MindMap Gallery Design mind mapping for educational research
The design mind map about educational research includes the basic content of research design, research design standards, research object sampling design, etc. Welcome everyone to like and follow~
Edited at 2023-11-05 19:14:44This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
Educational research design
Basic content of research design
Clarify the purpose of the research and select research objects (who to choose for research)
Choosing research methods and design methods (how to research
Determine research variables and observation indicators (what to study
Choosing research tools and materials (what research to use
Developing research procedures and selecting the research environment (where to conduct research
Statistical methods for data sorting and analysis (what to do after research is completed)
Research design standards
reliability
Meaning: Reflects the consistency of the research tool’s measurement results on the research object
Influencing factors
research tool
Subject
main test
Research design aspects
Research implementation aspects
Classification
stability
homogeneity
Valuation method
Repeat method
similarity method
inter-rater reliability
Degree of internal consistency (split-half method)
Ways to improve valuation
Increase the number of questions
Questions are of moderate difficulty
Homogeneous content
Unification of procedures
plenty of time
Rating is objective
The subject's physical and mental condition was normal
Reliable research tools
validity
Meaning: Whether the research results accurately reflect the actual situation of the research object, indicating the accuracy, reliability and validity of the research results
Classification
construct validity
Meaning: The rationality of theoretical conception and the appropriateness of its transformation into new abstract and operational definitions
Influencing factors
Concept lacks explanation
Single method
Differences in ideation levels
Psychological impact on subjects and subjects
Improvement measures
Rigorous theoretical conception
Strict specification of variables
Operationally define variables and develop objective measurement indicators
Eliminate influencing factors
internal validity
Meaning: The degree of clarity that there is a certain relationship between the independent variable and the dependent variable
Influencing factors
Subject matured
subject selection bias
Subject loss
history
Measurement means
pretest effect
statistical regression
among various factors
Improvement measures
Normative manipulation of independent variables
Effectively control irrelevant variables
scientific observation dependent variable
Statistical conclusion validity
Meaning: Validity of data analysis and methods of research results
influencing factors
Poor data quality
Violation of assumptions of statistical tests
Low statistical power
Improvement measures
Ensure data quality
Clarify the basic assumptions of statistical testing
external validity
Meaning: summary, representativeness, and generalizability of experimental results
factor
Poor representativeness of subjects
Variable operations are not clearly defined
Research side effects on subjects
Multiple experiments deal with interference
Pre-test and experimental treatment
Subjects and experimental treatments
Research and practical scenarios
promote
Subject representative
Scenario similarity repeatability experiment
Ecological validity: The extent to which research results generalize and apply to other contexts
Population validity: The degree to which it applies to the population from which the subjects are drawn
The relationship between reliability and validity
Reliability is the basis of validity. Validity is the purpose of reliability. To have validity, it has certain reliability. To have reliability, it does not necessarily have validity.
Research object sampling design
The meaning and principles of sampling design
Various concepts of sampling design
overall
sample
sample size
sampling
significance
Solve the difficulties that make overall research difficult to conduct
Save manpower, material and financial resources
Improve the accuracy and depth of research results
Reduce the pollution atmosphere of research and improve the scientific nature of research
in principle
randomness of sampling
representativeness of sampling
overall prescriptiveness
Reasonable sample size
General procedures for sampling
stipulate overall
Specify sample size
Determine sampling method and take samples
statistical inference
Basic methods of sampling
random sampling
simple random sampling
Random principle, independent of each other
Method: lottery method, random number table method
Applicable conditions: large sample size, low heterogeneity, small overall number
Excellent: Convenient, guaranteed representativeness, easy to determine sampling error
Disadvantages: Numbering is time-consuming, laborious, heterogeneous, and error-prone
Systematic sampling
at certain intervals
Method: arrange numbers, determine sample size, determine interval, select starting point for sampling
Applicable conditions: large sample
Excellent: systematic, decentralized, guaranteed representativeness
Missing: corresponds to periodicity, serious error
Stratified sampling
Stratified by characteristics
Method: overall stratification, determining proportions, sampling, small differences within layers, large differences between layers
Applicable to: complex ingredients with large differences
Excellent: Reduce errors and allow human control
Missing: Need to know well
cluster sampling
The population is divided into layers and groups, randomly, and all members are samples
Method: overall number, random sampling
Applicable: Large overall range and low heterogeneity
Advantages: Convenient, centralized processing, labor-saving
Deficiency: The sample distribution is uneven, the representativeness is poor, the error is large due to large differences, and the accuracy is low.
multi-stage sampling
The process is divided into two stages
Method: Group level one, group level two
Applicable to: large overall, multiple units and complex situations
Advantages: multiple research methods, simple and easy to implement, saving money
Disadvantage: Sampling error is large
non-random sampling
Convenient for sampling
Principle of convenience, uncertainty of probability, lack of understanding
Advantages: saving, simplicity
Lack of representativeness, low rigor
purposive sampling
Judge for yourself
Familiar area
Low representation and lack of rigor
criterion sampling
stratified purposive sampling
opportunity sampling
homogeneous sampling
extreme case
typical
Determination of sample size
Generally speaking, the larger the sample size, the smaller the sampling error
The nature of the population under study
Research purpose and subjective and objective conditions
Study the maximum error allowed and the probability of inference errors
Determination of research variables and design of research indicators
variable type
Variable relationships: related variables, causal variables
Research objects: subject variables, object variables
Whether to measure directly: directly measured variables, indirect measured variables
Whether the researcher actively manipulates: operational variables, non-operational variables
Whether to become the object of research operations: research variables, non-research variables
Meaning: Characteristics of events, conditions, and phenomena that can be manipulated in nature and quantity.
Selection of research variables
Determine research variables according to research purpose
Identify extraneous variables
Determine the number of study variables
Consider the levels of study variables
Research indicators and their measurement levels
Meaning: Items that specifically measure the categories, status, horizontal speed and other characteristics of research variables
Qualitative indicators
Property categories, representing different categories
Quantitative indicators
Sequential degree, no absolute zero, no identical units, comparison
distance indicator
Quantitative differences and separation distances, without absolute zero, have the same units
fixed ratio index
Proportional ratio, with absolute zero point
Principles of research indicator design
Theoretical guidance
integrity
Simplicity
feasibility
operability
Design of operational definitions of research variables and indicators
Operational definition and its characteristics
Perceivable and measurable, with specific definitions of variables or indicators
Content: Use specific things to explain
Method: direct perception and measurement (empirical method
Focus: extension and operation process
abstract definition
The common essence of variables and indicators
Concept Note
Logical method
reveal the essence
The role of operational definitions
Improve research objectivity
testing of research hypotheses
Improve uniformity
Comparability of research results
Testing and replication of research evaluation results
Principles of Operationally Defined Design
Symmetry: operation and pumping are symmetrical, pumping determines operation, and operation is the concrete manifestation
unique
Operationally defined design methods
method and procedure description
dynamic characterization method
static characterization method
Choice of irrelevant variable control methods
Meaning: In addition to independent variables, all variables that can affect the dependent variable and interfere with the experiment
category
main test
Subject
experimental design
Experiment implementation environmental conditions
data processing
Two effects
Causing inconsistencies in experimental results
Is the error effect constant?
Random error: caused by accidental random variables, difficult to control, inconsistent results for the same thing, and uncertain direction
system error
Causing inaccuracies in experimental results
Control of irrelevant variables
elimination method
constant method
Equilibrium method
statistical control
Blind method
offset method
random method
other variables
independent variable
dependent variable
Moderator
mediating variable
irrelevant variables
extra variables