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Term |
Definition |
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Academic performance |
Academic
performance refers to a student's level of achievement or success in their
educational pursuits.
It
typically includes factors such as grades, test scores, class participation,
and overall understanding of the subject matter . |
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Action research |
Action
research is a research approach that focuses on solving practical problems in
real-world settings.
It
involves collaboration between researchers and practitioners to identify,
implement, and evaluate interventions or improvements.
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Alternative hypothesis |
In
hypothesis testing, the alternative hypothesis (H₁)
represents the assertion or claim that is contrary to the null hypothesis (H₀).
It suggests that there is a significant relationship, difference, or effect
present in the population being studied.
The
alternative hypothesis is typically denoted as H₁
and is formulated based on the research question or the objective of the
study. It represents the hypothesis that the researcher wants to support or demonstrate
with evidence from the data.
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ANCOVA |
ANCOVA,
short for Analysis of Covariance, is a statistical technique used in
educational research to analyze the relationship between a categorical
independent variable (such as different teaching methods or instructional
interventions) and a continuous dependent variable (such as student
achievement scores), while controlling for the effects of one or more
covariates (such as pretest scores or socioeconomic status).
ANCOVA
allows researchers in education to determine if there are significant
differences in the means of the dependent variable among different groups,
while accounting for the influence of covariates that may impact the outcome.
By controlling for covariates, researchers can better isolate the effect of
the categorical independent variable on the dependent variable, thus
enhancing the accuracy of their findings.
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ANOVA |
ANOVA
(Analysis of Variance) is a statistical technique used to compare means
between two or more groups or conditions.
It
determines whether the observed differences are statistically significant and
can be generalized to the population.
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APA |
APA stands for
the American Psychological
Association.
However,
in the context of academic writing and research, APA typically refers to the APA style,
which is a set of guidelines and conventions established by the American
Psychological Association for writing and documenting research papers,
articles, and other scholarly works.
The
APA style provides rules and recommendations for formatting, citing sources,
organizing content, and presenting research findings in the social sciences,
including psychology, education, sociology, and other related disciplines.
It
is widely used by researchers, students, and professionals to ensure
consistency, clarity, and proper attribution of sources in academic writing.
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Applied research |
Applied
research refers to a type of research that focuses on solving practical problems
or addressing specific real-world issues.
It
involves the systematic investigation of a specific problem or question with
the aim of providing practical solutions or contributing to the improvement
of practices, processes, or policies.
Example
of applied research topics: i. Assessing the effectiveness of professional development programs for
teachers’
ii. Evaluating
the effectiveness of a new teaching method, iii. Investigating the impact of technology
integration in the classroom, iv. Assessing the impact of family engagement strategies on
student achievement.
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Attitude towards science |
Attitude
towards science refers to an individual's beliefs, feelings, and opinions
about science and its relevance.
It
can influence a person's interest in science, motivation to learn, and
willingness to pursue scientific careers.
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Average deviation |
Average
deviation, also known as mean deviation, is a statistical measure that quantifies
the average distance between each data point in a dataset and the mean or
average of that dataset. It is used to determine the variability or
dispersion of the data points around the mean.
To
calculate the average deviation, you follow these steps:
The
formula for calculating the average deviation is as follows: Average
Deviation = (Σ|X - μ|) / N Where:
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Basic research |
Basic
research, also known as fundamental or pure research, refers to scientific or
academic inquiry that is conducted to expand knowledge and understanding in a
particular field without any immediate practical application or specific
problem-solving goal.
Basic
research can be found in various disciplines, including physics, biology,
psychology, sociology, and many others. It typically involves theoretical
explorations, experimentation, and hypothesis testing in controlled settings.
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Categorical variable |
Categorical
variable is a variable that represents distict categories or groups. E.g.
Gender(Male/female),ethnicity(Hausa,Igbo and Yoruba), grade level(Level1, Level
2, Level 3 and Level 4).
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Causation |
Causation
refers to a cause-and-effect relationship between variables, where changes in
one variable directly lead to changes in another variable. Establishing
causation requires more rigorous evidence and meeting specific criteria.
To establish causation, researchers often
employ experimental designs, where they manipulate an independent variable
and observe its effects on a dependent variable. Random assignment to control
and experimental groups helps ensure that any observed effects are due to the
manipulated variable, rather than other factors.
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Central limit theorem |
The
central limit theorem states that the sampling distribution of the mean
approaches a normal curves as the sample size, n, get larger, regardless
of the shape of the original population distribution.
As
a general guideline, a sample size of 30 or more is often considered
sufficient for the Central Limit Theorem to provide a reasonably accurate
approximation.
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Chi Square |
The
chi-square test is a statistical test used to analyze categorical data and
determine if there is a significant association between variables. It
compares observed and expected frequencies to assess if the relationship is
statistically significant.
It
is commonly used in various fields to examine relationships between
categorical variables and test hypotheses.
