experimental design presentation

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Prevent plagiarism. Run a free check.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bevans, R. (2023, June 21). Guide to Experimental Design | Overview, 5 steps & Examples. Scribbr. Retrieved August 29, 2024, from https://www.scribbr.com/methodology/experimental-design/

Is this article helpful?

Rebecca Bevans

Other students also liked, random assignment in experiments | introduction & examples, quasi-experimental design | definition, types & examples, how to write a lab report, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

User Preferences

Content preview.

Arcu felis bibendum ut tristique et egestas quis:

• Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris
• Duis aute irure dolor in reprehenderit in voluptate
• Excepteur sint occaecat cupidatat non proident

Keyboard Shortcuts

Lesson 1: introduction to design of experiments, overview section  .

In this course we will pretty much cover the textbook - all of the concepts and designs included. I think we will have plenty of examples to look at and experience to draw from.

Please note: the main topics listed in the syllabus follow the chapters in the book.

A word of advice regarding the analyses. The prerequisite for this course is STAT 501 - Regression Methods and STAT 502 - Analysis of Variance . However, the focus of the course is on the design and not on the analysis. Thus, one can successfully complete this course without these prerequisites, with just STAT 500 - Applied Statistics for instance, but it will require much more work, and for the analysis less appreciation of the subtleties involved. You might say it is more conceptual than it is math oriented.

  Text Reference: Montgomery, D. C. (2019). Design and Analysis of Experiments , 10th Edition, John Wiley & Sons. ISBN 978-1-119-59340-9

What is the Scientific Method? Section  

Do you remember learning about this back in high school or junior high even? What were those steps again?

Decide what phenomenon you wish to investigate. Specify how you can manipulate the factor and hold all other conditions fixed, to insure that these extraneous conditions aren't influencing the response you plan to measure.

Then measure your chosen response variable at several (at least two) settings of the factor under study. If changing the factor causes the phenomenon to change, then you conclude that there is indeed a cause-and-effect relationship at work.

How many factors are involved when you do an experiment? Some say two - perhaps this is a comparative experiment? Perhaps there is a treatment group and a control group? If you have a treatment group and a control group then, in this case, you probably only have one factor with two levels.

How many of you have baked a cake? What are the factors involved to ensure a successful cake? Factors might include preheating the oven, baking time, ingredients, amount of moisture, baking temperature, etc.-- what else? You probably follow a recipe so there are many additional factors that control the ingredients - i.e., a mixture. In other words, someone did the experiment in advance! What parts of the recipe did they vary to make the recipe a success? Probably many factors, temperature and moisture, various ratios of ingredients, and presence or absence of many additives.  Now, should one keep all the factors involved in the experiment at a constant level and just vary one to see what would happen?  This is a strategy that works but is not very efficient.  This is one of the concepts that we will address in this course.

• understand the issues and principles of Design of Experiments (DOE),
• understand experimentation is a process,
• list the guidelines for designing experiments, and
• recognize the key historical figures in DOE.

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

Share this:

Reader Interactions

March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

Comments and Questions Cancel reply

Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Note that this "residual" for the within plot \(subplot\) part of the analysis is actually the sum of squares for the interaction of rows \(w\ hole plots\) with varieties \(subplot treatments\)---as in an RCBD.

- r_k\(i\) ~ N\(0, sigma^2_r\)

- e_ijk ~ N\(0, sigma^2_e\)

• Privacy Policy

Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Case Study – Methods, Examples and Guide

Quasi-Experimental Research Design – Types...

Correlational Research – Methods, Types and...

Survey Research – Types, Methods, Examples

Descriptive Research Design – Types, Methods and...

Transformative Design – Methods, Types, Guide

Experimental Design

Mar 25, 2012

650 likes | 2.04k Views

Experimental Design. Experimental Design and the struggle to control threats to validity. LOW. NATURALISTIC. CASE-STUDY. INCREASINGLY CONSTRAINED. CORRELATIONAL. DIFFERENTIAL. EXPERIMENTAL. HIGH.

Share Presentation

• low impact methods
• measure impacts
• biological growth

Presentation Transcript

Experimental Design and the struggle to control threats to validity

LOW NATURALISTIC CASE-STUDY INCREASINGLY CONSTRAINED CORRELATIONAL DIFFERENTIAL EXPERIMENTAL HIGH

Experimental design is a planned interference in the natural order of events by the researcher.

What is Experimental Design? Experimental research allows us to test hypotheses and infer causality under controlled conditions designed to maximize internal validity. The high control and internal validity often mean a reduction of external validity. That is, the more precise, constrained, and artificial we become in the experiment, the less natural are the procedures and findings. The result is that we sometimes have difficulty generalizing experimentation to the natural environment, or a larger population.

Experimental Design • Comparisons are made under different and controlled conditions. • Subjects are assigned to each type of condition in an unbiased manner, usually matched or random. • Although causality can often be inferred, results may not be applicable outside of the experimental setting • Causality: If X, then Y AND If not X, then not Y

TIME 0: PRE-TEST. Collect baseline data. CONTROL TREATMENT #1 TREATMENT #2 TREATMENT #3 Experimental

TIME 1: TREATMENT GIVEN CONTROL JOLT COKE COFFEE Experimental

TIME 3: POST-TEST. Collect data on the effects of the treatment and compare to pretest, and to each treatment. CONTROL JOLT COKE COFFEE Experimental

Hypotheses • NULL (Statistical)mean JOLT= mean COKE = mean COFFEE = mean CONTROL • RESEARCH (Causal)Caffeine causes people to grow tall and nervous. • CONFOUNDING (Rival)The differences are due to the amount of citric acid in the drinks.

Compare Scores A simple 2-group, posttest-only design • Outfitters given low-impact training • Outfitters given NO low-impact training • Measure impacts caused by their clients • Measure impacts caused by their clients

Hypotheses • NULL (Statistical)mean Trained= meanUn-trained • RESEARCH (Causal)The clients of outfitters trained in low impact methods will cause fewer impacts than clients of outfitters who did not receive such training. • CONFOUNDING (Rival)Prior knowledge may have caused the observed differences in response.

Threats to Validity

Reliability • Repeated use of the measure with identical subjects yields identical and consistent results. It is improved by: • Clear conceptualization • Precise measurement • Multiple indicators • Pilot-testing

Validity • Internal Validity– design and measurement concerns that reduces chances for internal errors. • External Validity– describes our ability and intent to generalize to subjects beyond our study sample. Largely an issue of design and sampling.

Validity and Reliability Reliable, NOT valid Valid, NOT Reliable Valid AND Reliable NOT Valid, NOT Reliable NOT Valid but Reliable Valid but UNReliable NOT Valid and UNReliable Valid and Reliable

Internal Validity • Specifically, measurement validity • Measures are valid for a single purpose • Three types of validity: • Face—as judged by others or by logic • Content—captures the entire meaning of the experience • Criterion—agrees with a validates, reliable external source: • Concurrent, agrees with a preexisting measure • Predictive, agrees with a future behavior or outcome

Validity • Because of the confounding Hypothesis we are not 100% sure that our conclusions are valid. • Did we indeed measure the effects of new knowledge? • Independent -- Dependent? • Other Variable/s -- Dependent?

Road map for research success • Anticipate all threats to validity. • Take plans to eliminate them. • Statistical validity • Construct validity • External validity • Internal validity.

Statistical validity • The variations in the dependent variable are not due to variation in the subjects, but due to variations in the measuring instrument. The instrument is unreliable. • Some statistical assumptions have been violated (e.g., non-normal data treated with parametric statistics; means of ordinal data, etc.).

Construct validity • How well the observed data support the theory, and not a rival theory.

External validity • The degree to which the findings of the study are valid for subjects outside the present study. The degree to which they are generalizable. • Unbiased, complex sampling procedures; many studies, mid-constraint approaches help strengthen external validity. • < external = > internal

Internal Validity • Threats to causality (our ability to infer). • Did the independent variable cause the dependent to change (were they related), or did some confounding variable intervene? • Maturation, history, testing, instrumentation, regression to the mean, selection, attrition, diffusion and sequencing effects.

Maturation • If the time between pre- and posttest is great enough to allow the subjects to mature, they will! • Subjects may change over the course of the study or between repeated measures of the dependent variable due to the passage of time per se. Some of these changes are permanent (e.g., biological growth), while others are temporary (e.g., fatigue).

History • Outside events may influence subjects in the course of the experiment or between repeated measures of the dependent variable. • Eg., a dependent variable is measured twice for a group of subjects, once at Time A and again at Time B, and that the independent variable (treatment) is introduced between the two measurements. • Suppose also that Event X occurs between Time A and Time B. If scores on the dependent measure differ at these two times, the discrepancy may be due to the independent variable or to Event X.

Testing • Subjects gain proficiency through repeated exposure to one kind of testing. Scores will naturally increase with repeated testing. • If you take the same test (identical or not) 2 times in a row, over a short period of time, you increase your chances of improving your score.

Instrumentation • Changes in the dependent variable are not due to changes in the independent variable, but to changes in the instrument (human or otherwise). • Measurement instruments and protocols must remain constant and be calibrated. • Human observers become better, mechanical instruments become worse!

Regression to the mean • If you select people based on extreme scores (High or low), in subsequent testing they will have scores closer to the mean (they would have regressed to the center).

Selection • When random assignment or selection is not possible the two groups are not equivalent in terms of the independent variable/s. • For example, males=treatment; females=control. • Highest threats in naturalistic, case study and differential approaches.

Attrition • When subjects are lost from the study. • If random it may be OK. • Confounding attrition is when the loss is in one group or because of the effects of the independent variable. (Jolt killed off 2 people!)

Diffusion of treatment • When subjects communicate with each other (within and between groups) about the treatment) they diffuse the effects of the independent variable.

Sequencing effects • The effects caused by the order in which you apply the treatment. • A B C • A C B • B A C, etc.

Subject effects • Subjects “know” what is expected of them, and behave accordingly (second guessing). • Social desirability effect. • Placebo effect. A placebo is a dummy independent effect. Some people react to it.

Experimenter effects • Forcing the study to produce the desired outcome. • Expectations of an outcome by persons running an experiment may significantly influence that outcome.

Single- and double-blind procedures • Single blind –subjects don’t know which is treatment, which is not. • Double blind—experimenter is also blind.

Designs • One shot: • G1 T------O2 • One Group Pre-Post: • G1O1------T------O2 • Static Group: • G1 T------O2 • G2 (C) ------O2 T=Treatment O=Observation (measurement) C=Control

More designs • Random Group: • RG1 T-------O • RG2 (C) O • Pretest-Posttest, Randomized Group: • RG1O1------T------O2 • RG2 (C) O1------- ------O2 R=Random

Yet another design: • Solomon four-group: • RG1 O1------T------O2 • RG2 O1-------------O2 • RG3 T------O2 • RG4 O2

One last one ! • Latin Square: • RG1 O T1 O T2 O T3 O • RG2 O T2 O T3 O T1 O • RG3 O T3 O T1 O T2 O

• More by User

Experimental Design. Week 9 Lecture 1. Agenda. Purpose of experimental design Key elements in experimental design Various types of experimental design Causal-comparative design Field and controlled laboratory experiment. Purpose of experimental design.

