethnography, narrative inquiry, and case study draw from some similar methods of data collecting that include interviews, collection of artifacts (writings, drawings, images), and observations. The differences between the methodologies include the time-frame for research; the boundaries of the research; and the epistemology.

Triangulation of Data

Triangulation is a method used by qualitative researchers to check and establish trustworthiness in their studies by using and analyzing multiple (three or more) data collection methods to address a research question and develop a consistency of evidence from data sources or approaches. Thus, triangulation facilitates trustworthiness of data through cross verification of evidence, to support claims, from more than two data collection sources. Triangulation also tests the consistency of findings obtained through different data sources and instruments, while minimizing bias in the researcher’s interpretations of the data.

If we think about the example of studying the use of math journals in an elementary classroom, the researcher would want to collect at least three sources of data – the journal prompts, assessment scores, and interviews. When the researcher is analyzing the data, they will want to find themes or evidence across all three data sources to address their research question. In a very basic analysis, if the students demonstrated a deeper level of reflection about math in the journals, their assessment scores improved, and their interviews demonstrated they had more confidence in their number sense and math abilities – then, the researcher could conclude, on a very general level, that math journals improved their students’ math skills, confidence, or abilities. Ideally, the study would examine specific aspects of math to enable deeper analysis of math journals, but this example demonstrates the basic idea of triangulation. In this example, all of the data provided evidence that the intervention of a math journal improved students’ understanding of math, and the three data sources provided trustworthiness for this claim.

Data Collection Checklist

• Based on your research question, what data might you need ?
• What are the multiple ways you could collect that data ?
• How might you document this data , or organize it so that it can be analyzed?
• What methods are most appropriate for your context and timeframe ?
• How much time will your data collection require? How much time can you allow for?
• Will you need to create any data sources (e.g., interview protocol, elicitation materials)?
• Do your data sources all logically support the research question, and each other?
• Does your data collection provide for multiple perspectives ?
• How will your data achieve triangulation in addressing the research question?
• Will you need more than three data sources to ensure triangulation of data?

Action Research Copyright © by J. Spencer Clark; Suzanne Porath; Julie Thiele; and Morgan Jobe is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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• Section 2: Home
• Developing the Quantitative Research Design
• Qualitative Descriptive Design
• Design and Development Research (DDR) For Instructional Design
• Qualitative Narrative Inquiry Research
• Action Research Resource

What is Action Research?

Considerations, creating a plan of action.

• Case Study Design in an Applied Doctorate
• SAGE Research Methods
• Research Examples (SAGE) This link opens in a new window
• Dataset Examples (SAGE) This link opens in a new window
• IRB Resource Center This link opens in a new window

Action research is a qualitative method that focuses on solving problems in social systems, such as schools and other organizations. The emphasis is on solving the presenting problem by generating knowledge and taking action within the social system in which the problem is located. The goal is to generate shared knowledge of how to address the problem by bridging the theory-practice gap (Bourner & Brook, 2019). A general definition of action research is the following: “Action research brings together action and reflection, as well as theory and practice, in participation with others, in the pursuit of practical solutions to issues of pressing concern” (Bradbury, 2015, p. 1). Johnson (2019) defines action research in the field of education as “the process of studying a school, classroom, or teacher-learning situation with the purpose of understanding and improving the quality of actions or instruction” (p.255).

Origins of Action Research

Kurt Lewin is typically credited with being the primary developer of Action Research in the 1940s. Lewin stated that action research can “transform…unrelated individuals, frequently opposed in their outlook and their interests, into cooperative teams, not on the basis of sweetness but on the basis of readiness to face difficulties realistically, to apply honest fact-finding, and to work together to overcome them” (1946, p.211).

Sample Action Research Topics

Some sample action research topics might be the following:

• Examining how classroom teachers perceive and implement new strategies in the classroom--How is the strategy being used? How do students respond to the strategy? How does the strategy inform and change classroom practices? Does the new skill improve test scores? Do classroom teachers perceive the strategy as effective for student learning?
• Examining how students are learning a particular content or objectives--What seems to be effective in enhancing student learning? What skills need to be reinforced? How do students respond to the new content? What is the ability of students to understand the new content?
• Examining how education stakeholders (administrator, parents, teachers, students, etc.) make decisions as members of the school’s improvement team--How are different stakeholders encouraged to participate? How is power distributed? How is equity demonstrated? How is each voice valued? How are priorities and initiatives determined? How does the team evaluate its processes to determine effectiveness?
• Examining the actions that school staff take to create an inclusive and welcoming school climate--Who makes and implements the actions taken to create the school climate? Do members of the school community (teachers, staff, students) view the school climate as inclusive? Do members of the school community feel welcome in the school? How are members of the school community encouraged to become involved in school activities? What actions can school staff take to help others feel a part of the school community?
• Examining the perceptions of teachers with regard to the learning strategies that are more effective with special populations, such as special education students, English Language Learners, etc.—What strategies are perceived to be more effective? How do teachers plan instructionally for unique learners such as special education students or English Language Learners? How do teachers deal with the challenges presented by unique learners such as special education students or English Language Learners? What supports do teachers need (e.g., professional development, training, coaching) to more effectively deliver instruction to unique learners such as special education students or English Language Learners?

Remember—The goal of action research is to find out how individuals perceive and act in a situation so the researcher can develop a plan of action to improve the educational organization. While these topics listed here can be explored using other research designs, action research is the design to use if the outcome is to develop a plan of action for addressing and improving upon a situation in the educational organization.

Considerations for Determining Whether to Use Action Research in an Applied Dissertation

• When considering action research, first determine the problem and the change that needs to occur as a result of addressing the problem (i.e., research problem and research purpose). Remember, the goal of action research is to change how individuals address a particular problem or situation in a way that results in improved practices.
• If the study will be conducted at a school site or educational organization, you may need site permission. Determine whether site permission will be given to conduct the study.
• Consider the individuals who will be part of the data collection (e.g., teachers, administrators, parents, other school staff, etc.). Will there be a representative sample willing to participate in the research?
• If students will be part of the study, does parent consent and student assent need to be obtained?
• As you develop your data collection plan, also consider the timeline for data collection. Is it feasible? For example, if you will be collecting data in a school, consider winter and summer breaks, school events, testing schedules, etc.
• As you develop your data collection plan, consult with your dissertation chair, Subject Matter Expert, NU Academic Success Center, and the NU IRB for resources and guidance.
• Action research is not an experimental design, so you are not trying to accept or reject a hypothesis. There are no independent or dependent variables. It is not generalizable to a larger setting. The goal is to understand what is occurring in the educational setting so that a plan of action can be developed for improved practices.

Considerations for Action Research

Below are some things to consider when developing your applied dissertation proposal using Action Research (adapted from Johnson, 2019):

• Research Topic and Research Problem -- Decide the topic to be studied and then identify the problem by defining the issue in the learning environment. Use references from current peer-reviewed literature for support.
• Purpose of the Study —What need to be different or improved as a result of the study?
• Research Questions —The questions developed should focus on “how” or “what” and explore individuals’ experiences, beliefs, and perceptions.
• Theoretical Framework -- What are the existing theories (theoretical framework) or concepts (conceptual framework) that can be used to support the research. How does existing theory link to what is happening in the educational environment with regard to the topic? What theories have been used to support similar topics in previous research?
• Literature Review -- Examine the literature, focusing on peer-reviewed studies published in journal within the last five years, with the exception of seminal works. What about the topic has already been explored and examined? What were the findings, implications, and limitations of previous research? What is missing from the literature on the topic?  How will your proposed research address the gap in the literature?
• Data Collection —Who will be part of the sample for data collection? What data will be collected from the individuals in the study (e.g., semi-structured interviews, surveys, etc.)? What are the educational artifacts and documents that need to be collected (e.g., teacher less plans, student portfolios, student grades, etc.)? How will they be collected and during what timeframe? (Note--A list of sample data collection methods appears under the heading of “Sample Instrumentation.”)
• Data Analysis —Determine how the data will be analyzed. Some types of analyses that are frequently used for action research include thematic analysis and content analysis.
• Implications —What conclusions can be drawn based upon the findings? How do the findings relate to the existing literature and inform theory in the field of education?
• Recommendations for Practice--Create a Plan of Action— This is a critical step in action research. A plan of action is created based upon the data analysis, findings, and implications. In the Applied Dissertation, this Plan of Action is included with the Recommendations for Practice. The includes specific steps that individuals should take to change practices; recommendations for how those changes will occur (e.g., professional development, training, school improvement planning, committees to develop guidelines and policies, curriculum review committee, etc.); and methods to evaluate the plan’s effectiveness.
• Recommendations for Research —What should future research focus on? What type of studies need to be conducted to build upon or further explore your findings.
• Professional Presentation or Defense —This is where the findings will be presented in a professional presentation or defense as the culmination of your research.

Adapted from Johnson (2019).

Considerations for Sampling and Data Collection

Below are some tips for sampling, sample size, data collection, and instrumentation for Action Research:

Sampling and Sample Size

Action research uses non-probability sampling. This is most commonly means a purposive sampling method that includes specific inclusion and exclusion criteria. However, convenience sampling can also be used (e.g., a teacher’s classroom).

Critical Concepts in Data Collection

Triangulation- - Dosemagen and Schwalbach (2019) discussed the importance of triangulation in Action Research which enhances the trustworthiness by providing multiple sources of data to analyze and confirm evidence for findings.

Trustworthiness —Trustworthiness assures that research findings are fulfill four critical elements—credibility, dependability, transferability, and confirmability. Reflect on the following: Are there multiple sources of data? How have you ensured credibility, dependability, transferability, and confirmability? Have the assumptions, limitations, and delimitations of the study been identified and explained? Was the sample a representative sample for the study? Did any individuals leave the study before it ended? How have you controlled researcher biases and beliefs? Are you drawing conclusions that are not supported by data? Have all possible themes been considered? Have you identified other studies with similar results?