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Citation |
Citation
is a reference to a published work in a research paper or article.
It
includes details such as the author's name, publication title, journal name,
year, and page numbers, allowing readers to locate the original source.
"The
importance of renewable energy sources has been widely recognized (James
& John, 2019)."
In
this example, "James & John" refers to the authors of the
source, and "2019" indicates the year of publication. The in-text
citation is placed within parentheses and appears directly after the
information or quote that is being cited.
According
to Chukwudi (2018), "Climate change is a pressing issue that requires
immediate action" (p. 24).
In
this example, "Chukwudi" is the author's last name,
"2018" is the year of publication, and "p. 24" indicates
the page number where the quote can be found.
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Cluster analysis |
Cluster
analysis in education is a method of grouping students based on their
similarities in characteristics or performance.
For instance, using data on grades,
attendance, and study habits, cluster analysis helps identify clusters of
students with similar patterns.
These
clusters aid in understanding different learner types, such as high
achievers, average performers, or struggling students.
This
information helps tailor teaching strategies, interventions, and personalized
learning plans to improve educational outcomes.
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Confidence level |
The
confidence level is a measure of how confident we can be in the results of a
statistical analysis. It is often expressed as a percentage, such as 95% or
90%. For
example, a 95% confidence level means that if we were to repeat the same
analysis many times using different samples, we would expect the true
population parameter to be within the calculated interval in about 95% of
those analyses.
The
confidence level is closely related to the margin of error in estimation. The
margin of error is the maximum
amount by which our estimate may differ from the true population parameter. A
higher confidence level requires a larger sample size, resulting in a smaller
margin of error.
In
hypothesis testing, the confidence level determines the threshold for making
decisions. It represents the probability of rejecting the null hypothesis
when it is actually true (Type I error). A common choice is a 5% significance
level, corresponding to a 95% confidence level. If the calculated p-value is
less than 0.05, we reject the null hypothesis and conclude that there is
evidence in favor of the alternative hypothesis.
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Confounding variable |
A
variable that is related to both the independent and dependent variables, but
is not part of the research design. It can create a false association or
influence the results.
For
example, if the age of students is related to both the teaching method and
test scores, it could act as a confounding variable. . Other
examples include: Teacher experience, student motivation, school quality,
e.t.c.
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Construct validity |
Construct
validity refers to the extent to which a research study accurately measures
or represents the theoretical constructs or concepts it intends to assess.
It
ensures that the measurements used in the study are valid and meaningful.
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Content validity |
Content
validity in educational research refers to the degree to which an assessment
instrument, such as a test or questionnaire, accurately measures the content
it intends to assess.
For
instance, when developing a mathematics achievement test, content validity
would ensure that the test covers all relevant topics and skills taught in
the mathematics curriculum, such as algebra, geometry, and statistics. This
would ensure that the test accurately represents the content domain and
provides a valid measure of students' mathematical knowledge and skills.
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Continuous variable |
Continuous
variable is a variable that can take any value within a specific range E.g.
Age, time spent studying, educational e.t.c.
|
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Control group |
A
control group is a group of participants in a research study that does not
receive the experimental treatment or intervention.
It serves as a baseline for comparison to
measure the effects of the treatment.
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Control variable |
A
variable held constant to isolate the effect of the independent variable E.g. Age, gender, educational background
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Correlation |
Correlation
refers to a statistical relationship or association between two or more
variables. It measures the extent to which changes in one variable are
related to changes in another variable.
Correlation
does not imply causation but indicates the strength and direction of the
relationship between variables.
Correlation
can be positive (both variables increase or decrease together), negative (one
variable increases while the other decreases), or zero (no relationship).
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Correlation coefficient |
The
correlation coefficient is a statistical measure that quantifies the strength
and direction of the relationship between two variables.
It
ranges from -1 to +1, with a value of 0 indicating no correlation.
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Critical thinking
|
Critical
thinking is the ability to objectively analyze and evaluate information,
arguments, and ideas.
It
involves questioning assumptions, considering multiple perspectives,
identifying biases, and making informed judgments based on evidence and
logical reasoning.
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Critical value |
A
critical value is a threshold used in hypothesis testing and determining
statistical significance. It is based on the chosen level of significance
(alpha), representing the probability of rejecting the null hypothesis when
it's true. The critical value is selected based on the desired alpha level
(e.g., 0.05 or 0.01) and degrees of freedom associated with the test
The
critical value is derived from a probability distribution like the
Z-distribution or t-distribution, depending on the test. It sets the boundary
for rejecting the null hypothesis if the test statistic falls outside it.
By
comparing the test statistic to the critical value, researchers assesses if
results are unlikely due to chance alone, leading to either rejecting the
null hypothesis or accepting the alternative.
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Data Collection |
Data
collection refers to the process of gathering information or data for
research, analysis, or decision-making purposes.
It
involves systematically collecting, recording, and organizing data to obtain meaningful
insights and draw conclusions.