1.05k views • 20 slides

Introduction. Good experimental design allows you to:Isolate effects of each input variableDetermine effects due to interactions of input variablesDetermine magnitude of experimental errorObtain maximum info with minimum effort. The fundamental principle of science, the definition almost, is thi

516 views • 28 slides

Experimental Design. How To Design A Psychology Experiment. Start with a research question It must be testable – you must be able to change one variable and measure another Identify the variable you will change – the independent variable (IV)

344 views • 7 slides

Experimental Design. Dependent variable (DV): Variable observed to determine the effects of an experimental manipulation (behavior) Independent variable (IV): Variable manipulated by the experimenter (environmental event or treatment)

620 views • 15 slides

Experimental Design. Christian Ruff With thanks to: Rik Henson Daniel Glaser. Statistical parametric map (SPM). Design matrix. Image time-series. Kernel. Realignment. Smoothing. General linear model. Gaussian field theory. Statistical inference. Normalisation. p &lt;0.05. Template.

603 views • 37 slides

Experimental Design. Fr. Clinic II Dr. J. W. Everett. Planning. Begins with carefully considering objectives (or goals) How do our filters work? Which filter is best? Performance Cost Ease of Use Durability…. Variables.

424 views • 24 slides

Experimental Design. Experimental Design. Strongest design with respect to internal validity. If X then Y and If not X, then not Y or If the program is given , then the outcome occurs and If the program is not given , then the outcome does not occur. Dilemma.

637 views • 45 slides

Experimental Design. Hw 3. RECap : scientific method. The Water Cycle. Earthquakes &amp; Tsunamis. Earth’s Seasons. Science. We’ve learned a lot of bad ways (fallacies) for figuring out whether claims are true. There is a good way of finding things out: science!

1.07k views • 77 slides

EXPERIMENTAL DESIGN

EXPERIMENTAL DESIGN. Science answers questions with experiments. Begin by asking a question about your topic. DEFINE THE PROBLEM. What is a good question for an experiment?. One that is testable with the materials at hand. Now we need a hypothesis to guide our investigation.

590 views • 44 slides

Experimental Design. Hw 3. Harry Potter. Best selling book series in history. Highest grossing film series in history. “Harry Potter and the Prisoner of Azkaban” won the Hugo Award for best Novel. Argument from Ignorance. “Absence of evidence is not evidence of absence.” – Carl Sagan

664 views • 54 slides

Experimental Design . Designs that allow testing of hypotheses. Learning objectives. Describe pre-experimental, experimental and quasi-experimental research designs.

485 views • 13 slides

Experimental Design. P291 Research Methods November 20, 2012. Well designed e xperiments have:. Research hypotheses with specifically predicted causal effects At least 2 levels of one IV Random assignment of participants

336 views • 7 slides

Experimental Design. A researcher can most convincingly identify cause-and-effect relationships by using an experimental design In such a design, the researcher considers many possible factors that might cause or influence a particular condition or phenomenon

691 views • 41 slides

EXPERIMENTAL DESIGN. Experimental and Pre (Quasi) Experimental Designs. Basic Issues in Experimental Design. Manipulation of the Independent Variable Selection of the Dependent Variable Assignment of subjects (or other test units) Control over extraneous variables.

766 views • 21 slides

Experimental Design. EPP 245/298 Statistical Analysis of Laboratory Data. Basic Principles of Experimental Investigation. Sequential Experimentation Comparison Manipulation Randomization Blocking Simultaneous variation of factors Main effects and interactions Sources of variability

831 views • 51 slides

Reaching a balance between statistical power and available finances. Experimental Design. Cost of Microarrays. Glass arrays €250 - €400 Affymetrix arrays €700 - €900. Experimental Design. Choice of microarray Hybridization design Number of replicates Dye-bias RNA samples.

292 views • 14 slides

Experimental Design. 8 th Grade Science Objective: Design a controlled scientific experiment S1, C2, PO2. What careers or professions use the Scientific Method?. scientists city water/ sewer/ landfills/etc. medical professions research and development of products ??.

417 views • 24 slides

Experimental Design. Brian Mennecke College of Business Iowa State University. The Source…. The source of much of this information comes from Campbell &amp; Stanley…

509 views • 36 slides

Experimental Design. Sara Bengtsson With thanks to: Christian Ruff Rik Henson. Statistical parametric map (SPM). Design matrix. Image time-series. Kernel. Realignment. Smoothing. General linear model. Gaussian field theory. Statistical inference. Normalisation. p &lt;0.05.

677 views • 42 slides

Experimental Design. How to conduct a valid experiment. A Good Experiment. Tests one variable at a time. If more than one thing is tested at a time, it won’t be clear which variable caused the end result.

417 views • 26 slides

Plant Breeding and Genomics

Introduction to Experimental Design

Shawn C. Yarnes, The Ohio State University

Defining Variables and Experimental Units

Experimental design begins with the formulation of experimental questions , which help define the variables that will change in an experiment. Experimental treatments , or independent variables , are the controlled part of an experiment expected to affect the response , or dependent variables . The experimenter must identify which treatment and response variables will best answer experimental questions.

Consider the broad experimental question. How do plants respond to fertilizer application? This question must be made more specific to design an effective experiment. 

The dependent variable , plant response , can be defined and measured in numerous ways. If the experimenter is interested in plant growth and nitrogen content, the question can be made more specific by asking how does plant growth and nitrogen content change in response to fertilizer application? Determination of response variables is influenced by experimental objectives and practical considerations. For example, total dry weight is more accurate than height as a measurement of plant growth, but in the case of a tree experiment, height might be more practical.

The independent variable , fertilizer treatment , can also be defined in numerous ways that will help specify experimental questions. A single fertilizer treatment with different levels can be tested, or multiple fertilizers compared. Levels can be: qualitative, or categorical, as when denoting males and females in a population; or quantitative, such as different fertilizer concentrations. Levels can also be defined as fixed or random effects. Sex distribution in a population is generally a random effect ; while fertilizer application is an experimenter controlled, or  fixed effect . The decision to define a variable as fixed or random will affect future statistical analyses (See  Analysis of Variance (ANOVA): Experimental Design for Fixed and Random Effects ). 

Once response and experimental treatments are defined, proper control treatments must be determined. Controls are integral to the scientific method by providing baseline values against which other treatments are compared. Negative controls , such as non-fertilized plants in Example 1, are null treatments where no response is expected. The simplest experiment has one response variable, one negative control, and one treatment. If experimental results support a null hypothesis (H 0 ), no significant difference is observed between controls and other treatments. 

Positive controls are treatments where a known response is expected. Positive controls are often used to validate assays or equipment functioning. For example, many enzyme kits come with pre-digested substrates, so that experimental digestions can be deemed successful compared to the positive control. Positive controls can also be used to calibrate or standardize measurements. For example, a standard curve of known substrate concentrations can be used to calculate the amount of unknown substrate concentrations.  

Experimental units must be defined during experimental design. The experimental unit is an individual, object, or plot subjected to treatment independently of other units. The number of experimental units is the sum of all treatments, levels, and and replicates. When experimental units are sampled only once, the experimental unit and sampling unit  are the same. The experimental unit can also be comprised of multiple sampling units. When experimental units are heterogeneous for the response variable, the mean of multiple sample units can be more precise than a single measure.  For example, if leaf nitrogen content is variable between leaves, an experimenter may choose to measure the nitrogen content from multiple leaves, using the mean nitrogen content to represent the individual plant.  Increasing the sampling units does not increase replication.

Planning for Statistical Inference

The goal of an experiment is to detect differences between treatments. Statistical determination of these differences requires replication to compute experimental error and randomization to help ensure that the measure of experimental error is valid. Discussions of experimental error and replication become circular, because replications are needed to compute experimental error, and the number of replications needed is based on the magnitude of experimental error. Experimental design requires an a priori estimation of error. In some situations a preliminary study is used to estimate error. In other situations error is inferred using reasonable assumptions based on the current understanding of the study system.

Experimental Error

Experimental error is the variation among experimental units within the same treatment group. There are many possible reasons for error. Errors within an experiment are additive. Reducing the amount of error in an experiment increases your ability to detect significant differences between treatments. A well-designed experiment considers the error contributed by both natural variation and lack of experimental uniformity.

Natural variation is a large component of error in biological experiments. Genetic and developmental differences, as well as differences in species abundance and diversity, can vary between experimental units. In plant breeding, clones and inbreed lines are often utilized to reduce genetic variation between experimental units. 

Lack of experimental uniformity is the source of error over which an investigator has the most control. Although there is always an imperfect ability to provide identical environments for each experimental unit, identifying and controlling error is essential. Errors in technique and/or data recording can inflate estimated experimental error (decrease precision) and introduce bias into the results (decrease accuracy).

Relationship Between Error and Sample Size

The sample size needed to detect differences between treatments increases with error. This is the reason biological field experiments generally require larger sample sizes than more controlled laboratory experiments. Experimental effort and expense are directly proportional to sample size. For these reasons controlling error is the focus of every investigator.  

The graph below illustrates the realtionship between error (σ), sample size, and the ability to detect differences between two means. (See  Estimating Sample Size for Comparison of Two Means and  Equation to Estimate Sample Size Required for QTL Detection ).

Funding Statement

Development of this page was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, agreement 2009-85606-05673, administered by Michigan State University. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture.  

PBGworks 1445

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

good and valuable

Very very good

Good presentation.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Promoting Research

Graphical Abstracts Vs. Infographics: Best practices for using visual illustrations for increased research impact

Dr. Sarah Chen stared at her computer screen, her eyes staring at her recently published…

• Publishing Research

10 Tips to Prevent Research Papers From Being Retracted

Research paper retractions represent a critical event in the scientific community. When a published article…

• Industry News

Google Releases 2024 Scholar Metrics, Evaluates Impact of Scholarly Articles

Google has released its 2024 Scholar Metrics, assessing scholarly articles from 2019 to 2023. This…

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• AI in Academia
• Career Corner
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer Review Week 2024
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

In your opinion, what is the most effective way to improve integrity in the peer review process?

Going beyond the comparison: toward experimental instructional design research with impact

• Methodology
• Published: 28 August 2024

Cite this article

• Adam G. Gavarkovs 1 ,
• Rashmi A. Kusurkar 2 , 3 , 4 ,
• Kulamakan Kulasegaram 5 , 6 &
• Ryan Brydges 6 , 7  

31 Accesses

Explore all metrics

To design effective instruction, educators need to know what design strategies are generally effective and why these strategies work, based on the mechanisms through which they operate. Experimental comparison studies, which compare one instructional design against another, can generate much needed evidence in support of effective design strategies. However, experimental comparison studies are often not equipped to generate evidence regarding the mechanisms through which strategies operate. Therefore, simply conducting experimental comparison studies may not provide educators with all the information they need to design more effective instruction. To generate evidence for the what and the why of design strategies, we advocate for researchers to conduct experimental comparison studies that include mediation or moderation analyses, which can illuminate the mechanisms through which design strategies operate. The purpose of this article is to provide a conceptual overview of mediation and moderation analyses for researchers who conduct experimental comparison studies in instructional design. While these statistical techniques add complexity to study design and analysis, they hold great promise for providing educators with more powerful information upon which to base their instructional design decisions. Using two real-world examples from our own work, we describe the structure of mediation and moderation analyses, emphasizing the need to control for confounding even in the context of experimental studies. We also discuss the importance of using learning theories to help identify mediating or moderating variables to test.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Research-Based Instructional Perspectives

To prove or improve, that is the question: the resurgence of comparative, confounded research between 2010 and 2019

Instructional Design Methods and Practice

Explore related subjects.