Sample Instrumentation

Below are some of the possible methods for collecting action research data:

• Pre- and Post-Surveys for students and/or staff
• Staff Perception Surveys and Questionnaires
• Semi-Structured Interviews
• Focus Groups
• Observations
• Document analysis
• Student work samples
• Classroom artifacts, such as teacher lesson plans, rubrics, checklists, etc.
• Attendance records
• Discipline data
• Journals from students and/or staff
• Portfolios from students and/or staff

A benefit of Action Research is its potential to influence educational practice. Many educators are, by nature of the profession, reflective, inquisitive, and action-oriented. The ultimate outcome of Action Research is to create a plan of action using the research findings to inform future educational practice. A Plan of Action is not meant to be a one-size fits all plan. Instead, it is mean to include specific data-driven and research-based recommendations that result from a detailed analysis of the data, the study findings, and implications of the Action Research study. An effective Plan of Action includes an evaluation component and opportunities for professional educator reflection that allows for authentic discussion aimed at continuous improvement.

When developing a Plan of Action, the following should be considered:

• How can this situation be approached differently in the future?
• What should change in terms of practice?
• What are the specific steps that individuals should take to change practices?
• What is needed to implement the changes being recommended (professional development, training, materials, resources, planning committees, school improvement planning, etc.)?
• How will the effectiveness of the implemented changes be evaluated?
• How will opportunities for professional educator reflection be built into the Action Plan?

Sample Action Research Studies

Anderson, A. J. (2020). A qualitative systematic review of youth participatory action research implementation in U.S. high schools. A merican Journal of Community Psychology, 65 (1/2), 242–257. https://onlinelibrary-wiley-com.proxy1.ncu.edu/doi/epdf/10.1002/ajcp.12389

Ayvaz, Ü., & Durmuş, S.(2021). Fostering mathematical creativity with problem posing activities: An action research with gifted students. Thinking Skills and Creativity, 40. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S1871187121000614&site=eds-live

Bellino, M. J. (2018). Closing information gaps in Kakuma Refugee Camp: A youth participatory action research study. American Journal of Community Psychology, 62 (3/4), 492–507. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=133626988&site=eds-live

Beneyto, M., Castillo, J., Collet-Sabé, J., & Tort, A. (2019). Can schools become an inclusive space shared by all families? Learnings and debates from an action research project in Catalonia. Educational Action Research, 27 (2), 210–226. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135671904&site=eds-live

Bilican, K., Senler, B., & Karısan, D. (2021). Fostering teacher educators’ professional development through collaborative action research. International Journal of Progressive Education, 17 (2), 459–472. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=149828364&site=eds-live

Black, G. L. (2021). Implementing action research in a teacher preparation program: Opportunities and limitations. Canadian Journal of Action Research, 21 (2), 47–71. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=149682611&site=eds-live

Bozkuş, K., & Bayrak, C. (2019). The Application of the dynamic teacher professional development through experimental action research. International Electronic Journal of Elementary Education, 11 (4), 335–352. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135580911&site=eds-live

Christ, T. W. (2018). Mixed methods action research in special education: An overview of a grant-funded model demonstration project. Research in the Schools, 25( 2), 77–88. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135047248&site=eds-live

Jakhelln, R., & Pörn, M. (2019). Challenges in supporting and assessing bachelor’s theses based on action research in initial teacher education. Educational Action Research, 27 (5), 726–741. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=140234116&site=eds-live

Klima Ronen, I. (2020). Action research as a methodology for professional development in leading an educational process. Studies in Educational Evaluation, 64 . https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S0191491X19302159&site=eds-live

Messiou, K. (2019). Collaborative action research: facilitating inclusion in schools. Educational Action Research, 27 (2), 197–209. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135671898&site=eds-live

Mitchell, D. E. (2018). Say it loud: An action research project examining the afrivisual and africology, Looking for alternative African American community college teaching strategies. Journal of Pan African Studies, 12 (4), 364–487. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=133155045&site=eds-live

Pentón Herrera, L. J. (2018). Action research as a tool for professional development in the K-12 ELT classroom. TESL Canada Journal, 35 (2), 128–139. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=135033158&site=eds-live

Rodriguez, R., Macias, R. L., Perez-Garcia, R., Landeros, G., & Martinez, A. (2018). Action research at the intersection of structural and family violence in an immigrant Latino community: a youth-led study. Journal of Family Violence, 33 (8), 587–596. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132323375&site=eds-live

Vaughan, M., Boerum, C., & Whitehead, L. (2019). Action research in doctoral coursework: Perceptions of independent research experiences. International Journal for the Scholarship of Teaching and Learning, 13 . https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edsdoj&AN=edsdoj.17aa0c2976c44a0991e69b2a7b4f321&site=eds-live

Sample Journals for Action Research

Educational Action Research

Canadian Journal of Action Research

Sample Resource Videos

Call-Cummings, M. (2017). Researching racism in schools using participatory action research [Video]. Sage Research Methods  http://proxy1.ncu.edu/login?URL=https://methods.sagepub.com/video/researching-racism-in-schools-using-participatory-action-research

Fine, M. (2016). Michelle Fine discusses community based participatory action research [Video]. Sage Knowledge. http://proxy1.ncu.edu/login?URL=https://sk-sagepub-com.proxy1.ncu.edu/video/michelle-fine-discusses-community-based-participatory-action-research

Getz, C., Yamamura, E., & Tillapaugh. (2017). Action Research in Education. [Video]. You Tube. https://www.youtube.com/watch?v=X2tso4klYu8

Bradbury, H. (Ed.). (2015). The handbook of action research (3rd edition). Sage.

Bradbury, H., Lewis, R. & Embury, D.C. (2019). Education action research: With and for the next generation. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Bourner, T., & Brook, C. (2019). Comparing and contrasting action research and action learning. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Bradbury, H. (2015). The Sage handbook of action research . Sage. https://www-doi-org.proxy1.ncu.edu/10.4135/9781473921290

Dosemagen, D.M. & Schwalback, E.M. (2019). Legitimacy of and value in action research. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Johnson, A. (2019). Action research for teacher professional development. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Lewin, K. (1946). Action research and minority problems. In G.W. Lewin (Ed.), Resolving social conflicts: Selected papers on group dynamics (compiled in 1948). Harper and Row.

Mertler, C. A. (Ed.). (2019). The Wiley handbook of action research in education. John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/detail.action?docID=5683581

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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 ).

Decision tree to identify inferential statistics for an association.

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.

Further Reading

• Devor J, Peck R. Statistics: the exploration and analysis of data. 7th ed. Boston (MA): Brooks/Cole Cengage Learning; 2012. [ Google Scholar ]
• 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. [ Google Scholar ]
• Mendenhall W, Beaver RJ, Beaver BM. Introduction to probability and statistics. 13th ed. Belmont (CA): Brooks/Cole Cengage Learning; 2009. [ Google Scholar ]
• Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003. [ Google Scholar ]
• Plichta SB, Kelvin E. Munro’s statistical methods for health care research. 6th ed. Philadelphia (PA): Wolters Kluwer Health/Lippincott, Williams & Wilkins; 2013. [ Google Scholar ]

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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.

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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.

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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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Tool or Template

• Switchboard Tool Data Analysis Action Planning Templates

Data Analysis and Action Planning Templates

It’s critical to regularly analyze and act on data in order to make informed decisions and guide project implementation. To facilitate ongoing learning and project improvement, teams should hold regular data analysis and action planning meetings throughout the project cycle. These Data Analysis and Action Planning Templates can be used to plan for data analysis, document observations from analysis meetings, and document actions required to address issues that staff identify.

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• May 30, 2019
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PESTLE Analysis

Insights and resources on business analysis tools

PEST Analysis: Examples and Meaning in Business

Last Updated: Apr 8, 2024 by Jim Makos Filed Under: PEST Analysis

What is a PEST analysis, and what are its four parts? What is the difference between PESTLE analysis and PEST, and why is it important for every business? As a business student, analyst, manager or owner, you are called to conduct a PEST analysis sooner or later. In the next 10 minutes, I’ll go through everything you need to know about PEST analysis and how you can do a PEST analysis of an organization starting from scratch. I promise you’ll know more about PEST analysis than 99% of people out there, as I’m explaining everything as concisely as possible. Let’s start with the PEST analysis definition.

What is a PEST Analysis?

PEST analysis is a strategic tool for organizations to identify and assess how Political, Economic, Social, and Technological external factors impact operations so that they can gain a competitive edge. A PEST analysis helps you determine how these factors will affect a business’s performance and strategy in the long term. It is often used in collaboration with other analytical business tools. For example:

• A combination of PEST and SWOT analysis usually gives a clearer understanding of a situation with related internal and external factors
• PESTLE analysis is an extension of PEST analysis that covers legal and environmental factors

I’m going to explain the PEST analysis as simply as possible with examples and a template for better understanding. I will also show how to do a PEST analysis starting from scratch, even for people without any business education like me!

Why Do a PEST Analysis

It’s simple: to succeed. For a business to be successful, they need a few things:

• A solid product
• Marketing plan
• Identifiable brand
• Happy customers
• Thorough budget
• An investor or two
• Unique selling position
• And a whole lot of research

Throughout the endless market research, customer acquisition costs, and project risk assessments, business managers could forget about outside influences ( we call these external factors in this type of analysis). Aside from the company’s internal resources and industry factors, PEST’s macroeconomic factors can impact a company’s performance in a big way.

By being aware of external factors, managers can aid their business. But if they don’t know them, they can cripple their business before it begins. That’s how advantageous PEST analysis is .