Remember
that effective data collection requires careful planning, attention to
detail, and adherence to ethical standards. It is crucial to design and
implement a robust data collection process to obtain reliable and valid data
for your research or analysis
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Data Analysis |
Data
analysis is the process of inspecting, cleaning, transforming, and
interpreting data to uncover patterns, trends, and relationships.
It
involves using statistical techniques and software tools to make sense of the
data collected.
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Data visualization |
Data
visualization involves representing data in visual formats, such as graphs,
charts, and info graphics, to facilitate understanding and interpretation.
It
helps researchers communicate their findings effectively.
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Dependent variable |
Dependent
variable is the variable being measured or observed in response to changes in
the independent variable. It is the outcome or result of the study.
In
the education example, the dependent variable could be the students' test
scores or learning outcomes.
E.g. Test scores, GPA, graduation rates
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Descriptive research |
Descriptive
research is a type of research methodology that focuses on observing and
describing existing phenomena, characteristics, behaviors, or conditions
without attempting to manipulate variables or establish causal relationships.
The
main goal of descriptive research is to provide an accurate and detailed
picture of a particular subject or population.
Example
could be Investigation of the academic performance of students from different
socio-economic backgrounds or Investigation of the factors influencing
student dropout rates in a specific educational institution.
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Descriptive statistics |
Descriptive
statistics involves summarizing and presenting data in a meaningful and
concise manner.
It includes measures such as mean, median,
mode, range, and standard deviation to describe the central tendency,
variability, and distribution of the data.
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Descriptive survey
research |
Descriptive
survey research is a specific type of descriptive research that utilizes
surveys as the primary data collection method.
It
involves gathering data through structured questionnaires or interviews to
obtain information directly from individuals or groups of interest.
It
helps researchers gather information about opinions, behaviors, and
characteristics.
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Discrete variable
|
Discrete
variable that can only take specific, separate value E.g. Number of students
in a class, test scores(0-100).
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Editing/Revising |
It
is process of making the research report better by fixing mistakes, making it
easier to understand, and adding new information where necessary.
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Educational research
|
Educational
research focuses on studying various aspects of the education system, such as
teaching methods, student learning, curriculum design, and educational
policies.
It
aims to improve educational practices and outcomes.
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Experimental Group |
An
experimental group refers to a group of participants or subjects in a
research study who are exposed to the specific intervention or treatment
being investigated.
In
an experiment, the goal is to examine the effects of the intervention or
treatment on the variables of interest. The experimental group receives the
intervention or treatment, allowing researchers to compare their outcomes
with those of a control group, which does not receive the intervention.
By
comparing the results between the experimental group and the control group,
researchers can evaluate the impact and effectiveness of the intervention
under investigation.
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Experimental Study |
Experimental
study is research design where the researcher manipulates the independent
variable to observe its effects on the dependent variable.
It
allows for cause-and-effect relationships to be established.
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Expost facto research |
Ex
post facto research refers to a type of observational study in which the
researcher analyzes data from events or conditions that have already
occurred, without the ability to manipulate or control the variables being
studied.
It
involves examining relationships between variables that have already taken
place or are beyond the researcher's control.
An
example of ex post facto research could be investigating the relationship between student performance and a specific teaching
method or curriculum after the instruction has taken place.
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Factor analysis |
Factor
analysis is a statistical technique used to uncover underlying factors or
dimensions that explain patterns in data.
In
the context of education, let's say we have a questionnaire with multiple
items that measure different aspects of student performance, such as
attendance, homework completion, and test scores.
By
applying factor analysis, we can identify underlying factors, such as
"academic engagement" or "study habits," that contribute
to the observed patterns in student performance.
This helps us understand the relationships
between different variables and simplifies complex data into meaningful
dimensions, allowing educators to target specific areas for improvement and
develop effective interventions.
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Friedman test |
The
Friedman test is a non-parametric statistical test used to compare the
distributions of three or more related or paired samples.
It
is often used in the education field to analyze data collected from multiple
conditions or time points, where the dependent variable is measured
repeatedly within the same group.
The
Friedman test is a ranked-based test that does not assume a specific
distribution of the data. It allows for the detection of differences between
the groups but does not identify which specific groups differ from each
other. If the Friedman test yields a significant result, post-hoc tests
(e.g., Dunn's test) can be performed to determine pairwise differences
between the groups.
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F-test |
The
F-test is a statistical hypothesis test that is used to compare the variances
of two or more populations or groups. It assesses whether the variability or
dispersion among the groups is significantly different.
The
F-test calculates the F-statistic, which is the ratio of two sample
variances. It compares the larger variance to the smaller variance to
determine if there is a significant difference between them.
The
test is based on the F-distribution, which is a probability distribution that
arises in the context of hypothesis testing involving variances.
Examples
of F-tests include one-way ANOVA, two-way ANOVA, Analysis of Covariance
(ANCOVA), Regression, MANOVA, Goodness-of-Fit, and Homogeneity of Variances.