• Artificial Intelligence

Data availability

No datasets were generated or analysed during the current study.

As an alternative to the regression approach, structural equation modelling (SEM) has gained popularity in the health professions education literature (Stoffels et al., 2023 ). SEM requires that a researcher make additional assumptions regarding the functional relationships between the covariates, the mediator(s), and the outcome(s) (VanderWeele, 2012 ). Though specifying these relationships can increase power, it comes with an increased risk of model misspecification (VanderWeele, 2012 ). Accordingly, we recommend that researchers beginning with experimental comparison studies involving a single mediator opt for using the regression-based approach with controls for mediator-outcome confounding (VanderWeele, 2012 ).

We did not actually analyze our data in the manner described below, for reasons described in our published manuscript. Here, we describe an alternative data analysis strategy for clarity.

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51 (6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Article   Google Scholar  

Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan . Journal of Statistical Software . https://doi.org/10.18637/jss.v080.i01

Carver, C. S., & Scheier, M. F. (1998). On the Self-Regulation of Behavior (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139174794

Book   Google Scholar  

Cheung, J. J. H., & Kulasegaram, K. M. (2022). Beyond the tensions within transfer theories: Implications for adaptive expertise in the health professions. Advances in Health Sciences Education, 27 (5), 1293–1315. https://doi.org/10.1007/s10459-022-10174-y

Cheung, J. J. H., Kulasegaram, K. M., Woods, N. N., & Brydges, R. (2019). Why Content and cognition matter: Integrating conceptual knowledge to support simulation-based procedural skills transfer. Journal of General Internal Medicine, 34 (6), 969–977. https://doi.org/10.1007/s11606-019-04959-y

Cheung, J. J. H., Kulasegaram, K. M., Woods, N. N., & Brydges, R. (2021). Making concepts material: A randomized trial exploring simulation as a medium to enhance cognitive integration and transfer of learning. Simulation in Healthcare: THe Journal of the Society for Simulation in Healthcare, 16 (6), 392–400. https://doi.org/10.1097/SIH.0000000000000543

Cheung, J. J. H., Kulasegaram, K. M., Woods, N. N., Moulton, C., Ringsted, C. V., & Brydges, R. (2018). Knowing How and Knowing Why: Testing the effect of instruction designed for cognitive integration on procedural skills transfer. Advances in Health Sciences Education, 23 (1), 61–74. https://doi.org/10.1007/s10459-017-9774-1

Cook, D. A. (2005). The research we still are not doing: An agenda for the study of computer-based learning. Academic Medicine, 80 (6), 541–548. https://doi.org/10.1097/00001888-200506000-00005

Cook, D. A. (2009). The failure of e-learning research to inform educational practice, and what we can do about it. Medical Teacher, 31 (2), 158–162. https://doi.org/10.1080/01421590802691393

Durik, A. M., Shechter, O. G., Noh, M., Rozek, C. S., & Harackiewicz, J. M. (2015). What if I can’t? Success expectancies moderate the effects of utility value information on situational interest and performance. Motivation and Emotion, 39 (1), 104–118. https://doi.org/10.1007/s11031-014-9419-0

Ertmer, P. A., & Stepich, D. A. (2005). Instructional design expertise: How will we know it when we see it? Educational Technology, 45 (6), 38–43.

Google Scholar  

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28 (4), 717–741. https://doi.org/10.1007/s10648-015-9348-9

Friedman, C. P. (1994). The research we should be doing. Academic Medicine, 69 (6), 455–457. https://doi.org/10.1097/00001888-199406000-00005

Gavarkovs, A. G., Crukley, J., Miller, E., Kusurkar, R. A., Kulasegaram, K., & Brydges, R. (2023a). Effectiveness of life goal framing to motivate medical students during online learning: A randomized controlled trial. Perspectives on Medical Education, 12 (1), 444–454. https://doi.org/10.5334/pme.1017

Gavarkovs, A. G., Finan, E., Jensen, R. D., & Brydges, R. (2024). When I say … active learning. Medical Education . https://doi.org/10.1111/medu.15383

Gavarkovs, A. G., Kusurkar, R. A., & Brydges, R. (2023b). The purpose, adaptability, confidence, and engrossment model: A novel approach for supporting professional trainees’ motivation, engagement, and academic achievement. Frontiers in Education, 8 , 1036539. https://doi.org/10.3389/feduc.2023.1036539

Hardré, P. L., Ge, X., & Thomas, M. K. (2005). Toward a model of development for instructional design expertise. Educational Technology, 45 (1), 53–57.

Hatano, G. & Inagaki, I. (1986). Two courses of expertise. In Child Development and Education in Japan (pp. 262–272). W. H. Freeman.

Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). The Guilford Press.

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19 (4), 509–539. https://doi.org/10.1007/s10648-007-9054-3

Kusurkar, R. A. (2023). Self-determination theory in health professions education research and practice. In R. M. Ryan (Ed.), The oxford handbook of self-determination theory (pp. 665–683). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197600047.013.33

Chapter   Google Scholar  

Kusurkar, R. A., Croiset, G., & Ten Cate, OTh. J. (2011). Twelve tips to stimulate intrinsic motivation in students through autonomy-supportive classroom teaching derived from Self-Determination Theory. Medical Teacher, 33 (12), 978–982. https://doi.org/10.3109/0142159X.2011.599896

Laidley, T. L., & Braddock, C. H. (2000). Role of adult learning theory in evaluating and designing strategies for teaching residents in ambulatory settings. Advances in Health Sciences Education, 5 (1), 43–54. https://doi.org/10.1023/A:1009863211233

Lawson, A. P., & Mayer, R. E. (2021). Benefits of writing an explanation during pauses in multimedia lessons. Educational Psychology Review, 33 (4), 1859–1885. https://doi.org/10.1007/s10648-021-09594-w

Maheu-Cadotte, M.-A., Cossette, S., Dubé, V., Fontaine, G., Lavallée, A., Lavoie, P., Mailhot, T., & Deschênes, M.-F. (2021). Efficacy of serious games in healthcare professions education: A systematic review and meta-analysis. Simulation in Healthcare: THe Journal of the Society for Simulation in Healthcare, 16 (3), 199–212. https://doi.org/10.1097/SIH.0000000000000512

Mann, K. V. (2004). The role of educational theory in continuing medical education: Has it helped us? Journal of Continuing Education in the Health Professions, 24 (Supplement 1), S22–S30. https://doi.org/10.1002/chp.1340240505

Mayer, R. E. (2023). How to assess whether an instructional intervention has an effect on learning. Educational Psychology Review, 35 (2), 64. https://doi.org/10.1007/s10648-023-09783-9

Schoemann, A. M., Boulton, A. J., & Short, S. D. (2017). Determining power and sample size for simple and complex mediation models. Social Psychological and Personality Science, 8 (4), 379–386. https://doi.org/10.1177/1948550617715068

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and quasi-experimental designs for generalized causal inference . Houghton Mifflin.

Spencer, S. J., Zanna, M. P., & Fong, G. T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes. Journal of Personality and Social Psychology, 89 (6), 845–851. https://doi.org/10.1037/0022-3514.89.6.845

Stoffels, M., Torre, D. M., Sturgis, P., Koster, A. S., Westein, M. P. D., & Kusurkar, R. A. (2023). Steps and decisions involved when conducting structural equation modeling (SEM) analysis. Medical Teacher . https://doi.org/10.1080/0142159X.2023.2263233

Tai, A.-S., Lin, S.-H., Chu, Y.-C., Yu, T., Puhan, M. A., & VanderWeele, T. (2023). Causal mediation analysis with multiple time-varying mediators. Epidemiology, 34 (1), 8–19. https://doi.org/10.1097/EDE.0000000000001555

VanderWeele, T. J. (2012). Invited commentary: Structural equation models and epidemiologic analysis. American Journal of Epidemiology, 176 (7), 608–612. https://doi.org/10.1093/aje/kws213

VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction . Oxford University Press.

VanderWeele, T. J. (2016). Mediation analysis: A practitioner’s guide. Annual Review of Public Health, 37 (1), 17–32. https://doi.org/10.1146/annurev-publhealth-032315-021402

VanderWeele, T. J., & Knol, M. J. (2014). A tutorial on interaction. Epidemiologic Methods . https://doi.org/10.1515/em-2013-0005

Woods, N. N., Brooks, L. R., & Norman, G. R. (2007). It all make sense: Biomedical knowledge, causal connections and memory in the novice diagnostician. Advances in Health Sciences Education, 12 (4), 405–415. https://doi.org/10.1007/s10459-006-9055-x

Download references

Author information

Authors and affiliations.

Faculty of Medicine, University of British Columbia, City Square East Tower, 555 W 12th Ave, Suite 200, Vancouver, BC, V5Z 3X7, Canada

Adam G. Gavarkovs

Research in Education, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1118, Amsterdam, The Netherlands

Rashmi A. Kusurkar

LEARN! Research Institute for Learning and Education, Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, The Netherlands

Amsterdam Public Health, Quality of Care, Amsterdam, The Netherlands

Department of Family and Community Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Kulamakan Kulasegaram

The Wilson Centre, University of Toronto/University Health Network, Toronto, ON, Canada

Kulamakan Kulasegaram & Ryan Brydges

Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Ryan Brydges

You can also search for this author in PubMed   Google Scholar

Contributions

A.G. conceptualized the topic of the manuscript and wrote the first draft. R.K., K.K., and R.B. provided contributions to subsequent drafts of the manuscript. All authors reviewed the final version of the manuscript.

Corresponding author

Correspondence to Adam G. Gavarkovs .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Gavarkovs, A.G., Kusurkar, R.A., Kulasegaram, K. et al. Going beyond the comparison: toward experimental instructional design research with impact. Adv in Health Sci Educ (2024). https://doi.org/10.1007/s10459-024-10365-9

Download citation

Received : 06 March 2024

Accepted : 05 August 2024

Published : 28 August 2024

DOI : https://doi.org/10.1007/s10459-024-10365-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Randomized controlled trial
• Quantitative data analysis
• Learning theory
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 31 August 2024

Effects of pecha kucha presentation pedagogy on nursing students’ presentation skills: a quasi-experimental study in Tanzania

• Setberth Jonas Haramba 1 ,
• Walter C. Millanzi 1 &
• Saada A. Seif 2  

BMC Medical Education volume  24 , Article number:  952 ( 2024 ) Cite this article

1 Altmetric

Metrics details

Introduction

Ineffective and non-interactive learning among nursing students limits opportunities for students’ classroom presentation skills, creativity, and innovation upon completion of their classroom learning activities. Pecha Kucha presentation is the new promising pedagogy that engages students in learning and improves students’ speaking skills and other survival skills. It involves the use of 20 slides, each covering 20 seconds of its presentation. The current study examined the effect of Pecha Kucha’s presentation pedagogy on presentation skills among nursing students in Tanzania.