What are the four parts of PEST analysis?

Now, let me explain each of the four parts of a PEST analysis more thoroughly. You’ll better understand what each of these external factors in this analysis is all about.

• Political – Here, government regulations and legal factors are assessed in terms of their ability to affect the business environment and trade markets. The main issues addressed in this section include political stability, tax guidelines, trade regulations, safety regulations, and employment laws.
• Economic – Next, businesses examine the economic issues that have an impact on the company. This would include factors like inflation, interest rates, economic growth, the unemployment rate and policies, and the business cycle followed in the country.
• Social – At this stage, businesses focus on the society and people. Elements like customer demographics, cultural limitations, lifestyle attitudes, and education come into play here. This part allows a business to understand how consumer needs are shaped.
• Technological – This may come as a surprise, but technology may not always be an ally for businesses. Depending on the product, technology may affect the organization positively but also negatively. In PEST’s last section we find technological advancements, the role of the Internet, and how an industry’s innovation creates winners and losers.

Every business is different. Some factors may not affect a firm or industry as they would with others. But it’s beneficial to have a well-rounded view of the many factors that could affect them. Along with the ones that will affect them.

This is why we do PEST analysis for a business — to be aware of risks, opportunities, influences, and limitations. Let’s go deeper into these external factors that impact the success of a business. I’ll also briefly mention a specific example for each of them.

Political Factors

Political factors in PEST analysis refer to the extent to which the government and political actions in a country influence the business climate. Here are some examples that will occasionally make it into the (P) of my PEST analysis:

• Tax policies
• Tax incentives
• Political tensions
• Employment laws
• Import restrictions
• Health and safety laws
• Consumer protection laws
• Tariff and Trade restrictions
• Regulation and deregulation

For instance, a country’s foreign policy often plays an important role in determining trade regulations. This can either result in trade restrictions or trade incentives and can affect an organization’s operations. Read my dedicated page on political factors with more examples here .

Economic Factors

In the (E) part of PEST Analysis, we run into how the economy affects the organization. I consider the following economic factors when doing a PEST analysis:

• Interest rate
• Inflation rates
• Exchange rates
• Unemployment rate

For instance, exchange rates affect a global organization by influencing the cost of imported and exported goods. Furthermore, interest rates influence the cost of capital available to the organization. Thus they are significant in the expansion and growth of a business. Find more economic factors and examples of how they affect businesses here .

Social Factors

Social factors include different cultural and demographic aspects of society. These can affect the macro-environment in which the organization operates.

In the ‘S’ part of the PEST analysis I usually examine:

• Age distribution
• Cultural diversity
• Demographics shifts
• Population growth rate
• Health consciousness and trends
• Changing consumer lifestyles and preferences

A study of these factors can help organizations understand the dynamics of existing and emerging potential markets along with future customer needs.

Social factors are more unpredictable than economic and political factors, simply because people are unpredictable. But every business needs customers. And what and how they buy has an immediate effect on an organization’s profitability.

Based on these social factors, marketers create buyer personas. These avatars are necessary for businesses to target the ideal customer.

For example, if you’re selling whey powder, you go after fitness enthusiasts and bodybuilders. You are looking for people that follow an active lifestyle. Hence, a declining trend in health consciousness doesn’t seem encouraging.

That’s the tip of the iceberg. Learn more about social factors here .

Technological Factors

Technological factors aren’t important only for tech-related businesses. The (T) part in PEST analysis may affect even the most old-school organization that’s been operating for a century.

Technology is evolving at a rapid pace and consumers are becoming extremely tech-savvy. With the advent of new technology, older technology gets outdated and obsolete. If an organization does not look out for technological changes, it can lag behind its competitors.

I often include the following technological factors when conducting a PEST analysis:

• Cybersecurity Threats
• Emerging Technologies
• Big data and computing
• AI and Machine Learning
• Supply Chain Automation

Let’s consider the advancements in computing; more specifically, networking.

If a business offers the latest and fastest Wi-Fi in their store, it’s an added luxury. It’s annoying if it still operates on 3G speeds, but won’t ruin sales. However, if they handle all receipts in an online database and that goes offline because they didn’t keep their network infrastucture up-to-date then they have a major problem. Especially in big holidays like Black Friday.

Again, this is about impact on the business operation. How will ‘X’ technology affect the business in the long and short term? That’s what we’re trying to figure out with PEST analysis.

A ton more technological factors can be found here .

PEST Analysis Examples

Here is a hypothetical PEST analysis example that can give you a clear understanding of how this works:

Here at PESTLEanalysis.com I rarely limit myself to PEST analysis. I almost always go the extra mile and include the Legal and Environmental factors when I initiate a PEST analysis. This leads to a more detailed analysis called PESTLE.

PESTLE Analysis: An extension of PEST Analysis

PESTLE analysis is an extension of PEST that is used to assess two additional macroeconomic factors. These factors are the  Legal and Environmental conditions that can have an impact on a organization. Examples of PESTLE analysis are similar to those of a PEST analysis, but they will include factors such as these:

• Discrimination laws
• Copyright and patent laws

Environment:

• Waste management
• Changes in weather and climate
• Laws regarding pollution and recycling
• Use of green or eco-friendly products and practices

So, if you want to assess a business situation comprehensively, a PESTLE analysis is a definite must. You can find more about that analysis here .

Why PEST Analysis Is Important For Every Business

So, now that we did a PEST analysis, how’s that going to help the business?

What does a five-year business plan look like? Or a ten-year plan? It likely involves growth.

Whether it’s the expansion of a product line or opening stores in new locations, business changes need proper preparation. And that’s where the PEST analysis comes in.

PEST analysis is the foolproof plan for business expansion !

Both new business owners and veterans should include PEST analysis in their business plan. By breaking down the critical influences in the P.E.S.T. categories, businesses get a better understanding of whether their next business move is strategic or doesn’t make sense.

For example, politics isn’t just about political tensions, unrest and elections. Politics are also about trade policies, regulations and taxation. Companies doing business worldwide have to consider laws in the countries they operate, as well. Even if they aren’t doing international trade yet, it could be a possibility in the future, and going in blind is a good way to toss success out the window.

PEST analysis helps people become aware.

Aware of how political parties and regulations can impact a business. And how the economy (past, present, and future) affects an industry. It allows people to understand consumers — who they are, what they buy, and why they don’t buy. And finally, it identifies what technology is necessary for the development and success of a product, business, or industry.

It’s almost like an outline. It shows people what influences impact the quality, success, or devastation of businesses and industries. You can’t stop the four influences, but if you’re aware of them and their impact, you can plan around, against, or with them.

PEST analysis is often used by business analysts, marketers, students, and business owners, since it’s super important for every business!

All you need to do a proper PEST analysis is time. And the payoff is worth every second.

How PEST analysis works

PEST analysis requires research and data, sometimes ten years old, sometimes only a couple. The more information I have to go through, the more accurate my final results will be. By looking into the past and the present, I can make predictions for the future.

By studying these recent developments through a PEST analysis lens, organizations are deciding whether to jump into this for the long haul or for the time being.

You want to look at your industry in a similar light. Ten years ago, did it exist? Has it slowed down within the last two years or are more companies diving in? More competition can be a strong sign an industry is booming, but it could also be the first sign of oversaturation.

Break down your assessment into the four categories of PEST analysis. Start with politics and work your way through the remaining factors. Or start from the bottom. Whatever gets the job done and makes the analysis enjoyable.

How to Do a PEST Analysis From Scratch

I’ve written dozens of PEST analyses over the last couple of years. Below I document my process on how to do a PEST analysis , even when you’ve never written one before.

You should have a topic in mind. Most PEST analyses are about a specific business, industry, or product. However, they can also be applied to countries, too. You can’t start without a topic, though, so have it ready.

Where to find information for your PEST analysis

It’ll be easier to find and segment information if you break your analysis down into four sections, like the acronym implies:

• Technological

Each section will require its own information. However, some of this information will overlap.

For instance, the economy is often closely tied to political (in)stability. And the state of the economy always affects consumers (social). You don’t need to look for these patterns specifically— it’ll become apparent as you discover new information.

Start with the history

You should be familiar with your topic. If you’re not, read about its history. Learn how it was established, how long it has been around, and who founded it. Read about any major achievements on the organization in question over the last few years. Jot down notes whenever something that seems relevant or important pops up.

After this informational primer, it’s time to start on the four sections. I do my PEST analysis in order of the acronym because the information often bleeds into the next section.

Finding Political Information

Political information is easier to find than in other sections of the analysis (social and technological, specifically). Here, you’ll want to investigate the current political climate.

For instance, if the organization originates from America, you’ll research the current political parties. Who is in charge? Has this affected business operations in any way?

If your topic (business, product, industry) was established years ago, what was the political climate like then? Are different parties in power now? If this is the case, then you’ll want to compare how things have changed for your topic from then to now.

This is also the section where you’ll look into laws and regulations affecting business. Remember the list we went through in the beginning.

I find this information with a simple Google search. Such as “tariff laws USA” (plug in the country you’re searching for if it’s not the United States).

It’s best to get this information from a government site. These sites end in .gov. You may also find information from organizations (websites ending in .org) but not all of these sites are legitimate organizations. Be wary while you research.

Honestly, most of the information you’ll find is dense. But it’s easier if you have a goal. Look for signs of:

• Government (in)stability
• Possible political corruption
• New bills/regulations that may impact your topic
• Any issues your topic has had with current/former regulations or political parties

If your topic is a company, finding the right information may be easier. Search for “company name + political issues” or “company name + policies” and see what comes up. Avoid any information from untrustworthy sites and sites with no legitimate source.

Finding Economic Information

While you’re researching political information, you may come across connections to the current economy. For instance, political instability often leads to economic instability. This causes unemployment rates to rise and employee strikes. This affects how much disposable income people have.