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Historical research |
Historical
research involves the examination and interpretation of past events,
developments, or phenomena. It aims to understand and analyze historical
contexts, causes, and effects through the investigation of primary and
secondary sources.
Historical
research often relies on archival materials, documents, artifacts, and
interviews with individuals who have firsthand knowledge or experiences
related to the research topic.
Example
of historical research topic: “The Historical
Development and Impact of the Universal Basic Education (UBE) Programme in
Nigeria”.
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Hypothesis |
Hypothesis
is a specific statement that predicts the relationship between variables in a
research study.
It guides the research process and provides
a framework for data analysis and interpretation, and it is based on existing
knowledge or theories and serves as a starting point for investigation.
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Hypothesis
testing |
Hypothesis
testing is a statistical process used to assess the validity of a hypothesis
or claim about a population.
It
involves formulating null and alternative hypotheses, collecting data, and
using statistical tests to retain or reject the null hypothesis.
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Independent
variable |
An
independent variable is the variable manipulated or controlled by the
researcher in a study.
It
is believed to have an effect on the dependent variable.
It
is the factor that is believed to have an effect on the dependent variable. E.g.
Teaching method, study time, class size e.t.c.
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Inferential
statistics |
Inferential
statistics involves using sample data to make inferences or draw conclusions
about a larger population.
It
helps researchers generalize their findings beyond the immediate sample.
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Informed
consent form |
An
informed consent form is a document that explains the purpose, procedures,
risks, and benefits of participating in a research study. Participants sign
the form to indicate their voluntary agreement to participate.
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Instrument |
Instrument
refers to the tools or techniques used to collect data in a research study.
Examples
include surveys, questionnaires, interviews, and observation protocols.
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Interval
scale |
The interval scale has all the
properties of the ordinal scale, but the intervals between the values are
equal and can be measured. However, it does not have a true zero point.
Examples
include temperature measured in Celsius or Fahrenheit, but 0°C or 0°F does
not indicate the complete absence of temperature. Others are calendar dates,
IQ scores, GPA, test/exam scores, latitude & Longitude, e.t.c.
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Kruskal-Wallis
test |
The Kruskal-Wallis test is a
non-parametric statistical test used to compare three or more independent
groups when the dependent variable is continuous.
It is an extension of the
Mann-Whitney U test, which is used for comparing two independent groups.
The Kruskal-Wallis test is used
when the assumptions for parametric tests, such as the analysis of variance
(ANOVA), are violated. These assumptions include normality and equal
variances in the populations. By using rank scores instead of raw data, the
Kruskal-Wallis test allows for the comparison of groups without assuming
these specific distributional characteristics.
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Literature |
In research studies, literature
refers to previously published scholarly works and academic sources relevant
to the research topic.
Examples of literature in research
studies include academic journal articles, books, conference proceedings,
research projects, dissertations, and research reports that contribute to the
existing knowledge and inform the current study.
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Literature
review |
A
literature review is a critical summary and evaluation of existing research
and scholarly articles related to a specific topic.
It
helps researchers understand the current state of knowledge and identify
research gaps.
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Mastery learning method |
Mastery
learning method is an instructional approach where students progress through
a subject at their own pace, mastering each concept before moving on to the
next.
It
focuses on providing targeted feedback, additional practice, and remediation
to ensure that all students achieve a high level of mastery.
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MANOVA |
MANOVA
(Multivariate Analysis of Variance) is a statistical technique that
simultaneously analyzes multiple dependent variables to determine if there
are significant differences between groups or treatments.
It extends the ANOVA test to consider the
relationships among variables. MANOVA assesses whether the mean vectors of
groups differ significantly, providing insights into overall differences
across multiple variables.
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Mann-Whitney U test |
The
Mann-Whitney U test, also known as the Wilcoxon rank-sum test, is a
non-parametric statistical test used to compare the distributions of two
independent groups when the dependent variable is continuous.
It
is often used in the education field to compare the performance of students
from different groups or conditions.
The
Mann-Whitney U test is used when the assumptions of parametric tests, such as
the independent samples t-test, are not met. It does not assume that the data
follow a specific distribution and can be applied to ordinal, interval, or
ratio data.
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Margin of error |
The
margin of error is the maximum
amount by which our estimate may differ from the true population parameter. A
higher confidence level requires a larger sample size, resulting in a smaller
margin of error.
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Mean |
The
mean, also called the average, is a statistical measure calculated by summing
up all the values in a dataset and dividing the sum by the total number of
values.
It
provides a measure of central tendency, representing the typical value in the
data. The mean is sensitive to extreme values, so it may not accurately
represent the typical value if outliers are present.
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Mixed methods research |
Mixed
methods research combines both quantitative and qualitative research methods
in a single study.
It
allows researchers to collect and analyze both numerical and non-numerical
data to gain a comprehensive understanding of a research problem.
