Please enable JavaScript.
Coggle requires JavaScript to display documents.
Research Statistical Analysis - Coggle Diagram
Research
Statistical
Analysis
(1) write hypotheses and plan the research design using quantitative data
research tool
investigating trends, patterns, and relationships (e,g, cause-and-effect relationship, potential correlation between variables) about the data
research design
strategy for data collection and analysis
determine the statistical test in order to test the hypothesis later
types of design
descriptive
study the characteristics or phenomenon
correlational
relationship between variable
experimental
cause-and-effect
comparation
group level
between-subjects design
individual level
within-subjects design
both
mixed (factorial) design
level of measurement
categorical
data representing
groupings
nominal (e.g. gender)
ordinal (e.g. ability)
quantitative
data represents
amounts
interval salce (e.g. test score)
ratio scale (e.g. age)
(2) collect data using research process
specific hypotheses
make decision
research design, sample size, sampling procedure
sampling approaches
probability
sampling
random selection (non-biasness)
parametric tests can make strong statistical inferences using probability sampling (ideal sample)
non-probability
sampling
voluntary self-selectioin
sample size (min 30)
(3) summarize data using descriptive statistics
collect data from sample -> organise and summarise data
organizing data in frequency distribution table
displaying data in a bar chart
visualising the relationship
using scatter plot
normal or skew distriibution
calculate central tendency
mode
median
mean
(4) test hypotheses and make estimates with inferential statistics
formally test hypotheses and make estimation about the population
null
hypothesis
no effect
alternative
hypothesis
improvement
2 types of
estimations
point estimate
best guess
interval estimate
range of best guess
hypothesis testing
test statistic
how much value differs
from null hypothesis
p value
likelihood of obtaining results
if null hypothesis
statistical tests
comparison
difference in outcome
regression
cause-and-effect
relationship between variables
correlation
relationship between
variable without assumption
2 methods of
inferences
estimation
population sampling
hypothesis testing
research predictions
goal of research
to investigate a relationship between variables within a population
starts with a prediction, use statistical analysis to test the prediction
(5) interpret and generalise
findings/results
statistical significance to form conclusion
compare p value to a significance level (0.05)
effect size for the sampling