You may have already found information in your political section that confirms economic problems. But if you haven’t, search government sites for current tax rates, interest rates (if your topic involves international business), and the current state of the economy. Is it good? Thriving? Or bad and declining?

Again, use government websites. Search for economic statistics over the last few years. If your topic is an industry, see how many companies (startups) have started within the last few years.

If your topic is a business that has international stores, look into the relationship between the country of origin and each country the company does business. If the relationship is good, it’s often a good outlook for the company. But if it’s bad, it may lead to problems. What problems? Do a bit of digging online.

Also, if your PEST analysis is for a company, you may look into stocks . Have they been declining? On the rise? Because if it’s the former, then the business may not be looking good. And you’ll want to find out why .

If my topic is a business, I sometimes check out the competition. I’ll look into how that other company has been fairing economically, specifically how its sales have risen or fallen over the last couple of years. If it’s dropped products, shifted marketing efforts, etc., I want to know why . A competitor analysis isn’t always necessary , but it can shed light on possible problems your topic may face.

Finding Social Information

This section is a bit trickier. Political and economic sectors rely heavily on data and evidence. You can find this information on government websites. News sites too, even. And although you can find databases about demographics and population growth for this section — all applicable in a PEST analysis — I wouldn’t stop there.

In the social section, I often examine how consumers are impacted by political and economic factors. You can draw conclusions based on the information you’ve already gathered from your political and economic segments.

For instance, if there is political instability and the economy is on the fritz, then consumers may feel uneasy. They may have fewer job options. And that means they’re less likely to spend frivolously. If your topic is a luxury product, it may mean the company that makes it may have lower sales this year.

But you also want to learn about how consumers feel about your topic. If it’s a company, do consumers generally like it? Or is public opinion souring? There should be a reason for why.

Consider Facebook. The company’s CEO, Mark Zuckerberg, has consistently been in hot water over the years. If not for data breaches affecting millions of users, but for their shady involvement with fake news and political tampering.

This has led many consumers to shy away from using Facebook. And this affects businesses that use Facebook to reach new customers.

In this section of the PEST analysis, I’m more likely to search for my topic on news sites and publications. The more popular the topic, the easier it’ll be to find articles written about it. But if the topic has ever been in the news, you’ll likely find it online.

Websites to search include :

• Consumer Reports
• Local news websites
• Other reputable sources

If you know your topic has been in the news for something bad, you can search the topic + the problem.

Although the information may overlap, take keynotes here. See how the problem is affecting consumer opinion. You may even want to take a look at the comments (if there are any) and see what people are saying. It’s coming straight from the lion’s mouth (consumers).

I think many PEST analyses favor numbers too much. We live in a world where anyone with an opinion can be heard, thanks to the internet. And enough of those voices can cause a business to change its policies and products. It can even cause the company to collapse.

So it’s important to search for how consumers feel about your topic too.

Finding Technological Information

This section of the PEST analysis is a bit abstract as well. You’re looking into how new technological advancements has affected your topic positively or negatively. You should also look into what technology your topic uses (currently). And what technology they may want to incorporate.

You may want to look at competitors if your topic is a product or business. See what others are using. And think about why they are.

Press releases

It may be beneficial to search for press releases involving your topic, if possible. If your company is using new technology, they may have announced it through a press release. You can search “company name + press release” or search through these press release websites:

• PR NewsWire
• NPR: National Public Radio

You may also find other information here for the other sections of the PEST analysis. Which is just an overall bonus. If all else fails, check if your topic has a website (unless it’s an industry or country). Discuss how they use social media (if they don’t, then… discuss that too!). In this section, you’re assessing what your topic uses, what it doesn’t, and why.

Putting it all together in a final PEST analysis

You’ll likely have heaps of information at hand. For some it’ll feel like too much — but that’s never the case for a PEST analysis. As you begin to read through each section’s notes, incorporate the most interesting, pressing, or surprising information. If anything overlaps with other sections, include that too.

I write each section of a PEST analysis at a time. I take my notes and create coherent sentences. Sometimes I make a list of the most important points and include them that way. If the section is long, I’ll use subheadings to break up the information.

Work on each section separately. And then if there are overlapping themes, incorporate those in. You may want to use those at the end of each section to connect to the next.

Once you’ve done this, you’ve completed your PEST analysis! Most of the work is in finding the information and making it coherent. The last 10-20 percent is putting it all together. So, once the research phase is done, you’re basically done too!

Understanding PEST Analysis: Taking Action

In conclusion, developing an understanding of what is PEST analysis becomes even more important when a company is about to launch a new business or a new product. In general, when they are about to change something drastically. That’s when all these factors play an important role in determining the feasibility and profitability of the new venture.

Therefore, developing an understanding of PEST analysis is useful for organizations for analyzing and understanding the ground realities of the environment they have to operate in.

Realizing what is PEST and knowing how to take this analysis into consideration, the organization can be in a better position to analyze the challenges, environment, factors, opportunities, restrictions and incentives it faces. In case an organization fails to take into account any one of these factors, it may fail to plan and operate properly.

But don’t PEST analysis stop you. Here are some variations that may come in handy when assessing how the external environment affects an organization:

• STEEP Analysis
• STEEPLED Analysis
• SWOT Analysis

• Open access
• Published: 16 May 2024

Drawing up the public national Rational Pharmacotherapy Action Plan as part of social and health services reform in Finland: a bottom-up approach involving stakeholders

• Heidi Tahvanainen   ORCID: orcid.org/0000-0003-1315-0457 1 , 2 , 5 ,
• Liisa-Maria Voipio-Pulkki 2 ,
• Katri Hämeen-Anttila   ORCID: orcid.org/0000-0002-3515-0792 3 , 6 ,
• Ulla Närhi 2 , 4 ,
• Taina Mäntyranta 2 ,
• Anna-Riia Holmström   ORCID: orcid.org/0000-0002-3908-5430 5 &
• Marja Airaksinen   ORCID: orcid.org/0000-0002-6077-5671 5  

BMC Health Services Research volume  24 , Article number:  631 ( 2024 ) Cite this article

Metrics details

Ensuring equal access to medicines and their appropriate and safe use at reasonable costs are core functions of health systems. Despite that, few descriptions of national medicines policies' development processes and implementation strategies have been published. This study aimed to describe the government program-based development of the Rational Pharmacotherapy Action Plan in Finland as a part of the undergoing major health and social service system reform, also covering the implementation of rational pharmacotherapy in the reformed system and processes.

The data of this qualitative study consisted of public reports and Steering Group meeting memos related to the development of the national Rational Pharmacotherapy Action Plan that the Ministry of Social Affairs and Health coordinated. Qualitative content analysis applying systems theory and the conceptual framework of integrated services as theoretical frameworks was used as an analysis method.

The national Rational Pharmacotherapy Action Plan covering 2018–2022 was created in a bottom-up development process involving a wide range of stakeholders. Rational pharmacotherapy was redefined by adding equality as the fifth pillar to complement the previously defined pillars of being effective, safe, high-quality, and cost-effective. The Action Plan formed a normative framework for long-term development, with a vision and principles focusing on people-centeredness, better coordination and management of the medication use processes, the continuity of treatment paths and the flow of patient and medicines information through partnerships, and evidence-informed policies and practices.

Through intensive stakeholder participation, the bottom-up approach created a national vision and principles of rational pharmacotherapy along with strong commitment to implementing the goals and measures. The concern lies in ensuring the continuity of the Action Plan implementation and achieving a balanced long-term development aligned with the integrated and reformed national social and health services system. The development of the pharmaceutical system has several national and EU-level dependencies requiring political long-term commitment. While the Action Plan differs from the national medicines policy, it forms a good basis for long-term development covering important parts of medicine policy at the micro, meso, and macro levels of the service system.

Peer Review reports

National health systems may have several goals, but the ultimate is improving population health and well-being [ 1 ]. According to the World Health Organization (WHO), an effective health service system should meet these goals by providing equal access to affordable and high-quality services, including care and healing services, health promotion, prevention, and rehabilitation for the entire population [ 1 , 2 ]. Access to medicines is essential to a well-functioning health system and is necessary to achieve public health goals [ 1 , 3 ]. When successful, pharmacotherapy can save lives, maintain, or improve functional capacity, and mitigate or even prevent diseases or their symptoms, thus improving quality of life. The demand for and spending on pharmaceuticals are expected to grow due to aging populations, rising income levels, increasing costs of developing new technologies, and increased patient expectations [ 4 ]. Consequently, pharmaceuticals constitute a substantial portion of healthcare spending in Europe and globally. In Finland, pharmaceuticals accounted for 14–15% of the total social and health services costs in 2021 [ 5 ].

Many countries have experienced challenges in meeting all health service demands. Health inequalities, limited availability and access to services, safety, and productivity are the main shared concerns [ 6 , 7 , 8 ]. In the last decade, a major social and health services reform has been prepared in Finland, aiming to improve the coordination, integration, and equality of access to services while balancing continuously growing health and social services costs [ 9 , 10 ]. In many countries, including Finland, the government or another third party pays a significant part of pharmaceutical costs. Therefore, effective means are required to monitor and guide the safe and appropriate use of medicines and their cost-effectiveness. Pharmacotherapy can be considered inappropriate if it does not meet the conditions and core components of rationality defined by the WHO [ 11 , 12 ]. According to the WHO, rational use of medicines occurs when patients receive medications appropriate to their clinical needs, in doses that meet their own individual requirements, for an adequate period, and at the lowest cost to them and their community [ 11 ]. Due to the complex interrelationships of pharmaceutical and health system governance, countries have been recommended to find ways to harmonize better and align their pharmaceutical policy activities with national health policies and systems [ 1 , 13 ].