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Moderating variable |
Moderating
Variable is a variable that affects the relationship between the independent
and dependent variables.
It
influences the strength or direction of the relationship. For instance, in an
education study, the moderating variable could be the students' prior knowledge, which may influence how the teaching
method impacts their learning outcomes.
Other
examples include E.g. Gender,
socioeconomic status.
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Mole concept in
chemistry
|
Mole
concept is a fundamental concept in chemistry that relates the mass of a
substance to the number of atoms or molecules it contains.
It allows chemists to quantify and compare
the amounts of substances in chemical reactions.
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Nominal
scale/variables |
The nominal scale is the lowest level of
measurement and involves variables that can be categorized into distinct
groups or categories.
Nominal
variables do not have any inherent order or magnitude. Examples include
gender (male or female), marital status (married, single, divorced), or eye
color (blue, brown, green).
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Non-parametric
test |
A
non-parametric test is a statistical test that does not rely on specific
assumptions about the population distribution. These tests are often used
when the data does not meet the assumptions required for parametric tests.
Examples
of non-parametric tests include: Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis test, Spearman's rank correlation coefficient
and Chi-square test of independence.
Non-parametric
tests are more flexible and robust to deviations from assumptions, but they
may have less statistical power compared to their parametric counterparts.
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Normal
distribution |
The
normal distribution, also known as the bell curve, is a symmetrical
probability distribution where most data points are clustered around the
mean, with fewer data points in the tails. Many natural phenomena follow a
normal distribution.
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Objective |
Objective
is a specific goal or aim that a researcher wants to achieve through their
study. It provides a clear direction for investigation and helps focus the research
process.
The
main objective of the research study
represents the primary goal or purpose of the research and provides a general
direction and scope for the study. The specific
objectives, on the other hand, break down the broad objective into
measurable and specific components, guiding the research process and
supporting the accomplishment of the main objective.
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One-tailed
test |
A
one-tailed test is a statistical hypothesis test where the alternative
hypothesis is directional, focusing on a specific direction of effect. In a
one-tailed test, the critical region is located entirely in one tail of the
distribution.
For
example; One-tailed t-test: is used to determine if the mean of a sample is
significantly greater or smaller than a specified value.
Alternative
hypothesis (Ha): The mean score of Group A is significantly smaller than the
mean score of Group B.
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Ordinal
scale/variable |
Ordinal
Scale: The ordinal scale represents variables that not only have distinct
categories but also possess a natural order or ranking. The differences
between the categories, however, may not be equal or measurable.
Examples
include ratings on a Likert scale (e.g., strongly agree, agree, neutral,
disagree, strongly disagree) or educational attainment levels (e.g., high
school diploma, bachelor's degree, master's degree).
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Outliers |
An
outlier is a data point that
significantly deviates from the general pattern or distribution of the other
data points in a dataset. It is an observation that lies an abnormal distance
away from other observations, either in terms of its value or its
relationship to other variables.
Outliers
can arise due to various reasons, such as measurement errors, data entry
mistakes, natural variations, or extreme values in the population being
studied. They can have a significant impact on statistical analyses and may
distort the results or conclusions drawn from the data.
Identifying
and handling outliers is an important step in data analysis. Outliers can be
detected using various techniques, such as graphical methods (e.g., scatter
plots or boxplots) or statistical methods (e.g., Z-score). Once identified,
the analyst can decide on an appropriate course of action, which may involve
further investigation, data cleaning, transformation, or exclusion of the
outlier from the analysis.
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Parametric
tests |
A
parametric test is a statistical test that assumes specific characteristics
about the population being studied, such as normal distribution or equal
variances.
Examples
of parametric tests include: t-test, ANOVA, Pearson’s correlation
coefficient, linear regression, e.t.c.
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Peer
review |
Peer
review is a process where experts in the same field evaluate and critique a
research study or scholarly articles or papers before they are published. This
rigorous review process helps ensure the quality, accuracy and validity of
the research findings.
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Pilot
study |
A
pilot study, a small-scale preliminary investigation, is conducted before the
main study to assess feasibility, reliability, and effectiveness.
The
primary purpose of a pilot study is to identify and address any potential
issues or challenges before committing to a full-scale study.
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Plagiarism |
Plagiarism
is using someone else's work without giving proper credit.
It
includes presenting others' words, ideas, or creations as your own.
Plagiarism is widely condemned and can have serious consequences in academic
and professional contexts.
To
avoid plagiarism, always attribute and cite original sources. Properly referencing
and acknowledging the work of others is essential to maintain integrity and
uphold ethical standards.
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Population |
Population
is the entire group of individuals or elements that a researcher wants to
study and generalize the findings to.
It
may be a specific group, such as students in a school, or a broader
population, such as all adults in a country.
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Proof
reading |
Proofreading
the process of improving research report by carefully checking for errors,
making it clearer, and adding new information for better understanding.
