Enhance your grasp of quantitative methods with this comprehensive slide presentation. Explore essential concepts and techniques in quantitative analysis that are vital for decision-making in various fields, including #Statistics, #DataAnalysis, #QuantitativeResearch, and #BusinessAnalytics. #QuantitativeMethods #Statistics #DataAnalysis #QuantitativeResearch #BusinessAnalytics #DataVisualization #SamplingMethods #HypothesisTesting #RegressionAnalysis #QuantitativeModeling #ShortcutToSuccess

1. Shortcut
understanding about
quantitative methods
NUR FITHRIYAANI BT RASHIDI
UNIVERSITI MELAKA
DEGREE IN PSYCHOLOGY

2. PURPOSE OF
USING
QUANTITATIV
E METHOD
1.measure differences
between groups
2.assess r/ships between
variables
3.to test hypothesis
scientifically
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3. 2 methods we can
choose in
QUANTITATIVE
METHOD:
1. DESCRIPTIVE STATISTICS
2. INFERENTIAL STATISTICS

4. DIFFERENCES
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DESCRIPTIVE STATISTICS INFERENTIAL STATISTICS
Desc. stats :
-focus content of the sample
-describing detail of the
sample
eg: mean, median, mode,
SD, skewness
Inferential stats :
-aim to make predicitions about a population
-make predictions about differences between 2 or
more groups
-study the r/ships between 2 or more variables -test
the hypothesis to predict
eg: sampling will become a problem to analyze if u
study gender between male and female but the
sampling is 20% male and 80% female.
it needs to be equal : like this :- 50% female 50%
male

5. 4 TYPES OF INFERENTIAL STATISTIC
METHODS
T-TESTS
to assess difference
between groups
-in terms of comparing
Mean, median, Standard
deviation, skewness by
using it to truly see what
is significantly different
ANOVA
To assess differences
between multiple groups
CORRELATION
ANALYSIS
to assess the r/ship
between variables .
eg: if variable A goes
up... does variable B
goes down?
REGRESSIO
N ANALYSIS
to assess the causal
relationship between
variables.
eg: does variable A's
movement, causes
variable B's movement?
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6. Q: How to choose between the 4 types of
inferential methods?
A : Consider 2 factors -
a) Data types and shapes
• -to discuss this is to understand the
levels of measurement first.
• eg: data types ----- categorical data
type : YES/NO questions , numerical
data type : age, weight
• eg: shape of data ---------- normal
distribution : centred in the middle ,
skewed to the left and the right
skewed distribution: has a negative or
positive skew.
• (from the Descriptive statistics , it
helps u know the shape of your data
earlier)
b) Research questions and hypothesis
• If you want to only understand the
attributes of my sample and not the entire
population, use descriptive statistics.
• -but if you aim to understand :
(differences between groups) or
(relationships between variables) and
(predict outcomes in the population),
you will need to use both descriptive
statistics and inferential statistics.
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7. Example of
normal, positive,
negative skew for
data shapes
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Normal skew

8. Thank you
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NUR FITHRIYAANI BINTI
RASHIDI
Email address
Website