As part of a national policymaking, the need for rational pharmacotherapy programs has been recognized in several countries [ 4 , 14 , 15 ]. Few detailed descriptions of national medicines policies (NMP), development processes, and implementation strategies are available. However, such descriptions would benefit other countries in compiling their own policies [ 14 , 16 ] and developing international policy recommendations [ 3 , 4 ]. According to WHO, a NMP serves as both a commitment to a defined objective and a strategic roadmap for actionable steps [ 13 ]. This national comprehensive framework articulates and prioritizes the government's medium- to long-term goals for the pharmaceutical sector and use of medicines, outlining key strategies to achieve these objectives. For example, NMP has guided development activities in Australia since 2000 [ 16 ]. The overarching goal of the Australian’s NMP has been to optimize health outcomes through a collaborative partnership with key stakeholders. A similar target has been set for the NMP implementation in New Zealand [ 17 ]. In 2022, the updated Australian’s NMP has emerged as a coordinating framework that sets out a vision, common aim and intended outcomes, for all partners to work towards quality use of medicines and medicines safety by focusing on the current and future health needs of people in Australia [ 18 ].

In Finland, the development of the pharmaceutical system has been guided by the NMP since 2003 [ 19 ]. The NMP was originally drafted to evaluate the development needs of the pharmaceutical system in a situation where medicine legislation had not been comprehensively assessed for an extended period. Moreover, Finland had joined the European Union (EU) in 1995, resulting in the harmonization of national legislation with EU regulations. The purpose of the NMP was to bring predictability in the operating environment to several stakeholders in the medicines sector. In the 2011 NMP update, Finland implemented the WHO's recommendation to commit various key stakeholders to NMP goals by involving them in an open, systematic consultation process when preparing the NMP [ 13 , 20 ]. By doing so, Finland’s goal has been to develop the pharmaceutical sector and service system aligned with the health policy goals to meet the needs of the social and health services [ 21 ]. However, rapidly rising pharmaceutical costs, an aging population, and medication safety risks, especially among older adults, a fragmented operating system and culture, as well as pressures to promote the digitalization of healthcare formed complex challenges to be solved as part of the social and health services reform [ 22 , 23 , 24 ]. Therefore, the Ministry of Social Affairs and Health (MSAH) initiated in 2016 the preparation of the targeted Action Plan promoting rational pharmacotherapy based on the government mandate [ 25 ].

The aims of this study were to 1) describe the collaborative and bottom-up development process of the national long-term Rational Pharmacotherapy Action Plan during 2016–2017 and the key process outcomes, and 2) to analyze the content of the Action Plan in the conceptual framework of integrated care.

Context of the study

Finland has a population of 5.6 million, of which 1.7 million (30%) live in the metropolitan area of Helsinki [ 26 ]. Life expectancy at birth is one of the highest in the world, with 84.5 years for women and 79.2 years for men in 2021 [ 27 ]. GDP per capita was about 47,991 euros in 2022 [ 26 ]. Social security and access to health services are considered universal residents’ rights according to the Constitution of Finland [ 28 ].

Finland has a public healthcare system, complemented by private and occupational healthcare services [ 9 , 29 ]. The ongoing reform restructures the organization of public healthcare and social welfare systems, and rescue systems [ 10 ]. The aim of the new legislation is to ensure equal, interoperable, and cost-effective healthcare and social welfare services throughout the country. Additionally, the objective is to strengthen basic-level services and ensure better support for those who require a variety of social and healthcare services. In Finland care for the older people and substance abuse services are part of social welfare services unlike some other countries. Thus, integration and coordination of healthcare and social welfare services are essential and pharmacotherapy is part of this integration. In January 2023, the responsibilities of primary and secondary care were transferred from municipalities and hospital districts to the well-being services counties ( n  = 21) [ 10 , 29 ]. In addition, the City of Helsinki (the largest city) became responsible for services in its own area. The District of Helsinki and Uusimaa is responsible for specialized healthcare in the metropolitan area. Finland is divided into five collaborative areas for tertiary care, each with a university hospital [ 9 ]. The well-being services counties may either provide services, act in cooperation, or purchase services from private service providers [ 10 ]. The well-being services counties receive state funding according to the criteria set in legislation and supplement their finances with service fees, highest amounts of which are set in the legislation. They do not have the right to collect taxes to cover social and healthcare costs [ 10 ].

Pharmacotherapies conducted during hospital care are included in patient service fees, financed by the well-being service counties. In contrast, medication use in outpatient care is mainly covered by the public social insurance funded jointly by the government and the insured individuals (covering equally all permanent residents in Finland) [ 9 ]. Medicines for outpatient care are dispensed from community pharmacies and are partially or fully reimbursed by public social insurance, covering equally the entire population [ 9 , 30 ]. The reimbursement scheme for pharmacotherapy is disease-based and offers relatively high deductibles for long-term medicine users of chronic diseases [ 9 ].

In accordance with health service legislation in Finland, all operations are grounded in evidence-based practices [ 31 ]. The responsibilities related to informing decision-making through Health Technology Assessment (HTA) are decentralized among various organizations. In particular, the assessment and decision-making for outpatient care medicines fall under the jurisdiction of the Pharmaceutical Pricing Board [ 32 ], while new medicines for inpatient care are evaluated by the Finnish Medicines Agency (Fimea). Recommendations to introduce medicines in inpatient care are provided by the Council for Choices in Health Care [ 33 ]. Also, indication extensions of medicines are subject to HTA, coordinated by the Finnish Coordinating Center for Health Technology Assessment, with assessments conducted on a hospital level [ 34 ]. Furthermore, the decision-making has been informed by research conducted by several stakeholders: universities, state research institutes, agencies and institutions of various administrative branches, and advocacy organizations.

Since 2014, innovation activities in the medicines and health sector have been guided by the Health Sector Growth Strategy for Research and Innovation, along with its subsequent update, the Roadmap [ 35 ], prepared in cooperation with the Government and research and innovation funders and organizations in the health sector. The advancement of pharmaceutical innovations is crucial for treatment development and addressing unmet medical needs. Investing in innovation activities not only facilitates sector growth but also has the potential to boost health sector exports, a significant aspect of research, innovation, and industrial policy, particularly in Finland, where a considerable portion of medicines is imported [ 35 ]. The overarching focus of health sector policy and innovations in Finland has primarily centered on enhancing the ecosystem for personalized medicine [ 36 ].

Medicine use in outpatient care

In Finland, community pharmacies have remained the sole source of prescription and non-prescription medicines to outpatients with the exemption of nicotine replacement therapies released to open sale in 2006 [ 37 ]. Pharmacy operations are subject to licensing; a pharmacy owner must have at least a MSc (Pharm) degree, sufficient experience in pharmacy operations, prerequisites for running a pharmacy business and not been declared bankrupt or legally incompetent [ 38 ]. Community pharmacies are legally obligated to maintain an adequate supply of medicines that address the population’s needs within their operational area. Additionally, they must maintain adequate pharmaceutical personnel to fulfill certain duties, e.g., medication dispensing and counseling for both over-the-counter (OTC) and prescription medicines.

The retail prices of all medicines and the wholesale prices of medicines included in the reimbursement scheme are regulated [ 38 , 39 , 40 ]. The objective of regulating medicines price is to establish fair and equitable prices that benefit both medicine users and society at large. Furthermore, this regulatory framework sustains competition within the market for interchangeable medicines and shifts the focus of competition among community pharmacies primarily towards quality of customer service rather than pricing strategies. Price regulation ensures that community pharmacies of varying sizes can procure wholesale medicines at uniform rates, thereby enhancing vertical transparency in the distribution chain. Generic substitution was implemented in 2003, a reference price system in 2009, and the automatic substitution of biologics will be gradually implemented in the beginning of 2024 in outpatient care to enhance price competition and reduce medicines costs [ 41 , 42 ]. Since 2022, price regulation has made it possible to give discounts on the retail prices of OTC medicines [ 43 ]. Finland largely depends on imported medicines since there is no strong domestic pharmaceutical industry [ 44 ].

Since 2017, all outpatient prescriptions have been issued and dispensed electronically via a national electronic health record system, Kanta, maintained by the National Social Insurance Institution Kela [ 45 ]. Kanta is an entity of national patient information depository and data management services used by citizens, social and health service providers, and pharmacies [ 45 ]. It allows centralized use, storage, and maintenance of electronic patient data, and data exchange for cross-border purposes [ 46 , 47 ]. Citizens have adopted the use of Kanta services well and can browse their own medical records and prescriptions and, e.g., order repeat prescriptions through the online service [ 47 ]. The development and interoperability of national information management services and information management systems in social services and healthcare are guided and coordinated nationally [ 48 ].

Origins of the Rational Pharmacotherapy Action Plan

The prevention and mitigation of inequality, improving the availability, access, and continuity of care, and cost growth management have become increasingly important guiding principles in Finnish health policymaking during recent decades, reflected also in the NMP [ 10 , 20 , 29 ]. According to the NMP published in 2011, rational pharmacotherapy and good medication safety enhance people's well-being, improve public health, and decrease healthcare expenditures [ 20 ]. However, achieving the NMP goals had become more challenging, especially due to rapidly increasing medicines costs [ 23 ], the unequal and uncontrolled introduction of new pharmaceutical products [ 49 ] and medication safety risks, especially in the vulnerable population groups such as older adults [ 22 , 24 ]. Thus, the government program for 2015–2019 mandated the MSAH to establish a Rational Pharmacotherapy Action Plan (RPAP) [ 25 ]. The goals set for the Action Plan by the Government were to improve comprehensive patient care, improve people's functional capacity, and create conditions for cost-effective pharmacotherapy from the perspectives of the patient and society [ 50 ]. At the same time as the Action Plan was drawn up, social and health services reform was being prepared, which also covered the development of the pharmaceutical system aligned with the renewed social and health service system. It is to be noted that during the development of the Action Plan, no policy guidelines or legislation for restructuring the pharmaceutical system were available.