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Qualitative data
analysis |
Qualitative
data analysis involves interpreting and making sense of non-numerical data.
It
includes techniques like coding, thematic analysis, and identifying patterns
and themes in the data.
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Qualitative
research |
Qualitative
research involves collecting and analyzing non-numerical data such as interviews, observations, and texts.
It
aims to gain an in-depth understanding of experiences, meanings, and social
phenomena.
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Quantitative
research |
Quantitative
research involves collecting and analyzing numerical data to answer research questions.
It
focuses on objective measurements, statistical analysis, and numerical
representations of data.
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Quazi-experimental
research |
Quasi-experimental
research refers to a research approach that shares similarities with
experimental research but falls short of meeting all the criteria of a
traditional experiment. It is commonly employed in situations where complete
control over variables or random assignment of participants is impractical,
unethical, or not feasible.
In
quasi-experimental research, researchers typically select pre-existing groups
or naturally occurring events and compare their outcomes to analyze
cause-and-effect relationships. While it attempts to establish causal links,
the absence of randomization introduces potential biases that need to be
addressed using statistical techniques to enhance the reliability of the
findings.
Quasi-experimental
research provides a means to study real-world settings and draw conclusions,
albeit with careful considerations and adjustments.
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Questionnaire |
A
questionnaire is a data collection tool that consists of a set of structured
questions.
It
is used to gather information from participants in a systematic and
standardized manner.
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|
Random
sampling |
Random
sampling is a technique used to select a representative sample from a larger
population.
Each
member of the population has an equal chance to be included in the sample,
ensuring that the sample is unbiased and reflective of the population.
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|
Ratio
scale |
The
ratio scale is the highest level of measurement and possesses all the
properties of the interval scale. In addition to having equal intervals, the
ratio scale has a true zero point that indicates the absence of the measured
attribute.
Examples
include height, mass, volume, weight, time, or income.
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|
Reasoning
ability |
Reasoning
ability refers to the capacity to think logically, analyze information, and
draw valid conclusions.
It
involves skills such as deductive reasoning, inductive reasoning, problem-solving,
and making logical connections between ideas.
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|
Referencing |
Referencing
is the practice of acknowledging and providing information about sources used
in academic work.
It
involves citing author names, titles, and publication details to give credit
and facilitate traceability of ideas while avoiding plagiarism.
Examples:
Journal
Article: Sule,
A. E., John, C. B., & Brown, M. R.
(2022). The effects of diet on mental health in adults. Journal of Applied Psychology, 55(3), 123-145.
Book: Johnson,
M. S. (2019). The Art of Effective
Communication. New York, NY: Random House.
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|
Regression
analysis |
Regression
analysis is a statistical technique used to explore the relationship between
a dependent variable and one or more independent variables.
It
helps determine the strength and direction of the relationship and allows for
prediction.
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|
Relative standard deviation |
The
relative standard deviation (RSD) is a statistical measure that is used to
quantify the variability or dispersion of a dataset relative to its mean. It
is also known as the coefficient of variation(CV).
It is expressed as a percentage and provides
a way to compare the spread of different datasets, particularly when they
have different units of measurement or scales.
The
formula for calculating the relative standard deviation is: RSD
= (standard deviation / mean) * 100
where
the standard deviation is a measure of the dispersion of the dataset, and the
mean is the average value.
The
RSD is often used in analytical chemistry and other scientific fields where
precision and accuracy are important. It is particularly useful when
comparing the variability of measurements or observations from different
experiments, instruments, or conditions. A lower RSD indicates less
variability and greater precision, while a higher RSD suggests greater
variability and less precision.
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|
Reliability
|
Reliability
refers to the consistency and stability of research instrument.
An
instrument is considered reliable, if it produces consistent results when
repeated under similar conditions.
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|
Research |
Research
is a systematic investigation to discover new knowledge or validate
existing information.
It
involves gathering data, analyzing it, and drawing conclusions to answer
specific questions.
|
|
Research
bias |
Research
bias refers to systematic errors or distortions that can occur during the
research process, leading to inaccurate or misleading results.
Common
types of bias include selection bias, measurement bias, and publication bias.
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|
Research
conference
|
A
research conference is a gathering of researchers, scholars, and
practitioners to present and discuss their research findings.
It
provides a platform for knowledge sharing, networking, and collaboration.
|
|
Research
design |
A
research design is a plan or strategy outlining how a research study will be
conducted.
It
includes selecting the research method, sample size, data collection
techniques, and data analysis procedures.
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|
Research ethics
committee |
A
research ethics committee is a group of experts responsible for reviewing and
approving research studies to ensure ethical standards are met.
They
assess the potential risks and benefits to participants and ensure compliance
with ethical guidelines.
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|
Research
grant |
A
research grant is financial support provided to researchers by funding
agencies or organizations to conduct their research.
It covers expenses such as equipment,
supplies, and researcher salaries.