The aim of this study was to establish comparative nursing student’s presentation skills between exposure to the traditional PowerPoint presentations and Pecha Kucha presentations.

The study employed an uncontrolled quasi-experimental design (pre-post) using a quantitative research approach among 230 randomly selected nursing students at the respective training institution. An interviewer-administered structured questionnaire adopted from previous studies to measure presentation skills between June and July 2023 was used. The study involved the training of research assistants, pre-assessment of presentation skills, training of participants, assigning topics to participants, classroom presentations, and post-intervention assessment. A linear regression analysis model was used to determine the effect of the intervention on nursing students’ presentation skills using Statistical Package for Social Solution (SPSS) version 26, set at a 95% confidence interval and 5% significance level.

Findings revealed that 63 (70.87%) participants were aged ≤ 23 years, of which 151 (65.65%) and 189 (82.17%) of them were males and undergraduate students, respectively. Post-test findings showed a significant mean score change in participants’ presentation skills between baseline (M = 4.07 ± SD = 0.56) and end-line (M = 4.54 ± SD = 0.59) that accounted for 0.4717 ± 0.7793; p  < .0001(95%CI) presentation skills mean score change with a medium effect size of 0.78. An increase in participants’ knowledge of Pecha Kucha presentation was associated with a 0.0239 ( p  < .0001) increase in presentation skills.

Pecha Kucha presentations have a significant effect on nursing students’ presentation skills as they enhance inquiry and mastery of their learning content before classroom presentations. The pedagogical approach appeared to enhance nursing students’ confidence during the classroom presentation. Therefore, there is a need to incorporate Pecha Kucha presentation pedagogy into nursing curricula and nursing education at large to promote student-centered teaching and learning activities and the development of survival skills.

Trial registration

It was not applicable as it was a quasi-experimental study.

Peer Review reports

The nursing students need to have different skills acquired during the learning process in order to enable them to provide quality nursing care and management in the society [ 1 ]. The referred nursing care and management practices include identifying, analyzing, synthesizing, and effective communication within and between healthcare professionals [ 1 ]. Given an increasing global economy and international competition for jobs and opportunities, the current traditional classroom learning methods are insufficient to meet such 21st - century challenges and demands [ 2 ]. The integration of presentation skills, creativity, innovation, collaboration, information, and media literacy skills helps to overcome the noted challenges among students [ 2 , 3 , 4 ]. The skills in question constitute the survival skills that help the students not only for career development and success but also for their personal, social and public quality of life as they enable students to overcome 21st challenges upon graduation [ 2 ].

To enhance the nursing students’ participation in learning, stimulating their presentation skills, critical thinking, creativity, and innovation, a combination of teaching and learning pedagogy should be employed [ 5 , 6 , 7 , 8 ]. Among others, classroom presentations, group discussions, problem-based learning, demonstrations, reflection, and role-play are commonly used for those purposes [ 5 ]. However, ineffective and non-interactive learning which contribute to limited presentation skills, creativity, and innovation, have been reported by several scholars [ 9 , 10 , 11 ]. For example, poor use and design of student PowerPoint presentations led to confusing graphics due to the many texts in the slides and the reading of about 80 slides [ 12 , 13 , 14 ]. Indeed, such non-interactive learning becomes boring and tiresome among the learners, and it is usually evidenced by glazing eyes, long yawning, occasional snoring, the use of a phone and frequent trips to the bathroom [ 12 , 14 ].

With an increasing number of nursing students in higher education institutions in Tanzania, the students’ traditional presentation pedagogy is insufficient to stimulate their presentation skills. They limit nursing student innovation, creativity, critical thinking, and meaningful learning in an attempt to solve health challenges [ 15 , 16 ].These hinder nursing students ability to communicate effectively by being able to demonstrate their knowledge and mastery of learning content [ 17 , 18 ]. Furthermore, it affects their future careers by not being able to demonstrate and express their expertise clearly in a variety of workplace settings, such as being able to present at scientific conferences, participating in job interviews, giving clinic case reports, handover reports, and giving feedback to clients [ 17 , 18 , 19 ].

Pecha Kucha presentation is a new promising approach for students’ learning in the classroom context as it motivates learners’ self-directed and collaborative learning, learner creativity, and presentation skills [ 20 , 21 , 22 ]. It encourages students to read more materials, enhances cooperative learning among learners, and is interesting and enjoyable among students [ 23 ].

Pecha Kucha presentation originated from the Japanese word “ chit chat , ” which represents the fast-paced presentation used in different fields, including teaching, marketing, advertising, and designing [ 24 , 25 , 26 ]. It involves 20 slides, where each slide covers 20 s, thus making a total of 6 min and 40 s for the whole presentation [ 22 ]. For effective learning through Pecha Kucha presentations, the design and format of the presentation should be meaningfully limited to 20 slides and targeted at 20 s for each slide, rich in content of the presented topic using high-quality images or pictures attuned to the content knowledge and message to be delivered to the target audiences [ 14 , 16 ]. Each slide should contain a primordial message with well-balanced information. In other words, the message should be simple in the sense that each slide should contain only one concept or idea with neither too much nor too little information, thus making it easy to be grasped by the audience [ 14 , 17 , 19 ].

The “true spirit” of Pecha Kucha is that it mostly consists of powerful images and meaningful specific text rather than the text that is being read by the presenter from the slides, an image, and short phrases that should communicate the core idea while the speaker offers well-rehearsed and elaborated comments [ 22 , 28 ]. The presenter should master the subject matter and incorporate the necessary information from classwork [ 14 , 20 ]. The audience’s engagement in learning by paying attention and actively listening to the Pecha Kucha presentation was higher compared with that in traditional PowerPoint presentations [ 29 ]. The creativity and collaboration during designing and selecting the appropriate images and contents, rehearsal before the presentation, and discussion after each presentation made students satisfied by enjoying Pecha Kucha presentations compared with traditional presentations [ 21 , 22 ]. Time management and students’ self-regulation were found to be significant through the Pecha Kucha presentation among the students and teachers or instructors who could appropriately plan the time for classroom instruction [ 22 , 23 ].

However, little is known about Pecha Kucha presentation in nursing education in Sub-Saharan African countries, including Tanzania, since there is insufficient evidence for the research(s) that have been published on the description of its effects on enhancing students’ presentation skills. Thus, this study assessed the effect of Pecha Kucha’s presentation pedagogy on enhancing presentation skills among nursing students. In particular, the study largely focused on nursing students’ presentation skills during the preparation and presentation of the students’ assignments, project works, case reports, or field reports.

The study answered the null hypothesis H 0  = H 1, which hypothesized that there is no significant difference in nursing students’ classroom presentation skills scores between the baseline and end-line assessments. The association between nursing students’ presentation skills and participants’ sociodemographic characteristics was formulated and analyzed before and after the intervention. This study forms the basis for developing new presentation pedagogy among nursing students in order to stimulate effective learning and the development of presentation skills during the teaching and learning process and the acquisition of 21st - century skills, which are characterized by an increased competitive knowledge-based society due to changing nature and technological eruptions.

The current study also forms the basis for re-defining classroom practices in an attempt to enhance and transform nursing students’ learning experiences. This will cultivate the production of graduates nurses who will share their expertise and practical skills in the health care team by attending scientific conferences, clinical case presentations, and job interviews in the global health market. To achieve this, the study determined the baseline and end-line nursing students’ presentation skills during the preparation and presentation of classroom assignments using the traditional PowerPoint presentation and Pecha Kucha presentation format.

Methods and materials

This study was conducted in health training institutions in Tanzania. Tanzania has a total of 47 registered public and private universities and university colleges that offer health programs ranging from certificate to doctorate degrees [ 24 , 25 ]. A total of seven [ 7 ] out of 47 universities offer a bachelor of science in nursing, and four [ 4 ] universities offer master’s to doctorate degree programs in nursing and midwifery sciences [ 24 , 26 ]. To enhance the representation of nursing students in Tanzania, this study was conducted in Dodoma Municipal Council, which is one of Tanzania’s 30 administrative regions [ 33 ]. Dodoma Region has two [ 2 ] universities that offer nursing programs at diploma and degree levels [ 34 ]. The referred universities host a large number of nursing students compared to the other five [ 5 ] universities in Tanzania, with traditional students’ presentation approaches predominating nursing students’ teaching and learning processes [ 7 , 32 , 35 ].

The two universities under study include the University of Dodoma and St. John’s University of Tanzania, which are located in Dodoma Urban District. The University of Dodoma is a public university that provides 142 training programs at the diploma, bachelor degree, and master’s degree levels with about 28,225 undergraduate students and 724 postgraduate students [ 26 , 27 ]. The University of Dodoma also has 1,031 nursing students pursuing a Bachelor of Science in Nursing and 335 nursing students pursuing a Diploma in Nursing in the academic year 2022–2023 [ 33 ]. The St. John’s University of Tanzania is a non-profit private university that is legally connected with the Christian-Anglican Church [ 36 ]. It has student enrollment ranging from 5000 to 5999 and it provides training programs leading to higher education degrees in a variety of fields, including diplomas, bachelor degrees, and master’s degrees [ 37 ]. It hosts 766 nursing students pursuing a Bachelor of Science in Nursing and 113 nursing students pursuing a Diploma in Nursing in the academic year 2022–2023 [ 30 , 31 ].

Study design and approach

An uncontrolled quasi-experimental design with a quantitative research approach was used to establish quantifiable data on the participants’ socio-demographic profiles and outcome variables under study. The design involved pre- and post-tests to determine the effects of the intervention on the aforementioned outcome variable. The design involved three phases, namely the baseline data collection process (pre-test via a cross-sectional survey), implementation of the intervention (process), and end-line assessment (post-test), as shown in Fig.  1 [ 7 ].

A flow pattern of study design and approach

Target population

The study involved nursing students pursuing a Diploma in nursing and a bachelor of science in nursing in Tanzania. The population was highly expected to demonstrate competences and mastery of different survival and life skills in order to enable them to work independent at various levels of health facilities within and outside Tanzania. This cohort of undergraduate nursing students also involved adult learners who can set goals, develop strategies to achieve their goals, and hence achieve positive professional behavioral outcomes [ 7 ]. Moreover, as per annual data, the average number of graduate nursing students ranges from 3,500 to 4,000 from all colleges and universities in the country [ 38 ].

Study population

The study involved first- and third-year nursing students pursuing a Diploma in Nursing and first-, second-, and third-year nursing students pursuing a Bachelor of Science in Nursing at the University of Dodoma. The population had a large number of enrolled undergraduate nursing students, thus making it an ideal population for intervention, and it approximately served as a good representation of the universities offering nursing programs [ 11 , 29 ].