Theoretical framework of this study

System theory, also known as systems thinking, provides a holistic approach to understanding and evaluating complex phenomena, e.g., within social and health systems [ 1 , 51 ]. According to the system theory, all interventions tend to generate effects at the system level [ 51 ]. Different systems interact with each other but retain their autonomy due to the structures and processes of the system [ 1 , 3 , 51 ].

Integrated health systems are considered as a solution to maintaining accessibility, quality, and continuity of services [ 52 , 53 , 54 ]. System integration requires a tailor-made combination of structures, processes, and techniques to meet the service needs of people and population [ 54 ]. For example, functional integration includes mechanisms that establish connections between services through funding, information, and management. Normative integration consists of informal coordination mechanisms of mission, vision, values, and culture, which promote integration, if these have been successfully shared as a common set of goals at all levels of the system [ 54 ]. Different functions and processes at the system’s levels: the macro (system), the meso (organizational and professional) and the micro (clinical), complement each other to achieve integration goals [ 54 ].

In this study, the system theory [ 51 ] guided interpretations of the interaction between different parts of the pharmaceutical and health service systems. At the same time, the conceptual framework of integrated care [ 54 ] was applied to analyzing integration of functions between different actors in the medication use process at macro, meso and micro levels.

Study design and methods

This study is based on qualitative content analysis of the final report of the Rational Pharmacotherapy Action Plan (RPAP) [ 50 ] and the meeting memos of the Steering Group responsible for the development of the RPAP under the leadership of MSAH [ 55 , 56 , 57 ]. All reports published during the development process of RPAP were utilized in the analysis to verify the interpretations of the content analysis (Documents no. 3–13, Table s 1 in Additional File 1). The interpretation was also influenced by approved legislation of the national social and health services reform [ 58 ], the national programs that preceded the RPAP such as NMP 2020 [ 20 ], National recommendation for multidisciplinary cooperation in optimizing pharmacotherapy in older adults [ 24 ], Medicines information strategy 2020 [ 59 ] and Government resolution of patient and client safety strategy 2017–2021 [ 60 ]. In addition, recent developments under the initiative of the European Health Union [ 61 ] influenced the interpretation, e.g., the implementation of the European Pharmaceutical Strategy since 2020 [ 62 ], the Regulation on Health Technology Assessment [ 63 ], the proposal of the European Health Data Space including data and information development for cross-border healthcare and prescription development [ 64 ], and changes in the responsibilities and processes of the various EU Agencies [ 65 ]. Above mentioned reports describing the development of the operating environment were reviewed alongside the analysis to understand connections and dependencies.

A qualitative synthesis of data

The first part of the study was an inductive content analysis of the Steering Group meeting memos and the final report of the RPAP (Documents no. 1–2, Table s 1 in Additional File 1). The analysis focused on describing the development process of the Action Plan and the outcomes of the development process. The second part was a deductive content analysis of the key contents of the Action Plan based on the final report of the RPAP and Steering Group meeting memos. The system theory guided the analysis and thinking concerning how the structures and processes of the pharmaceutical system interacts as part of the social and health service system at the micro, meso and macro levels [ 51 ]. The conceptual framework of integrated care guided the analysis related to the medication use process [ 54 ].

First, the main themes of the final RPAP report and Steering Group memos, as well as the concept of rational pharmacotherapy were reviewed and refined to draw connections between NMP and broader social and health policies in the context of long-term development [ 10 , 20 , 21 , 58 , 59 , 66 ]. Then, the prioritized actions and principles to promote rational pharmacotherapy were identified. The prioritized actions were classified into the following functional integration categories of integration framework: funding (F), information (I), and management (M), depending on how the implementation of the prioritized action can be promoted. Working Group (WG) and expert reports published during the RPAP development process were utilized to verify the interpretations (Documents no. 3–13, Table s 1 in Additional File 1, Table s3 in Additional File 6). The classified actions were cross-tabulated with the key principles outlined by the Steering Group and presented in the RPAP and the different interacting levels of the system, i.e., micro, meso, and macro levels [ 51 , 54 ]. The long-term development visions for the system’s micro, meso, and macro levels were condensed based on the abstraction of prioritized actions and principles [ 51 , 54 ]. The EU level was considered at the system levels as the development of EU policy strongly influences the national development priorities of each Member State [ 62 , 63 , 64 , 65 ].

One researcher (HT) was responsible for the analysis, and other co-authors who were involved in the development process of RPAP (MA, KHA, UN, LMVP, TM) verified the validity of the analysis [ 57 ]. Author (UN), working in the MSAH at that time, launched the development project of the RPAP together with the chairman of the Steering Group, author (LMVP), who served at that time as director general in the MSAH. Author (HT), working in the MSAH at that time, coordinated the development of the RPAP and the compilation of the final report during 2017–2018. Author (KHA), working at that time in the Fimea, served as secretary of the Steering Group during 2016–2018 and was responsible for preparing the final report. Author (MA), professor at the Helsinki University, represented researchers to bring scientific evidence to the development process, served as chairman of the Research Working Group (WG) and member of the Steering Group, and author (TM), senior ministerial adviser in MSAH, brought information about the progress of social and health services reform to the development process of the RPAP, and served as chairman of the WG1 Prescribing, Dispensing and Use of medicines (Fig.  1 ).

Organization of the Rational Pharmacotherapy Action Plan development. MSAH = Ministry of Social Affairs and Health, HTA = Health Technology Assessment, WG = Working Group

Research ethics

Good scientific practices were followed throughout the research process [ 67 ]. Only reports available from open sources were used. The meeting memos were made available through a formal information request to the MSAH.

Description of the development process of the Rational Pharmacotherapy Action Plan

In January 2016, MSAH appointed a Steering Group to coordinate the RPAP development process, which consisted of four phases (Fig.  2 ). The aim was to create a long-term RPAP for national implementation using a bottom-up approach in cooperation with a wide range of stakeholders involved in the planning, policymaking, and implementation of social and health services.

Progression of the Rational Pharmacotherapy Action Plan development. MSAH = Ministry of Social Affairs and Health, RPAP = Rational Pharmacotherapy Action Plan, WG = Working Group

In the first phase, the Steering Group identified Finland’s current state and major challenges in rational pharmacotherapy. It established the following six WGs accordingly: WG1 Prescribing, dispensing and use of medicines, WG2 Pharmaceutical services as part of the social and health services system, WG3 Health Technology Assessment (HTA), WG4 Research, focusing on the ongoing and needed research in rational pharmacotherapy, WG5 Pharmaceutical innovations, and WG6 Data and information management (Fig.  1 ). The development needs of data and information management (WG 6) were derived from the work of each of the five WGs. The Steering Group set concrete goals for the WGs and guided the work by analyzing the current situation in regular follow-up meetings. An interim report was prepared by the Steering Group (Fig.  2 ).

A total of 40 stakeholder organizations and approximately 100 representatives participated in the development process. A diverse group of health and pharmaceutical experts and managers, authorities, and researchers were identified and invited by the MSAH to participate in the development work and WGs over two years. The following national umbrella organizations and stakeholders were involved in the Steering Group and WGs: public administrations, the major public third-party payor and government funding bodies, HTA bodies, civil servants working on the national preparation of the social and health care reform, organizations responsible for primary and secondary care (hospital districts), professional organizations and scientific societies, organizations representing pharmaceutical industry and community pharmacies, physicians, nurses, universities, hospital pharmacies, patient organizations and associations and representatives of the pensioners.

The work was based on a bottom-up activity that aimed to consider the interests of the various stakeholders in the field, even those with conflicting ones. The interaction with those involved in preparing the national social and health services reform progressing in parallel was regular and intensive. Similarly, the utilization of research results was a consistent practice. WG 4 mapped the ongoing research promoting rational pharmacotherapy, the researchers and research groups conducting the research, their view on future research needs and the need to improve research prerequisites and cooperation. The WGs used workshops, interactive seminars, invited experts for hearings, and extensive comment rounds of the draft documents in preparation. The overarching objective was to increase participation and commitment.

In the second phase, the WGs defined development measures based on assessments of the current state of rational pharmacotherapy, including administrative processes, strategies on patient safety [ 60 ] and medicines information [ 59 ], evidence of recent national development projects [ 24 ] and academic and non-academic literature (Fig.  2 ). The activities of the WG 4 supported the work of other WGs and enabled intensive utilization of research results. In addition, the following investigation reports were commissioned by the MSAH to inform the RPAP development: 1) The effects of EU legislation on national pharmaceutical system (not published), 2) The effects of social and health services reform on hospital pharmacy operations, 3) Steering for rational prescribing in selected countries, 4) Description of the regional organization and tasks of pharmaceutical working groups in Sweden, and 5) The patient-specific optimization of pharmacotherapies and the possibilities of information systems to support different phases of the medication management process (Reports no. 9–13, Table s 1 in Additional File 1). The regular interaction with the preparation of the national social and health services reform progressing in parallel guided the work of WGs 1, 2, and 3.

In the third phase, each WG compiled a separate report on action proposals to promote rational pharmacotherapy (Fig.  2 ). The development needs for data and information management was also compiled in a separate report based on the findings of each WG. A total of 13 reports were compiled to guide the development and promote rational pharmacotherapy at different levels of the service system (Table s 1 in Additional File 1). In the reports, comprehensive information was collected on national topics of rational pharmacotherapy, such as HTA operations and the utilization of its results in decision-making, as well as operating models and information needs of medicine users and different professionals in the different phases of the medication use process (e.g., the practices of collaborative medication review and optimization of pharmacotherapy, as well as the monitoring and documentation of the outcomes, as well as the division of work between different professionals). The existing national research evidence was used extensively to inform the work of different WGs, especially concerning the operation models and practices of safe and appropriate pharmacotherapy. Examples of rational pharmacotherapy steering methods were compiled from the sources described in the reports.