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|
Research grant proposal |
A
research grant proposal is a written document that outlines the research
project's objectives, methodology, timeline, and budget.
It
is submitted to funding agencies or organizations to seek financial support
for the research.
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|
Research
journal |
Research
journal is a publication that focuses on disseminating research findings
within a specific field or discipline.
Researchers
often publish their studies in reputable journals to share their work with
the scientific community.
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|
Research literature search |
A
research literature search involves systematically searching and reviewing
existing literature, including academic articles, books, and reports, related
to a specific research topic.
It
helps researchers identify gaps, build on existing knowledge, and support
their own research.
|
|
Research
Methodology |
Research
methodology refers to the overall approach or strategy used to conduct a
research study.
It
includes the methods, techniques, and procedures employed to collect and
analyze data.
|
|
Research
Outcome |
A
research outcome refers to the results or findings obtained from a research
study.
It
includes the new knowledge generated, the insights gained, the conclusions
drawn from the analysis of data, practical applications, policy recommendations,
or advancements in a particular field of study.
|
|
Research
paradigm |
A
research paradigm refers to the philosophical framework or worldview that
guides a researcher's approach to conducting research.
It
includes the researcher's assumptions, beliefs, and theoretical perspectives.
|
|
Research
proposal |
A
research proposal is a document that outlines the objectives, methods, and
expected outcomes of a research study.
It
serves as a blueprint and justification for conducting the research, and in
some cases to obtain approval and funding before initiating the research.
|
|
Sample |
A
sample refers to a portion or a selection of individuals or items chosen from
a larger population to represent and provide insights into the
characteristics or behaviors of that population.
|
|
Sample
size |
Sample
size refers to the number of participants or data points included in a
research study.
A
larger sample size generally leads to more reliable and generalizable
results.
|
|
Sampling
error |
Sampling
error refers to the difference between the characteristics of a sample and
the characteristics of the population it represents.
It
occurs due to the natural variability inherent in any sampling process.
|
|
Sampling
techniques |
Sampling
techniques are methods used to select individuals or items from a population
to form a representative sample.
Common
techniques include random sampling, stratified sampling, cluster sampling,
and convenience sampling.
Each
technique has its own advantages and considerations based on the research
objectives and population characteristics.
|
|
Scale of measurement |
Scale
of measurement refers to the properties and characteristics of the variables
used in research or data analysis.
It
categorizes variables into different levels based on the nature and
characteristics of the data they represent.
Four
(4) commonly recognized levels of measurement: nominal scale, ordinal scale, interval scale and ratio scale.
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|
Science
teaching
|
Science
teaching is the process of imparting scientific knowledge, concepts, and
skills to students.
It
involves engaging students in hands-on activities, experiments, and
discussions to foster understanding and scientific thinking.
|
|
Scientific
attitude |
A
scientific attitude refers to the mindset and characteristics that scientists
cultivate in their work.
It
includes traits such as curiosity, open-mindedness, skepticism, objectivity,
and a willingness to embrace uncertainty and learn from mistakes.
|
|
Scientific
inquiry
|
Scientific
inquiry is the process of asking questions, making observations, conducting
experiments, and analyzing data to develop scientific knowledge and
understanding.
It
involves critical thinking and a systematic approach to investigation.
|
|
Scientific
method |
The
scientific method is a systematic approach used by scientists to investigate
phenomena.
It
involves making observations, forming hypotheses, conducting experiments,
collecting data, analyzing results, and drawing conclusions.
|
|
Scientific
process |
Scientific
process refers to the step-by-step approach scientists use to investigate and
understand the natural world.
It
typically involves making observations, forming hypotheses, conducting
experiments or gathering data, analyzing results, and drawing conclusions.
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|
Simulation-games
method |
Simulation-games
method is an instructional approach that uses interactive simulations or
games to engage students in hands-on learning experiences.
It
allows students to explore and experiment in virtual environments, promoting
active participation, problem-solving, and application of knowledge.
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|
SPSS |
SPSS
(Statistical Package for the Social Sciences) is a software package used for
statistical analysis, data management, and data visualization.
It
provides a range of tools and techniques for analyzing data and conducting
research in various fields, including social sciences, business, and health
sciences.
|
|
Standard
deviation |
Standard
deviation is a measure of how spread out the values in a dataset are from the
mean.
It
provides information about the variability or dispersion of the data points.
A higher standard deviation indicates a greater spread, while a lower
standard deviation indicates less variability.
In
statistical analysis, standard deviation helps in understanding the
consistency or variability of a set of data, making it useful for evaluating
risk, comparing data sets, and interpreting results.
|
|
Statistical analysis |
Statistical
analysis is the process of collecting, organizing, and interpreting numerical
data to uncover patterns, trends, and relationships.
It
involves using statistical methods to draw meaningful conclusions from the
data.
|
|
Statistical
inference |
Statistical
inference involves drawing conclusions about a population based on a sample.