Inclusion criteria

The study included male and female nursing students pursuing a Diploma in nursing and a bachelor of science in nursing at the University of Dodoma. The referred students included those who were registered at the University of Dodoma during the time of study. Such students live on or off campus, and they were not exposed to PK training despite having regular classroom attendance. This enhanced enrollment of adequate study samples from each study program, monitoring of study intervention, and easy control of con-founders.

Exclusion criteria

All students recruited in the study were assessed at baseline, exposed to a training package and obtained their post-intervention learning experience. None of the study participants, who either dropped out of the study or failed to meet the recruitment criteria.

Sample size determination

A quasi-experimental study on Pecha Kucha as an alternative to traditional PowerPoint presentations at Worcester University, United States of America, reported significant student engagement during Pecha Kucha presentations compared with traditional PowerPoint presentations [ 29 ]. The mean score for the classroom with the traditional PowerPoint presentation was 2.63, while the mean score for the Pecha Kucha presentation was 4.08. This study adopted the formula that was used to calculate the required sample size for an uncontrolled quasi-experimental study among pre-scholars [ 39 ]. The formula is stated as:

Where: Zα was set at 1.96 from the normal distribution table.

Zβ was set at 0.80 power of the study.

Mean zero (π0) was the mean score of audiences’ engagement in using PowerPoint presentation = 2.63.

Mean one (π1) was the mean score of audience’s engagement in using Pecha Kucha presentation = 4.08.

Sampling technique

Given the availability of higher-training institutions in the study area that offer undergraduate nursing programs, a simple random sampling technique was used, whereby two cards, one labelled “University of Dodoma” and the other being labelled “St. Johns University of Tanzania,” were prepared and put in the first pot. The other two cards, one labelled “yes” to represent the study setting and the other being labelled “No” to represent the absence of study setting, were put in the second pot. Two research assistants were asked to select a card from each pot, and consequently, the University of Dodoma was selected as the study setting.

To obtain the target population, the study employed purposive sampling techniques to select the school of nursing and public health at the University of Dodoma. Upon arriving at the School of Nursing and Public Health of the University of Dodoma, the convenience sampling technique was employed to obtain the number of classes for undergraduate nursing students pursuing a Diploma in Nursing and a Bachelor of Science in Nursing. The study sample comprised the students who were available at the time of study. A total of five [ 5 ] classes of Diploma in Nursing first-, second-, and third-years and Bachelor of Science in Nursing first-, second-, and third-years were obtained.

To establish the representation for a minimum sample from each class, the number of students by sex was obtained from each classroom list using the proportionate stratified sampling technique (sample size/population size× stratum size) as recommended by scholars [ 40 ]. To recruit the required sample size from each class by gender, a simple random sampling technique through the lottery method was employed to obtain the required sample size from each stratum. During this phase, the student lists by gender from each class were obtained, and cards with code numbers, which were mixed with empty cards depending on the strata size, were allocated for each class and strata. Both labeled and empty cards were put into different pots, which were labeled appropriately by their class and strata names. Upon arriving at the specific classroom and after the introduction, the research assistant asked each nursing student to pick one card from the respective strata pot. Those who selected cards with code numbers were recruited in the study with their code numbers as their participation identity numbers. The process continued for each class until the required sample size was obtained.

To ensure the effective participation of nursing students in the study, the research assistant worked hand in hand with the facilitators and lecturers of the respective classrooms, the head of the department, and class representatives. The importance, advantages, and disadvantages of participating in the study were given to study participants during the recruitment process in order to create awareness and remove possible fears. During the intervention, study participants were also given pens and notebooks in an attempt to enable them to take notes. Moreover, the bites were provided during the training sessions. The number of participants from each classroom and the sampling process are shown in Fig.  2 [ 7 ].

Flow pattern of participants sampling procedures

Data collection tools

The study adapted and modified the students’ questionnaire on presentation skills from scholars [ 20 , 23 , 26 , 27 , 28 , 29 ]. The modification involved rephrasing the question statement, breaking down items into specific questions, deleting repeated items that were found to measure the same variables, and improving language to meet the literacy level and cultural norms of study participants.

The data collection tool consisted of 68 question items that assessed the socio-demographic characteristics of the study participants and 33 question items rated on a five-point Likert scale, which ranges from 5 = strongly agree, 4 = agree, 3 = not sure, 2 = disagree, and 1 = strongly disagree. The referred tool was used to assess the students’ skills during the preparation and presentation of the assignments using the traditional PowerPoint presentation and Pecha Kucha presentation formats.

The students’ assessment specifically focused on the students’ ability to prepare the presentation content, master the learning content, share presentation materials, and communicate their understanding to audiences in the classroom context.

Validity and reliability of research instruments

Validity of the research instrument refers to whether the instrument measures the behaviors or qualities that are intended to be measured, and it is a measure of how well the measuring instrument performs its function [ 41 ]. The structured questionnaire, which intends to assess the participants’ presentation skills was validated for face and content validity. The principal investigator initially adapted the question items for different domains of students’ learning when preparing and presenting their assignment in the classroom.

The items were shared and discussed by two [ 2 ] educationists, two [ 2 ] research experts, one [ 1 ] statistician, and supervisors in order to ensure clarity, appropriateness, adequacy, and coverage of the presentation skills using Pecha Kucha presentation format. The content validity test was used until the saturation of experts’ opinions and inputs was achieved. The inter-observer rating scale on a five-point Likert scale ranging from 5-points = very relevant to 1-point = not relevant was also used.

The process involved addition, input deletion, correction, and editing for relevance, appropriateness, and scope of the content for the study participants. Some of the question items were broken down into more specific questions, and new domains evolved. Other question items that were found to measure the same variables were also deleted to ease the data collection and analysis. Moreover, the grammar and language issues were improved for clarity based on the literacy level of the study participants.

Reliability of the research instruments refers to the ability of the research instruments or tools to provide similar and consistent results when applied at different times and circumstances [ 41 ]. This study adapted the tools and question items used by different scholars to assess the impact of PKP on student learning [ 12 , 15 , 18 ].

To ensure the reliability of the tools, a pilot study was conducted in one of the nursing training institutions in order to assess the complexity, readability, clarity, completeness, length, and duration of the tool. Ambiguous and difficult (left unanswered) items were modified or deleted based on the consensus that was reached with the consulted experts and supervisor before subjecting the questionnaires to a pre-test.

The study involved 10% of undergraduate nursing students from an independent geographical location for a pilot study. The findings from the pilot study were subjected to explanatory factor analysis (Set a ≥ 0.3) and scale analysis in order to determine the internal consistency of the tools using the Cronbach alpha of ≥ 0.7, which was considered reliable [ 42 , 43 , 44 ]. Furthermore, after the data collection, the scale analysis was computed in an attempt to assess their internal consistency using SPPSS version 26, whereby the Cronbach alpha for question items that assessed the participants’ presentation skills was 0.965.

Data collection method

The study used the researcher-administered questionnaire to collect the participants’ socio-demographic information, co-related factors, and presentation skills as nursing students prepare and present their assignments in the classroom. This enhanced the clarity and participants’ understanding of all question items before providing the appropriate responses. The data were collected by the research assistants in the classroom with the study participants sitting distantly to ensure privacy, confidentiality, and the quality of the information that was provided by the research participants. The research assistant guided and led the study participants to answer the questions and fill in information in the questionnaire for each section, domain, and question item. The research assistant also collected the baseline information (pre-test) before the intervention, which was then compared with the post-intervention information. This was done in the first week of June 2023, after training and orientation of the research assistant on the data collection tools and recruitment of the study participants.

Using the researcher-administered questionnaire, the research assistant also collected the participants’ information related to presentation skills as they prepared and presented their given assignments after the intervention during the second week of July 2023. The participants submitted their presentations to the principle investigator and research assistant to assess the organization, visual appeal and creativity, content knowledge, and adherence to Pecha Kucha presentation requirements. Furthermore, the evaluation of the participants’ ability to share and communicate the given assignment was observed in the classroom presentation using the Pecha Kucha presentation format.

Definitions of variables

Pecha kucha presentation.

It refers to a specific style of presentation whereby the presenter delivers the content using 20 slides that are dominated by images, pictures, tables, or figures. Each slide is displayed for 20 s, thus making a total of 400 s (6 min and 40 s) for the whole presentation.

Presentation skills in this study

This involved students’ ability to plan, prepare, master learning content, create presentation materials, and share them with peers or the audience in the classroom. They constitute the learning activities that stimulate creativity, innovation, critical thinking, and problem-solving skills.

Measurement of pecha kucha preparation and presentation skills

The students’ presentation skills were measured using the four [ 4 ] learning domains. The first domain constituted the students’ ability to plan and prepare the presentation content. It consisted of 17 question items that assessed the students’ ability to gather and select information, search for specific content to be presented in the classroom, find out the learning content from different resources, and search for literature materials for the preparation of the assignment using traditional PowerPoint presentations and Pecha Kucha formats. It also aimed to ascertain a deeper understanding of the contents or topic, learning ownership and motivation to learn the topics with clear understanding and the ability to identify the relevant audience, segregate, and remove unnecessary contents using the Pecha Kucha format.

The second domain constituted the students’ mastery of learning during the preparation and presentation of their assignment before the audience in the classroom. It consisted of six [ 6 ] question items that measured the students’ ability to read several times, rehearse before the classroom presentation, and practice the assignment and presentation harder. It also measures the students’ ability to evaluate the selected information and content before their actual presentation and make revisions to the selected information and content before the presentation using the Pecha Kucha format.

The third domain constituted the students’ ability to prepare the presentation materials. It consisted of six [ 6 ] question items that measured the students’ ability to organize the information and contents, prepare the classroom presentation, revise and edit presentation resources, materials, and contents, and think about the audience and classroom design. The fourth domain constituted the students’ ability to share their learning. It consisted of four [ 4 ] question items that measured the students’ ability to communicate their learning with the audience, present a new understanding to the audience, transfer the learning to the audience, and answer the questions about the topic or assignment given. The variable was measured using a 5-point Likert scale. The average scores were computed for each domain, and an overall mean score was calculated across all domains. Additionally, an encompassing skills score was derived from the cumulative scores of all four domains, thus providing a comprehensive evaluation of the overall skills level.

Implementation of intervention

The implementation of the study involved the training of research assistants, sampling of the study participants, setting of the venue, pre-assessment of the students’ presentation skills using traditional PowerPoint presentations, training and demonstration of Pecha Kucha presentations to study participants, and assigning the topics to study participants. The implementation of the study also involved the participants’ submission of their assignments to the Principal Investigator for evaluation, the participants’ presentation of their assigned topic using the Pecha Kucha format, post-intervention assessment of the students’ presentation skills, data analysis, and reporting [ 7 ]. The intervention involved Principal Investigator and two [ 2 ] trained research assistants. The intervention in question was based on the concept of multimedia theory of cognitive learning (MTCL) for enhancing effective leaning in 21st century.