In the fourth phase, the secretariat of the Steering Group, the Chairpersons of each WG, and the coordinator of the RPAP program compiled the final report of the RPAP (Fig.  2 ). The writing process was contributed by constant discussion and reflection between the secretariat of the Steering Group (KHA), coordinator of the RPAP (HT), and the Chairpersons of each WG to grasp multiple perspectives and views of the stakeholders participating in the development work. One part of this phase was to redefine the concept of rational pharmacotherapy and legitimize it on the national level.

Outcomes and core contents of the Rational Pharmacotherapy Action Plan development process

The final report [ 50 ] of the RPAP was published in March 2018, and it was planned to cover a period until the end of the next government term, 2022. The main content of the Action Plan was visualized as a house, where the structures and governance of the service system and evidence form the foundation for the people-centered services and partnerships in the medication use process and management of the medication regimen (Additional File 2). In the RPAP, the roles of different professionals and medicine users in the medication use process were defined and described for the first time in a national document to illustrate the complexity and vulnerability of the flow of information in the medication use process in outpatient care (Additional File 3). During the preparation of the RPAP it was found that professionals need support in optimizing and managing pharmacotherapy in dialogue with the medicine users. Therefore, a set of questions was created to support the discussion of rational pharmacotherapy with medicine users on a partnership basis (Additional File 4).

Based on the RPAP final report [ 50 ] and national policy documents [ 10 , 20 , 21 , 59 ], the recognized main themes of long-term medicines policy development to promote rational pharmacotherapy were the management of the medication use process, pharmaceutical services, evidence-informed decision-making, research, and innovations (Fig.  3 ). In the Action Plan, rational pharmacotherapy was redefined through five dimensions (Fig.  3 ). Equality was added to rational pharmacotherapy to complement the four previous elements: effective, safe, high-quality, and cost-effective. Based on the Steering Group memos, the goals of effectiveness and equality were especially emphasized. The dimension of environmental awareness was not included in the RPAP definition of rational pharmacotherapy although it was noticed by the measures aiming to reduce waste in the medication use process.

The rational pharmacotherapy concept and long-term policy themes [ 20 , 50 ]. * Italic used to mark dimension not included in the definition of rational pharmacotherapy in RPAP. NMP = National Medicines Policy, RPAP = Rational Pharmacotherapy Action Plan

Based on the Steering Group memos, RPAP aimed to respond to identified challenges in the coordination, appropriateness, and continuity of pharmacotherapy by emphasizing people-centeredness, partnership, equality, and improved management of the medication use process and patient-specific medication regimen. The crystallized long-term normative development visions based on the RPAP final report [ 50 ] for the different levels of the service system were 1) micro: comprehensive medication management based on people-centered interventions and interprofessional collaboration, 2) meso: management of the medication use process and governance of the pharmaceutical services as a unified entity, and 3) macro: evidence-informed steering and decision-making on pharmacotherapy and pharmaceutical services (Fig.  4 ).

Long-term normative vision and principles to promote rational pharmacotherapy under the conceptual framework of integrated care [ 54 ]. EU = European Union

Based on the RPAP final report [ 50 ] and the WG reports (reports no. 3–13, Table s 1 in Additional File 1), the visions and principles of the Action Plan aimed at improving the coordination and management of the medication use, the continuity of treatment paths, the flow of patient and medicines information and the effective use of knowledge resources (Table s2 in Additional File 5). At the micro level, this requires a review of the tasks and responsibilities of medication users and different professionals in the people-centered, partnership-based medication use process. The development on the micro level emphasizes ensuring continuity of pharmacotherapy, improving the management and monitoring of the medication regimen, and enhancing the clinical decision-making based on comprehensive patient and medicines information as part of the development of treatment paths and information systems.

For the service organizers, currently the well-being service counties, the Action Plan set several goals for governance, coordinating the medication use process, and creating conditions for clinical work that utilizes the expertise of different professionals. Coordinated and integrated service production, as well as research and development activities utilizing information of national and local registers aim to support the continuous development of clinical and medication safety practice and targeting of pharmacotherapy and clinical pharmacy services to those who benefit the most (Table s2 in Additional File 5). According to the Action Plan well-being services counties should establish effective means and structures for service production steering, control, and continuity of care also considering community pharmacy services and resources to ensure rational use of medicines and the implementation of national guidance. Evidence-informed steering and decision-making require successful development of regional information systems, utilization of information and interoperability with national data repositories and information management services. The simultaneity and mutual understanding of meso and macro level development was noteworthy in the Action Plan.

The need to improve governance and steering was also emphasized at the macro level, where several authorities related to pharmacotherapy operate. Also, the need for developing the division of work and the roles of different authorities was identified in the Action Plan. At the macro level, it was noted that the development of the harmonization and exchange of information and the overall administration of the pharmaceutical system extend to the EU level. However, considering the competence of each Member State (Fig.  4 , Table s2 in Additional File 5). It was found that implementing the prioritized actions of the RPAP would require purposeful development at different levels of the system. This entails further development focusing on enhancing the functional integration of social and health services and the pharmaceutical system, utilizing funding, information, and management resources effectively.

Implementation

Based on the Steering Group memos, the implementation of the Action Plan was aimed to support in various ways to create awareness of the redefinition of the rational pharmacotherapy and the set development goals. This aimed to get the development started both by different stakeholders and at different levels of the system. MSAH allocated quite a lot of time and resources for establishing an awareness campaign held in two phases, first targeted to social and health professionals and then to the public. The campaign messages and materials were designed in cooperation with the RPAP Steering Group and stakeholders. Implementation was carried out jointly with Fimea. In addition, MSAH launched a dedicated rational pharmacotherapy website rich in content in early 2018 [ 68 ].

Drafting the RPAP was an intensive, time-consuming, but fruitful national process in Finland. It enabled a comprehensive review of the current pharmacotherapy practices and, based on that, the creation of the long-term vision of rational pharmacotherapy as part of the planned social and health services reform. Although final legal decisions on the future of the social and health services system and the structures of the pharmaceutical system integrated into it were missing during the planning phase of the RPAP, it was possible to define guiding visions and principles for the promotion of rational pharmacotherapy in the micro, meso and macro levels of the service system (Fig.  4 ).

A variety of authorities and stakeholders participated in the development of the RPAP. This bottom-up and co-creation process was chosen because of the good experience gained from preparing the NMP 2020 [ 20 ]. This approach has been recommended by the WHO [ 13 ], and later used to support EU pharmaceutical decision-making [ 62 ]. The chosen development process of the Action Plan has proved to be important for the stakeholder commitment and enabled consensus of the visions and principles that still carry the development. This reinforces the earlier finding that the policy process is just as important as the policy document since the process must create a mechanism to bring different views together and achieve a sense of shared ownership of the outcome [ 14 ]. For example, the rational pharmacotherapy website continues to be actively managed by MSAH. Plenty of practical tools developed to promote rational pharmacotherapy in everyday clinical practice have been identified and shared through coordination with Fimea [ 69 ] and national medication safety development network [ 70 ]. In addition, Fimea has integrated the RPAP communication campaign into Pharmacotherapy Day’s annual campaign week, which is now organized in collaboration with different stakeholders [ 71 ].

During the development of the Action Plan, there was a thorough discussion about the definition of rational pharmacotherapy which led to the extension of the concept towards equity. That reflected the national principal value base of equal rights, which had become threatened by rapidly increasing healthcare and pharmacotherapy costs. Consequently, in the 2000s, Finnish pharmaceutical policy and decision-making on pharmaceuticals revolved around how to curb the ever-increasing costs. To control costs, changes have been made, e.g., to the public medicines reimbursement system and pricing, which have led to relatively high deductibles for long-term medicine users [ 9 ] and may have led to a decline in the adherence to chronic diseases [ 72 ]. However, the entire medication use process which extends across the borders of several different organizations, had not been evaluated to ensure effective, appropriate, and cost-effective pharmacotherapy. The RPAP provided an opportunity for this comprehensive review and the identification of various development measures (Table s2 in Additional File 5).

Most of the medicines are used in outpatient care, so the measures of the Action Plan are largely focused there and are especially aimed at optimizing pharmacotherapy for chronic diseases and self-managing at home. However, the Action Plan’s goals were unrelated to the operational environment. The evolution of pharmaceutical services within inpatient care has been intensive after the launch of the RPAP. Traditionally, hospital pharmacy services have focused primarily on logistical functions, with only limited integration into clinical practices. The development of the information flow, task definitions, and responsibilities in the medication use process, as well as to defining consistent operating models for medication optimization and management have progressed quickly in inpatient care [ 73 , 74 ]. The number of ward and clinical pharmacy staff has remarkably increased between 2017 and 2022 in Finland, and services have extended widely, focusing on system-based medication safety work and the development of comprehensive medication management [ 74 ]. However, there is a lot of regional variation in development and the goals of the Action Plan are still relevant. Especially in outpatient care, pharmaceutical services except for dispensing, counseling, and automated dose dispensing, are not well integrated into daily clinical practice [ 75 ]. The community pharmacies are willing to develop their services improving medication safety and supporting rational pharmacotherapy [ 76 ], but non-formed legislation and incentives have hampered the progression. The challenges of the legislation and delays in achieving the goals set in the Action Plan are partially explained by the national social and health services reform, the implementation of which has finally created the conditions for the beginning of legislation development of the pharmaceutical system.