It
uses statistical techniques to make predictions and generalizations from the
observed data.
|
|
Statistical
power |
Statistical
power is the probability of correctly rejecting a null hypothesis when it is
false.
Higher
statistical power indicates a lower chance of missing true effects and
increases the reliability of research results.
|
|
Statistical
significance |
Statistical
significance indicates whether the observed differences or relationships in
data are likely due to chance or if they are meaningful.
It
is determined through statistical tests and helps researchers determine if
the findings are meaningful or significant. Overall, It helps researchers
determine the reliability of their findings.
|
|
Statistical
significance testing |
Statistical
significance testing is a statistical method used to determine whether
observed differences or relationships in data are statistically significant
or likely due to chance.
It
involves calculating p-values and comparing them to a predetermined
significance level. E.g. t-test, ANOVA, e.t.c.
|
|
Statistical
software |
Statistical
software is computer software specifically designed for data analysis and
statistical computations. E.g. SPSS, Stata, R.
It
provides tools and functions to perform various statistical tests, generate
graphs, and interpret results.
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|
STEM
education
|
STEM
education stands for Science, Technology, Engineering, and Mathematics
education.
It
emphasizes an interdisciplinary approach to teaching and learning,
integrating these four fields to foster critical thinking, problem-solving,
and innovation skills.
|
|
STEM
integration |
STEM
integration refers to the incorporation of science, technology, engineering,
and mathematics across multiple disciplines and subjects.
It
promotes interdisciplinary learning and encourages students to make
connections between these fields.
|
|
Test-retest
reliability |
Test-retest
reliability is a measure used in research to assess the consistency and
stability of a measurement or instrument over time.
It
evaluates how well the results of a test or measurement correlate when the same
test is administered to the same individuals on two separate occasions.
The
closer the correlation coefficient is to 1.0, the higher the test-retest
reliability.
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|
t-test |
A
t-test is a statistical test used to compare means of two groups and determine
if there's a significant difference. It's commonly used when data follows a
normal distribution and variances are assumed to be equal.
There
are two main types: independent t-test (for comparing means of two
independent groups) and paired t-test (for comparing means of related
groups).
T-tests
help researchers assess if observed differences are statistically significant.
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|
Two-tailed
test |
A
two-tailed test, in statistics, is a hypothesis test that does not specify
the direction of the effect.
It
is used to determine whether the population parameter being tested is
significantly different from a specified value, without assuming whether it
is greater than or less than that value.
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|
Type-1
error |
Type
I error, or false positive, occurs when the null hypothesis is wrongly
rejected, despite it being true. It is a statistical mistake where the
researcher believes there is a significant effect or relationship based on
sample data, even though it's due to random chance.
The
probability of Type I error is the chosen significance level (alpha). For
example, at a 0.05 significance level, there's a 5% chance of falsely
rejecting the null hypothesis. It's crucial to consider both Type I and Type
II errors when interpreting research findings.
|
|
Type-2
error |
Type
II error is a statistical mistake where the researcher fails to detect a true
effect or relationship, incorrectly accepting the null hypothesis. It occurs
when the sample data does not provide enough evidence to conclude that an
effect exists, even though it actually does.
The
probability of Type II error is denoted as beta (β) and is influenced by
factors like sample size, effect size, and the chosen significance level
(alpha). To minimize Type II errors, researchers often increase sample sizes,
use more sensitive statistical tests, or adjust the significance level.
However,
it's important to strike a balance between Type I and Type II errors, as
reducing one may increase the risk of the other. Consideration of the
research context and objectives is crucial when interpreting findings.
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|
Validity |
Validity
of instrument refers to the extent to which data accurately measures what it
is intended to measure.
Valid
data reflects the true characteristics or phenomena under investigation.
|
|
Variable |
Variable
is a characteristic or factor that can be measured or manipulated in a
research study that the researcher is interested in.
It
can be categorical (e.g., gender)
or continuous (e.g., age).
It
can be independent (manipulated by the researcher e.g. teaching methods) or dependent (measured to observe the
effects of the independent variable, e.g.
academic performance).
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|
Variance |
Variance
is a measure of the dispersion or spread of a set of data points around their
mean (average) value. It quantifies how much the individual data points
deviate from the mean.
Mathematically,
variance is calculated as the average of the squared differences between each
data point and the mean. It provides an indication of the variability or
scatter of the data.
|
|
Wilcoxon signed-rank
test |
The
Wilcoxon signed-rank test is a non-parametric statistical test used to
compare the distributions of two related or paired samples when the dependent
variable is continuous.
It
is commonly used in the education field to analyze pre-test and post-test
scores or to compare the performance of students before and after an
intervention.
The
Wilcoxon signed-rank test is used when the assumptions of parametric tests,
such as the paired t-test, are not met. It does not assume that the data
follow a specific distribution and can be applied to ordinal, interval, or
ratio data
|