Training of research assistants

Two research assistants were trained with regard to the principles, characteristics, and format of Pecha Kucha presentations using the curriculum from the official Pecha Kucha website. Also, research assistants were oriented to the data collection tools and methods in an attempt to guarantee the relevancy and appropriate collection of the participants’ information.

Schedule and duration of training among research assistants

The PI prepared the training schedule and venue after negotiation and consensus with the research assistants. Moreover, the Principle Investigator trained the research assistants to assess the learning, learn how to collect the data using the questionnaire, and maintain the privacy and confidentiality of the study participants.

Descriptions of interventions

The intervention was conducted among the nursing students at the University of Dodoma, which is located in Dodoma Region, Tanzania Mainland, after obtaining their consent. The participants were trained regarding the concepts, principles, and characteristics of Pecha Kucha presentations and how to prepare and present their assignments using the Pecha Kucha presentation format. The study participants were also trained regarding the advantages and disadvantages of Pecha Kucha presentations. The training was accompanied by one example of an ideal Pecha Kucha presentation on the concepts of pressure ulcers. The teaching methods included lecturing, brainstorming, and small group discussion. After the training session, the evaluation was conducted to assess the participants’ understanding of the Pecha Kucha conceptualization, its characteristics, and its principles.

Each participant was given a topic as an assignment from the fundamentals of nursing, medical nursing, surgical nursing, community health nursing, mental health nursing, emergency critical care, pediatric, reproductive, and child health, midwifery, communicable diseases, non-communicable diseases, orthopedics and cross-cutting issues in nursing as recommended by scholars [ 21 , 38 ]. The study participants were given 14 days for preparation, rehearsal of their presentation using the Pecha Kucha presentation format, and submission of the prepared slides to the research assistant and principle investigator for evaluation and arrangement before the actual classroom presentation. The evaluation of the participants’ assignments involved the number of slides, quality of images used, number of words, organization of content and messages to be delivered, slide transition, duration of presentation, flow, and organization of slides.

Afterwards, each participant was given 6 min and 40 s for the presentation and 5 min to 10 min for answering the questions on the topic presented as raised by other participants. An average of 4 participants obtained the opportunity to present their assignments in the classroom every hour. After the completion of all presentations, the research assistants assessed the participant’s presentation skills using the researcher-administered questionnaire. The collected data were entered in SPSS version 26 and analyzed in an attempt to compare the mean score of participants’ presentation skills with the baseline mean score. The intervention sessions were conducted in the selected classrooms, which were able to accommodate all participants at the time that was arranged by the participant’s coordinators, institution administrators, and subject facilitators of the University of Dodoma, as described in Table  1 [ 7 ].

Evaluation of intervention

During the classroom presentation, there were 5 to 10 min for classroom discussion and reflection on the content presented, which was guided by the research assistant. During this time, the participants were given the opportunity to ask the questions, get clarification from the presenter, and provide their opinion on how the instructional messages were presented, content coverage, areas of strength and weakness for improvement, and academic growth. After the completion of the presentation sessions, the research assistant provided the questionnaire to participants in order to determine their presentation skills during the preparation of their assignments and classroom presentations using the Pecha Kucha presentation format.

Data analysis

The findings from this study were analyzed using the Statistical Package for Social Science (SPSS) computer software program version 26. The percentages, frequencies, frequency distributions, means, standard deviations, skewness, and kurtosis were calculated, and the results were presented using the figures, tables, and graphs. The mean score analysis was computed, and descriptive statistical analysis was used to analyze the demographic information of the participants in an attempt to determine the frequencies, percentages, and mean scores of their distributions. A paired sample t-test was used to compare the mean score differences of the presentation skills within the groups before and after the intervention. The mean score differences were determined based on the baseline scores against the post-intervention scores in order to establish any change in terms of presentation skills among the study participants.

The association between the Pecha Kucha presentation and the development of participants’ presentation skills was established using linear regression analysis set at a 95% confidence interval and 5% (≤ 0.05) significance level in an attempt to accept or reject the null hypothesis.

However, N-1 dummy variables were formed for the categorical independent variables so as to run the linear regression for the factors associated with the presentation skills. The linear regression equation with dummy variables is presented as follows:

Β 0 is the intercept.

Β 1 , Β 2 , …. Β k-1 are the coefficients which correspond to the dummy variables representing the levels of X 1 .

Β k is the coefficient which corresponds to the dummy variable representing the levels of X 2 .

Β k+1 is the coefficient which corresponds to the continuous predictor X 3 .

X 1,1 , X 1,2 ,……. X 1,k-1 are the dummy variables corresponding to the different levels of X 1 .

ε represents the error term.

The coefficients B1, B2… Bk indicate the change in the expected value of Y for each category relative to the reference category. If the Beta estimate is positive for the categorical or dummy variables, it means that the corresponding covariate has a positive impact on the outcome variable compared to reference category. However, if the beta estimate is positive for the case of continuous covariates, it means that the corresponding covariate has direct proportion effect on the outcome variables.

The distribution of the outcome variables was approximately normally distributed since the normality of the data is one of the requirements for parametric analysis. A paired t test was performed to compare the presentation skills of nursing students before and after the intervention.

Social-demographic characteristics of the study participants

The study involved a total of 230 nursing students, of whom 151 (65.65%) were male and the rest were female. The mean age of study participants was 23.03 ± 2.69, with the minimum age being 19 and the maximum age being 37. The total of 163 (70.87%) students, which comprised a large proportion of respondents, were aged less than or equal to 23, 215 (93.48%) participants were living on campus, and 216 (93.91) participants were exposed to social media.

A large number of study participants (82.17%) were pursuing a bachelor of Science in Nursing, with the majority being first-year students (30.87%). The total of 213 (92.61%) study participants had Form Six education as their entry qualification, with 176 (76.52%) participants being the product of public secondary schools and interested in the nursing profession. Lastly, the total of 121 (52.61%) study participants had never been exposed to any presentation training; 215 (93.48%) students had access to individual classroom presentations; and 227 (98.70%) study participants had access to group presentations during their learning process. The detailed findings for the participants’ social demographic information are indicated in Table  2 [ 46 ].

Baseline nursing students’ presentation skills using traditional powerPoint presentations

The current study assessed the participant’s presentation skills when preparing and presenting the materials before the audience using traditional PowerPoint presentations. The study revealed that the overall mean score of the participants’ presentation skills was 4.07 ± 0.56, including a mean score of 3.98 ± 0.62 for the participants’ presentation skills during the preparation of presentation content before the classroom presentation and a mean score of 4.18 ± 0.78 for the participants’ mastery of learning content before the classroom presentation. Moreover, the study revealed a mean score of 4.07 ± 0.71 for participants’ ability to prepare presentation materials for classroom presentations and a mean score of 4.04 ± 0.76 for participants’ ability to share the presentation materials in the classroom, as indicated in Table  3 [ 46 ].

Factors Associated with participants’ presentation skills through traditional powerPoint presentation

The current study revealed that the participants’ study program has a significant effect on their presentation skills, whereby being the bachelor of science in nursing was associated with a 0.37561 (P value < 0.027) increase in the participants’ presentation skills.The year of study also had significant effects on the participants’ presentation skills, whereby being a second-year bachelor student was associated with a 0.34771 (P value < 0.0022) increase in the participants’ presentation skills compared to first-year bachelor students and diploma students. Depending on loans as a source of student income retards presentation skills by 0.24663 (P value < 0.0272) compared to those who do not depend on loans as the source of income. Furthermore, exposure to individual presentations has significant effects on the participants’ presentation skills, whereby obtaining an opportunity for individual presentations was associated with a 0.33732 (P value 0.0272) increase in presentation skills through traditional PowerPoint presentations as shown in Table  4 [ 46 ].

Nursing student presentation skills through pecha kucha presentations

The current study assessed the participant’s presentation skills when preparing and presenting the materials before the audience using Pecha Kucha presentations. The study revealed that the overall mean score and standard deviation of participants’ presentation skills using the Pecha Kucha presentation format were 4.54 ± 0.59, including a mean score of 4.49 ± 0.66 for participant’s presentation skills during preparation of the content before classroom presentation and a mean score of 4.58 ± 0.65 for participants’ mastery of learning content before classroom presentation. Moreover, the study revealed a mean score of 4.58 ± 0.67 for participants ability to prepare the presentation materials for classroom presentation and a mean score of 4.51 ± 0.72 for participants ability to share the presentation materials in the classroom using Pecha Kucha presentation format as indicated in Table  5 [ 46 ].

Comparing Mean scores of participants’ presentation skills between traditional PowerPoint presentation and pecha kucha Presentation

The current study computed a paired t-test to compare and determine the mean change, effect size, and significance associated with the participants’ presentation skills when using the traditional PowerPoint presentation and Pecha Kucha presentation formats. The study revealed that the mean score of the participants’ presentation skills through the Pecha Kucha presentation was 4.54 ± 0.59 (p value < 0.0001) compared to the mean score of 4.07 ± 0.56 for the participants’ presentation skills using the traditional power point presentation with an effect change of 0.78. With regard to the presentation skills during the preparation of presentation content before the classroom presentation, the mean score was 4.49 ± 0.66 using the Pecha Kucha presentation compared to the mean score of 3.98 ± 0.62 for the traditional PowerPoint presentation. Its mean change was 0.51 ± 0.84 ( p  < .0001) with an effect size of 0.61.

Regarding the participants’ mastery of learning content before the classroom presentation, the mean score was 4.58 ± 0.65 when using the Pecha Kucha presentation format, compared to the mean score of 4.18 ± 0.78 when using the traditional power point presentation. Its mean change was 0.40 ± 0.27 ( p  < .0001) with an effect size of 1.48. Regarding the ability of the participants to prepare the presentation materials for classroom presentations, the mean score was 4.58 ± 0.67 when using the Pecha Kucha presentation format, compared to 4.07 ± 0.71 when using the traditional PowerPoint presentation. Its mean change was 0.51 ± 0.96 ( p  < .0001) with an effect size of 0.53.

Regarding the participants’ presentation skills when sharing the presentation material in the classroom, the mean score was 4.51 ± 0.72 when using the Pecha Kucha presentation format, compared to 4.04 ± 0.76 when using the traditional PowerPoint presentations. Its mean change was 0.47 ± 0.10, with a large effect size of 4.7. Therefore, Pecha Kucha presentation pedagogy has a significant effect on the participants’ presentation skills than the traditional PowerPoint presentation as shown in Table  6 [ 46 ].

Factors associated with presentation skills among nursing students through pecha kucha presentation

The current study revealed that the participant’s presentation skills using the Pecha Kucha presentation format were significantly associated with knowledge of the Pecha Kucha presentation format, whereby increase in knowledge was associated with a 0.0239 ( p  < .0001) increase in presentation skills. Moreover, the current study revealed that the presentation through the Pecha Kucha presentation format was not influenced by the year of study, whereby being a second-year student could retard the presentation skills by 0.23093 (p 0.039) compared to a traditional PowerPoint presentation. Other factors are shown in Table  7 [ 46 ].