Based on the results of this study, the ultimate goal of developing the pharmaceutical system should be to improve its integration into the social and health services system. In the Action Plan prioritized development activities can be promoted by renewing management, funding, and information sharing in the pharmaceutical, social and health services. During the implementation period of RPAP, the further planning for developing pharmacotherapy data and information management has progressed systematically at national level. MSAH has recently published the enterprise architecture of pharmacotherapy [ 48 ] and plans for the development of a centralized national information management services for pharmacotherapy [ 77 ] and medicinal data repository [ 78 ]. Community pharmacy system’s reform needs (e.g., tasks and operations) have been investigated in detail, also from the general public approach [ 79 ]. In the current legislative framework, where community pharmacies are regulated as a separate part of the social and health service system, it is not possible to develop the exchange of patient information, and the tasks of different professionals and organizations in the medication use process agilely as in inpatient care. In addition, the vision has matured that the national level decision-making processes to guide the use of medicines must be developed [ 80 , 81 ], and the data and information about pharmaceuticals and pharmacotherapy which accumulates in different registers must be utilized better than at present [ 81 , 82 ].

The Action Plan also highlighted the need for change in governance and funding, which require a new way of thinking to create incentives for various actors in the medication use process to promote rational pharmacotherapy. For example, community pharmacies currently make a profit primarily from the sale of medicines and the definition of profit margins rather than from services that optimize the use of medicines and monitor their effectiveness. The Action Plan set several goals for the regional well-being service counties for managing the medication use process, the governance of the services, and creating conditions for interprofessional and people-centered collaboration. The success of the several goals set for the well-being service counties may require the expansion of their operational mandate to the entire medication use process. Based on the restructuring of the social and health services, the well-being services counties should guide production more strongly than municipalities did before and pay special attention to those who use many services and may also use expensive medicines or several different medicines for their ailments [ 58 ]. However, the well-being services counties currently do not have a mandate, e.g., to guide or oblige community pharmacies to develop their services in the medication use process. To make pharmacotherapy more rational, the state and well-being services counties must succeed in integrating the region's community pharmacies into the service chains and by this enable better utilization of available knowledge and resources to medication use optimization and management. The well-being services counties play a central role in many prioritized actions in the Action Plan to promote rational pharmacotherapy. Therefore, they will be a significant player in medicines policy in the future.

The parallel development of the European-level pharmaceutical system increases the challenge of national development. The European Commission has recently published the proposal for the major EU pharmaceutical legislation reform [ 83 ]. The proposal complements the key previous changes and initiatives [ 63 , 64 ]. The goal is to make medicines more equally available, accessible, and affordable in the EU region, boost competitiveness, fight against antimicrobial resistance, and give rules to digital transformation. These phenomena and challenges have partly been identified at the national level during the development of the preceding NMP 2020 [ 20 ] and RPAP [ 50 ]. Recent global, EU and national level policymaking are more strongly interlinked than before, where e.g., environmental consciousness as a sixth dimension of rational pharmacotherapy is emphasized as a new theme [ 62 , 66 , 84 , 85 ].

The strength and uniqueness of the RPAP is the utilization of research in identifying the development needs. Research has not previously been utilized in preparing of medicines policy on the same scale in Finland, although the researchers were also involved in the preparation of the preceding NMP [ 20 ]. The importance of evidence-informed decision-making in implementing RPAP is reflected in the long-term research strategy and establishment of a research network to support the implementation of the RPAP [ 86 , 87 ]. The utilization of academic research could also have influenced the fact that the coverage of the RPAP was comparable to other international NMPs published in other countries [ 17 , 18 , 88 ]. That reflects similar pharmacotherapy challenges in the health systems globally.

The RPAP was the crystallization of NMP thinking as part of the broader social and health policy during one term of government [ 25 ]. Currently, there is no updated comprehensive NMP in Finland and the progress of the implementation of the RPAP has also yet to be evaluated. However, to ensure the long-term implementation of the RPAP, a commitment across political party lines has been sought. The officials of the MSAH have developed frameworks for the pharmaceutical system development, which the previous (2019–2022) and current (2023–2027) governments have included in their programs [ 85 , 89 ]. This commitment aims to ensure continuity and mutual support for long-term and predictable development, transcending different government periods. However, the key question is whether the subsequent governments will commit to a balanced policy that considers the different dimensions of rational pharmacotherapy supporting public health and national health policy goals. It would be appropriate to reform the structures of the pharmaceutical system in a controlled manner and create systemic conditions for implementing rational pharmacotherapy in Finland.

The study results are based on several publicly available documents published by the authorities (Table s 1 in Additional File 1) and are consistent with the theoretical framework of integrated care [ 54 ]. Only one researcher was responsible for the analyses which is a limitation of this study. However, the interpretations have been validated by authors who have been strongly involved in the RPAP development work and have long experience in national development and working at the interface of politics from different approaches. Each author's point of view in the RPAP development process has been different, enabling the adoption of different perspectives and views during different phases of the analysis, thus strengthening the consensus. However, as a typical limitation to document analysis [ 57 ], the documents selected for the analysis and the documents used to verify interpretations have affected the results, i.e., the accuracy and comprehensiveness of the observations made. For example, the development needs to be related to pharmaceutical innovation activities remained few in the results. On the other hand, the pharmaceutical innovation theme was paid less attention due to the more urgent needs to evaluate the effects of the ongoing social and health services reform. The research materials consisted of the central available materials describing the process and the final results of the RPAP. The expertise of authors with the subject of the study compensates for the limitations of document analysis. With the help of the author group, consisting of civil servants and academics, and theories that guided the analysis, it has been possible to strengthen the reliability of the results. The results represent a national case study and are, therefore, not transferable as such to other countries. As presented in this study, the national descriptions of medicines policy and system development holds potential to be useful in several other countries. The results provide an opportunity for cross-country benchmarking and learning.

The major ongoing changes in the domestic and international operational environment affecting the whole pharmaceutical system in Finland require further research. National research in the key areas of rational pharmacotherapy covering structures, processes, and outcomes should be continued, as well as monitoring the impact of the policy measures taken [ 86 ]. In addition, the impact of the RPAP on stakeholders’ and patients’ experiences should be investigated. Further research is needed to investigate the prerequisites for integrating community pharmacy services into regional service chains and creating incentives. Internationally, further research is needed on the effectiveness of NMP guidance, and the indicators defined for implementing the NMP.

Conclusions

Through intensive stakeholder participation, the bottom-up approach created a national vision and principles of rational pharmacotherapy and a strong commitment to implementing the goals and measures. The concern lies in ensuring the continuity of the Action Plan implementation and achieving a balanced long-term development aligned with the integrated national social and health system. The development of the pharmaceutical system has several national and EU-level dependencies requiring long-term political commitment. While the Action Plan differs from the national medicines policy it forms a good basis for long-term development covering important parts of medicine policy at the micro, meso and macro levels of the service system.

Availability of data and materials

Publicly available reports from open sources: https://julkaisut.valtioneuvosto.fi/ and https://stm.fi/rationaalinen-laakehoito/julkaisut were used. Meeting memos of the RPAP Steering Group were made available through a formal information request to the Ministry of Social Affairs and Health (VN/22279/2022). The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

European Medicines Agency

European Union

Finnish Medicines Agency

Gross Domestic Product

Health Technology Assessment

Ministry of Social Affairs and Health

National Medicine Policy

Over the Counter (medicine)

Rational Pharmacotherapy Action Plan

Real-World Data

Working Group

World Health Organization

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Acknowledgements

The authors would like to thank the civil servants of the Ministry of Social Affairs and Health and National Medicines Agency Fimea who served as chairmen of the Working Groups. In addition, the authors would like to thank all those stakeholders who participated in the development process of the Rational Pharmacotherapy Action Plan as Working Group members.

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Authors: Heidi Tahvanainen (HT), Katri Hämeen-Anttila (KHA), Marja Airaksinen (MA), Ulla Närhi (UN), Liisa-Maria Voipio-Pulkki (LMVP), Taina Mäntyranta (TM), Anna-Riia Holmström (ARH). Study concept and design: HT, MA. Acquisition and analysis or interpretation of data: HT, MA. Drafting of manuscript: HT, MA. Critical revision of manuscript for important intellectual content: HT, KHA, UN, LMVP, TM, ARH, MA. All authors have approved the final manuscript to be published.

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Ministry of Social affairs and Health, Meritullinkatu 8, 00170 Helsinki, Finland

Heidi Tahvanainen (HT), Ulla Närhi (UN), Liisa-Maria Voipio-Pulkki (LMVP), Taina Mäntyranta (TM)

National Medicines Agency, Fimea, Mannerheimintie 166, 00300 Helsinki, Finland

Katri Hämeen-Anttila (KHA)

HT, UN, and KHA have been appointed to new positions in other organizations.

Faculty of Pharmacy, Division of Pharmacology and Pharmacotherapy, Clinical Pharmacy Group, University of Helsinki, Viikinkaari 5 E, P.O. BOX 56, 00014, Helsinki, Finland

Heidi Tahvanainen (HT), Anna-Riia Holmström (ARH), Marja Airaksinen (MA)

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Tahvanainen, H., Voipio-Pulkki, LM., Hämeen-Anttila, K. et al. Drawing up the public national Rational Pharmacotherapy Action Plan as part of social and health services reform in Finland: a bottom-up approach involving stakeholders. BMC Health Serv Res 24 , 631 (2024). https://doi.org/10.1186/s12913-024-11068-y

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Received : 27 September 2023

Accepted : 02 May 2024

Published : 16 May 2024

DOI : https://doi.org/10.1186/s12913-024-11068-y

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• Rational pharmacotherapy
• National medicines policy
• Pharmaceutical services
• Medication safety
• Community pharmacy
• Integration of services
• Social and health services reform
• People-centeredness
• Partnership
• Evidence-informed policy

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