Social-demographic characteristics profiles of participants

The proportion of male participants was larger than the proportion of female participants in the current study. This was attributable to the distribution of sex across the nursing students at the university understudy, whose number of male nursing students enrolled was higher than female students. This demonstrates the high rate of male nursing students’ enrolment in higher training institutions to pursue nursing and midwifery education programs. Different from the previous years, the nursing training institutions were predominantly comprised of female students and female nurses in different settings. This significant increase in male nursing students’ enrollment in nursing training institutions predicts a significant increase in the male nursing workforce in the future in different settings.

These findings on Pecha Kucha as an alternative to PowerPoint presentations in Massachusetts, where the proportion of female participants was large as compared to male participants, are different from the experimental study among English language students [ 29 ]. The referred findings are different from the results of the randomized control study among the nursing students in Anakara, Turkey, where a large proportion of participants were female nursing students [ 47 ]. This difference in participants’ sex may be associated with the difference in socio-cultural beliefs of the study settings, country’s socio-economic status, which influence the participants to join the nursing profession on the basis of securing employment easily, an opportunity abroad, or pressure from peers and parents. Nevertheless, such differences account for the decreased stereotypes towards male nurses in the community and the better performance of male students in science subjects compared to female students in the country.

The mean age of the study participants was predominantly young adults with advanced secondary education. Their ages reflect adherence to national education policy by considering the appropriate age of enrollment of the pupils in primary and secondary schools, which comprise the industries for students at higher training institutions. This age range of the participants in the current study suits the cognitive capability expected from the participants in order to demonstrate different survival and life skills by being able to set learning goals and develop strategies to achieve their goals according to Jean Piaget’s theory of cognitive learning [ 41 , 42 ].

Similar age groups were noted in the study among nursing students in a randomized control study in Anakara Turkey where the average age was 19.05 ± 0.2 [ 47 ]. A similar age group was also found in a randomized control study among liberal arts students in Anakara, Turkey, on differences in instructor, presenter, and audience ratings of Pecha Kucha presentations and traditional student presentations where the ages of the participants ranged between 19 and 22 years [ 49 ].

Lastly, a large proportion of the study participants had the opportunity for individual and group presentations in the classroom despite having not been exposed to any presentation training before. This implies that the teaching and learning process in a nursing education program is participatory and student-centered, thus giving the students the opportunity to interact with learning contents, peers, experts, webpages, and other learning resources to become knowledgeable. These findings fit with the principle that guides and facilitates the student’s learning from peers and teachers according to the constructivism theory of learning by Lev Vygotsky [ 48 ].

Effects of pecha kucha presentation pedagogy on participants’ presentation skills

The participants’ presentation skills were higher for Pecha Kucha presentations compared with traditional PowerPoint presentations. This display of the Pecha Kucha presentation style enables the nursing students to prepare the learning content, master their learning content before classroom presentations, create good presentation materials and present the materials, before the audience in the classroom. This finding was similar to that at Padang State University, Indonesia, among first-year English and literature students whereby the Pecha Kucha Presentation format helped the students improve their skills in presentation [ 20 ]. Pecha Kucha was also found to facilitate careful selection of the topic, organization and outlining of the students’ ideas, selection of appropriate images, preparation of presentations, rehearsing, and delivery of the presentations before the audience in a qualitative study among English language students at the Private University of Manila, Philippines [ 23 ].

The current study found that Pecha Kucha presentations enable the students to perform literature searches from different webpages, journals, and books in an attempt to identify specific contents during the preparation of the classroom presentations more than traditional PowerPoint presentations. This is triggered by the ability of the presentation format to force the students to filter relevant and specific information to be included in the presentation and search for appropriate images, pictures, or figures to be presented before the audience. Pecha Kucha presentations were found to increase the ability to perform literature searches before classroom presentations compared to traditional PowerPoint presentations in an experimental study among English language students at Worcester State University [ 29 ].

The current study revealed that Pecha Kucha presentations enable the students to create a well-structured classroom presentation effectively by designing 20 meaningful and content-rich slides containing 20 images, pictures, or figures and a transitional flow of 20 s for each slide, more than the traditional PowerPoint presentation with an unlimited number of slides containing bullets with many texts or words. Similarly, in a cross-sectional study of medical students in India, Pecha Kucha presentations were found to help undergraduate first-year medical students learn how to organize knowledge in a sequential fashion [ 26 ].

The current study revealed that Pecha Kucha presentations enhance sound mastery of the learning contents and presentation materials before the classroom presentation compared with traditional PowerPoint presentations. This is hastened by the fact that there is no slide reading during the classroom Pecha Kucha presentation, thus forcing students to read several times, rehearse, and practice harder the presentation contents and materials before the classroom presentation. Pecha Kucha presentation needed first year English and literature students to practice a lot before their classroom presentation in a descriptive qualitative study at Padang State University-Indonesia [ 20 ].

The current study revealed that the participants became more confident in answering the questions about the topic during the classroom presentation using the Pecha Kucha presentation style than during the classroom presentation using the tradition PowerPoint presentation. This is precipitated by the mastery level of the presentation contents and materials through rehearsal, re-reading, and material synthesis before the classroom presentations. Moreover, Pecha Kucha was found to significantly increase the students’ confidence during classroom presentation and preparation in a qualitative study among English language students at the Private University of Manila, Philippines [ 23 ].

Hence, there was enough evidence to reject the null hypothesis in that there was no significant difference in nursing students’ presentation skills between the baseline and end line. The Pecha Kucha presentation format has a significant effect on nursing student’s classroom presentation skills as it enables them to prepare the learning content, have good mastery of the learning contents, create presentation materials, and confidently share their learning with the audience in the classroom.

The current study’s findings complement the available pieces of evidence on the effects of Pecha Kucha presentations on the students’ learning and development of survival life skills in the 21st century. Pecha kucha presentations have more significant effects on the students’ presentation skills compared with traditional PowerPoint presentations. It enables the students to select the topic carefully, organize and outline the presentation ideas, select appropriate images, create presentations, rehearse the presentations, and deliver them confidently before an audience. It also enables the students to select and organize the learning contents for classroom presentations more than traditional PowerPoint presentations.

Pecha Kucha presentations enhance the mastery of learning content by encouraging the students to read the content several times, rehearse, and practice hard before the actual classroom presentation. It increases the students’ ability to perform literature searches before the classroom presentation compared to a traditional PowerPoint presentation. Pecha Kucha presentations enable the students to create well-structured classroom presentations more effectively compared to traditional PowerPoint presentations. Furthermore, Pecha Kucha presentations make the students confident during the presentation of their assignments and project works before the audience and during answering the questions.

Lastly, Pecha Kucha presentations enhance creativity among the students by providing the opportunity for them to decide on the learning content to be presented. Specifically, they are able to select the learning content, appropriate images, pictures, or figures, organize and structure the presentation slides into a meaningful and transitional flow of ideas, rehearse and practice individually before the actual classroom presentation.

Strength of the study

This study has addressed the pedagogical gap in nursing training and education by providing new insights on the innovative students’ presentation format that engages students actively in their learning to bring about meaningful and effective students’ learning. It has also managed to recruit, asses, and provide intended intervention to 230 nursing students without dropout.

Study limitation

The current study has pointed out some of the strengths of the PechaKucha presentations on the students’ presentation skills over the traditional students’ presentations. However, the study had the following limitations: It involved one group of nursing students from one of the public training institutions in Tanzania. The use of one university may obscure the interpretation of the effects of the size of the intervention on the outcome variables of interest, thus limiting the generalization of the study findings to all training institutions in Tanzania. Therefore, the findings from this study need to be interpreted by considering this limitation. The use of one group of nursing students from one university to explore their learning experience through different presentation formats may also limit the generalization of the study findings to all nursing students in the country. The limited generalization may be attributed to differences in socio-demographic characteristics, learning environments, and teaching and learning approaches. Therefore, the findings from this study need to be interpreted by considering this limitation.

Suggestions for future research

The future research should try to overcome the current study limitations and shortcomings and extend the areas assessed by the study to different study settings and different characteristics of nursing students in Tanzania as follows: To test rigorously the effects of Pecha Kucha presentations in enhancing the nursing students’ learning, the future studies should involve nursing students’ different health training institutions rather than one training institution. Future studies should better use the control students by randomly allocating the nursing students or training institutions in the intervention group or control group in order to assess the students’ learning experiences through the use of Pecha Kucha presentations and PowerPoint presentations consecutively. Lastly, future studies should focus on nursing students’ mastery of content knowledge and students’ classroom performance through the use of the Pecha Kucha presentation format in the teaching and learning process.

Data availability

The datasets generated and analyzed by this study can be obtained from the corresponding author on reasonable request through [email protected] & [email protected].

Abbreviations

Doctor (PhD)

Multimedia Theory of Cognitive Learning

National Council for Technical and Vocational Education and Training

Principle Investigator

Pecha Kucha presentation

Statistical Package for Social Sciences

Tanzania Commission for Universities

World Health Organization

International Council of Nurses. Nursing Care Continuum Framework and Competencies. 2008.

Partnership for 21st Century Skills. 21st Century Skills, Education & Competitiveness. a Resour Policy Guid [Internet]. 2008;20. https://files.eric.ed.gov/fulltext/ED519337.pdf

Partnership for 21st Century Skills. 21St Century Knowledge and Skills in Educator Preparation. Education [Internet]. 2010;(September):40. https://files.eric.ed.gov/fulltext/ED519336.pdf

Partnership for 21st Century Skills. A State Leaders Action Guide to 21st Century Skills: A New Vision for Education. 2006; http://apcrsi.pt/website/wp-content/uploads/20170317_Partnership_for_21st_Century_Learning.pdf

World Health Organization. Four-Year Integrated Nursing And Midwifery Competency-Based Prototype Curriculum for the African Region [Internet]. Republic of South Africa: WHO Regional Office for Africa. 2016; 2016. 13 p. https://apps.who.int/iris/bitstream/handle/10665/331471/9789290232612-eng.pdf?sequence=1&isAllowed=y

World Health Organization, THREE-YEAR REGIONAL PROTOTYPE PRE-SERVICE COMPETENCY-BASED NURSING, CURRICULUM [Internet]. 2016. https://apps.who.int/iris/bitstream/handle/10665/331657/9789290232629-eng.pdf?sequence=1&isAllowed=y

Haramba SJ, Millanzi WC, Seif SA. Enhancing nursing student presentation competences using Facilitatory Pecha kucha presentation pedagogy: a quasi-experimental study protocol in Tanzania. BMC Med Educ [Internet]. 2023;23(1):628. https://bmcmededuc.biomedcentral.com/articles/ https://doi.org/10.1186/s12909-023-04628-z

Millanzi WC, Osaki KM, Kibusi SM. Non-cognitive skills for safe sexual behavior: an exploration of baseline abstinence skills, condom use negotiation, Self-esteem, and assertiveness skills from a controlled problem-based Learning Intervention among adolescents in Tanzania. Glob J Med Res. 2020;20(10):1–18.

Google Scholar  

Millanzi WC, Herman PZ, Hussein MR. The impact of facilitation in a problem- based pedagogy on self-directed learning readiness among nursing students : a quasi- experimental study in Tanzania. BMC Nurs. 2021;20(242):1–11.

Millanzi WC, Kibusi SM. Exploring the effect of problem-based facilitatory teaching approach on metacognition in nursing education: a quasi-experimental study of nurse students in Tanzania. Nurs Open. 2020;7(April):1431–45.

Article   Google Scholar  

Millanzi WC, Kibusi SM. Exploring the effect of problem based facilitatory teaching approach on motivation to learn: a quasi-experimental study of nursing students in Tanzania. BMC Nurs [Internet]. 2021;20(1):3. https://bmcnurs.biomedcentral.com/articles/ https://doi.org/10.1186/s12912-020-00509-8

Hadiyanti KMW, Widya W. Analyzing the values and effects of Powerpoint presentations. LLT J J Lang Lang Teach. 2018;21(Suppl):87–95.

Nichani A. Life after death by power point: PechaKucha to the rescue? J Indian Soc Periodontol [Internet]. 2014;18(2):127. http://www.jisponline.com/text.asp?2014/18/2/127/131292

Uzun AM, Kilis S. Impressions of Pre-service teachers about Use of PowerPoint slides by their instructors and its effects on their learning. Int J Contemp Educ Res. 2019.

Unesco National Commission TM. UNESCO National Commission Country ReportTemplate Higher Education Report. [ UNITED REPUBLIC OF TANZANIA ]; 2022.

TCU. VitalStats on University Education in Tanzania. 2020. 2021;1–4. https://www.tcu.go.tz/sites/default/files/VitalStats 2020.pdf.

Kwame A, Petrucka PM. A literature-based study of patient-centered care and communication in nurse-patient interactions: barriers, facilitators, and the way forward. BMC Nurs [Internet]. 2021;20(1):158. https://bmcnurs.biomedcentral.com/articles/ https://doi.org/10.1186/s12912-021-00684-2

Kourkouta L, Papathanasiou I. Communication in Nursing Practice. Mater Socio Medica [Internet]. 2014;26(1):65. http://www.scopemed.org/fulltextpdf.php?mno=153817

Foulkes M. Presentation skills for nurses. Nurs Stand [Internet]. 2015;29(25):52–8. http://rcnpublishing.com/doi/ https://doi.org/10.7748/ns.29.25.52.e9488

Solusia C, Kher DF, Rani YA. The Use of Pecha Kucha Presentation Method in the speaking for Informal Interaction Class. 2020;411(Icoelt 2019):190–4.

Sen G. What is PechaKucha in Teaching and How Does It Work? Clear Facts About PechaKucha in Classroom [Internet]. Asian College of Teachers. 2016 [cited 2022 Jun 15]. https://www.asiancollegeofteachers.com/blogs/452-What-is-PechaKucha-in-Teaching-and-How-Does-It-Work-Clear-Facts-About-PechaKucha-in-Classroom-blog.php

Pecha Kucha Website. Pecha Kucha School [Internet]. 2022. https://www.pechakucha.com/schools

Mabuan RA. Developing Esl/Efl Learners Public Speaking Skills through Pecha Kucha Presentations. Engl Rev J Engl Educ. 2017;6(1):1.

Laieb M, Cherbal A. Improving speaking performance through Pecha Kucha Presentations among Algerian EFL Learners. The case of secondary School students. Jijel: University of Mohammed Seddik Ben Yahia; 2021.

Angelina P, IMPROVING INDONESIAN EFL STUDENTS SPEAKING, SKILL THROUGH PECHA KUCHA. LLT J A. J Lang Lang Teach [Internet]. 2019;22(1):86–97. https://e-journal.usd.ac.id/index.php/LLT/article/view/1789

Abraham RR, Torke S, Gonsalves J, Narayanan SN, Kamath MG, Prakash J, et al. Modified directed self-learning sessions in physiology with prereading assignments and Pecha Kucha talks: perceptions of students. Adv Physiol Educ. 2018;42(1):26–31.

Coskun A. The Effect of Pecha Kucha Presentations on Students’ English Public Speaking Anxiety. Profile Issues Teach Prof Dev [Internet]. 2017;19(_sup1):11–22. https://revistas.unal.edu.co/index.php/profile/article/view/68495

González Ruiz C, STUDENT PERCEPTIONS OF THE USE OF PECHAKUCHA, In PRESENTATIONS IN SPANISH AS A FOREIGN LANGUAGE. 2016. pp. 7504–12. http://library.iated.org/view/GONZALEZRUIZ2016STU

Warmuth KA. PechaKucha as an Alternative to Traditional Student Presentations. Curr Teach Learn Acad J [Internet]. 2021;(January). https://www.researchgate.net/publication/350189239

Hayashi PMJ, Holland SJ. Pecha Kucha: Transforming Student Presentations. Transform Lang Educ [Internet]. 2017; https://jalt-publications.org/files/pdf-article/jalt2016-pcp-039.pdf

Solmaz O. Developing EFL Learners ’ speaking and oral presentation skills through Pecha Kucha presentation technique. 2019;10(4):542–65.

Tanzania Commission for Universities. University Institutions operating in Tanzania. THE UNITED REPUBLIC OF TANZANIA; 2021.

The University of Dodoma. About Us [Internet]. 2022 [cited 2022 Aug 22]. https://www.udom.ac.tz/about

NACTVET. Registered Institutions [Internet]. The United Republic of Tanzania. 2022. https://www.nacte.go.tz/?s=HEALTH

TCU. University education in tanzania 2021. VitalStats, [Internet]. 2022;(May):63. https://www.tcu.go.tz/sites/default/files/VitalStats 2021.pdf.

St. John University of Tanzania. About St. John University [Internet]. 2022 [cited 2022 Aug 22]. https://sjut.ac.tz/our-university/

TopUniversitieslist. St John’s University of Tanzania Ranking [Internet]. World University Rankings & Reviews. 2023 [cited 2023 Jul 1]. https://topuniversitieslist.com/st-johns-university-of-tanzania/

Tanzania Nursing and Midwifery Council. TANZANIA NURSING AND MIDWIFERY COUNCIL THE REGISTRATION AND LICENSURE EXAMINATION GUIDELINE FOR NURSESAND MIDWIVES IN TANZANIA REVISED VERSION. : 2020; https://www.tnmc.go.tz/downloads/

Salim MA, Gabrieli P, Millanzi WC. Enhancing pre-school teachers’ competence in managing pediatric injuries in Pemba Island, Zanzibar. BMC Pediatr. 2022;22(1):1–13.

Iliyasu R, Etikan I. Comparison of quota sampling and stratified random sampling. Biometrics Biostat Int J [Internet]. 2021;10(1):24–7. https://medcraveonline.com/BBIJ/comparison-of-quota-sampling-and-stratified-random-sampling.html

Surucu L, Ahmet M, VALIDITY, AND RELIABILITY IN QUANTITATIVE RESEARCH. Bus Manag Stud An Int J [Internet]. 2020;8(3):2694–726. https://bmij.org/index.php/1/article/view/1540

Lima E, de Barreto P, Assunção SM. Factor structure, internal consistency and reliability of the posttraumatic stress disorder checklist (PCL): an exploratory study. Trends Psychiatry Psychother. 2012;34(4):215–22.

Taber KS. The Use of Cronbach’s alpha when developing and Reporting Research Instruments in Science Education. Res Sci Educ. 2018;48(6):1273–96.

Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2(2011):53–5.

Madar P, London W, ASSESSING THE STUDENT :. PECHAKUCHA. 2013;3(2):4–10.

Haramba, S. J., Millanzi, W. C., & Seif, S. A. (2023). Enhancing nursing student presentation competencies using Facilitatory Pecha Kucha presentation pedagogy: a quasi-experimental study protocol in Tanzania. BMC Medical Education, 23(1), 628. https://doi.org/10.1186/s12909-023-04628-z

Bakcek O, Tastan S, Iyigun E, Kurtoglu P, Tastan B. Comparison of PechaKucha and traditional PowerPoint presentations in nursing education: A randomized controlled study. Nurse Educ Pract [Internet]. 2020;42:102695. https://linkinghub.elsevier.com/retrieve/pii/S1471595317305097

Mcleod G. Learning theory and Instructional Design. Learn Matters. 2001;2(2003):35–43.

Warmuth KA, Caple AH. Differences in Instructor, Presenter, and Audience Ratings of PechaKucha and Traditional Student Presentations. Teach Psychol [Internet]. 2022;49(3):224–35. http://journals.sagepub.com/doi/10.1177/00986283211006389

Download references

Acknowledgements

The supervisors at the University of Dodoma, statisticians, my employer, family members, research assistants and postgraduate colleagues are acknowledged for their support in an attempt to facilitate the development and completion of this manuscript.

The source of funds to conduct this study was the registrar, Tanzania Nursing and Midwifery Council (TNMC) who is the employer of the corresponding author. The funds helped the author in developing the protocol, printing the questionnaires, and facilitating communication during the data collection and data analysis and manuscript preparation.

Author information

Authors and affiliations.

Department of Nursing Management and Education, The University of Dodoma, Dodoma, United Republic of Tanzania

Setberth Jonas Haramba & Walter C. Millanzi

Department of Public and Community Health Nursing, The University of Dodoma, Dodoma, United Republic of Tanzania

Saada A. Seif

You can also search for this author in PubMed   Google Scholar

Contributions

S.J.H: conceptualization, proposal development, data collection, data entry, data cleaning and analysis, writing the original draft of the manuscript W.C.M: Conceptualization, supervision, review, and editing of the proposal, and the final manuscript S.S.A: Conceptualization, supervision, review, and editing of the proposal and the final manuscript.

Corresponding author

Correspondence to Setberth Jonas Haramba .

Ethics declarations

Ethics approval and consent to participate.

All methods were carried out under the relevant guidelines and regulations. Since the study involved the manipulation of human behaviors and practices and the exploration of human internal learning experiences, there was a pressing need to obtain ethical clearance and permission from the University of Dodoma (UDOM) Institution of Research Review Ethics Committee (IRREC) in order to conduct this study. The written informed consents were obtained from all the participants, after explaining to them the purpose, the importance of participating in the study, the significance of the study findings to students’ learning, and confidentiality and privacy of the information that will be provided. The nursing students who participated in this study benefited from the knowledge of the Pecha Kucha presentation format and how to prepare and present their assignments using the Pecha Kucha presentation format.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Haramba, S.J., Millanzi, W.C. & Seif, S.A. Effects of pecha kucha presentation pedagogy on nursing students’ presentation skills: a quasi-experimental study in Tanzania. BMC Med Educ 24 , 952 (2024). https://doi.org/10.1186/s12909-024-05920-2

Download citation

Received : 16 October 2023

Accepted : 16 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1186/s12909-024-05920-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Nursing students
• Pecha Kucha presentation pedagogy and presentation skills

BMC Medical Education

ISSN: 1472-6920

• General enquiries: [email protected]