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Psych stat reviewer sem 2

Psych stat reviewer sem 2

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REVIEW

TYPES OF ANALYSIS:

Descriptive – limited to the

description of the particular group

being studied.

- a conclusion cannot be applied to

cases outside the study group.

Inferential – application of the

findings or conclusions from a small

group to a large group from which

the smaller group was drawn.

TYPES OF VARIABLES:

1. Qualitative/Categorical

Attributes are in terms of categories.

Example:

• sex - Male / Female

• religious affiliation: Roman

Catholic / INC / Baptist / Islam /

ETC.

2. Quantitative/Numerical

Attributes are in terms of counts or

measurements Distinctions:

a. Discrete Variable

- uses the process of counting to

generate data

- values of attributes are in terms of

whole numbers only

Example: Number of t-shirts

owned

b. Continuous Variable

uses the process of measuring to

generate data.

- values of attributes may have

fractional or decimal parts.

Example: Age

LEVEL OF MEASUREMENT

Measurement – The process of

assigning numbers to observations.

SCALES OF MEASUREMENT

1. Nominal Level

- Consists of numbers which indicate

categories for purely classification

purposes.

- The categories are mutually

exclusive and exhaustive.

Example:

Sex: M = 1

F = 2

2. Ordinal Level

- Possesses rank order

characteristics

- The categories must still be

mutually exclusive and exhaustive,

but they also indicate the order of

magnitude of some variable

- Precise differences between ranks

do not exist

Example:

Likert-type scale

Strongly agree = 1

Agree = 2

Indifferent = 3

Disagree = 4

Strongly disagree = 5

3. Interval Level

- Has all the properties of the ordinal

scale

- A given interval (distance) between

scores has the same meaning

anywhere on the scale

- Intervals provide information about

how much better one value is

compared with another

- Has no absolute zero

Example:

IQ – there is a meaningful

difference between an IQ of 110

and 109 but the test does not

measure people who have no

intelligence.

Ratio Level

- Possesses all the characteristics of

the interval scale

- Has a true or absolute zero point

- The ratio of two values is

meaningful

Example: distance

ANALYTIC GOALS

- directed toward finding out from the

data one or more of the following

attributes or characteristics of the

group being studied:

1. Central tendency – general

characteristic of the group

Examples:

a. To determine the mean weekly

allowance of USLS College

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Freshmen for the second semester,

AY 2014 – 2015.

b. To determine the percentage of

USLS College students who prefer a

Samsung over a Nokia cellphone.

2. Variance in the group – how

individual members of the group vary

from the average characteristic of

the group.

Examples:

a. To determine the age range of the

students in Statistics class.

b. To determine if the Statistics final

grades of the students in a particular

class are similar.

3. Difference within the

group/between groups – whether

or not subgroups of the group/ two

separate groups being studied are

different or similar on certain traits

investigated.

Examples:

a. To compare the mean no. of Coke

sakto bottles consumed in a week

between the male and female USLS

students.

b. To determine if there is a

significant difference in the mean

number of text messages sent in a

day among the students from the five

different colleges of USLS.

4. Relationships within the group

– if relationship between certain

variables covered in the study exist

Examples:

a. To establish if there is a significant

relationship between choice of

cellphone brand and the college a

USLS student belongs to

b. To determine if relationship status

and final grades in Statistics are

independent.

5. Prediction – establishing a

mathematical/statistical model to

predict future outcomes.

Examples:

a. What factors influence the a

graduate’s ability to land a job within

one year after graduation?

b. What is the estimated sales of a

particular restaurant for next week if the

present conditions hold?

INTRODUCTION TO INFERENTIAL

STATISTICS

BRANCHES OF STATISTICS

The study of statistics has two major

branches: descriptive statistics and

inferential statistics.

Descriptive Statistics - Involves the

organization, summarization, and

display of data.

Inferential statistics - Involves using a

sample to draw conclusions about a

population.

POPULATIONS VS. SAMPLES

Population

- The complete set of individuals

- Characteristics are called parameters

Sample

- A subset of the population

- Characteristics are called statistics.

- In most cases we cannot study all the

members of a population

CONCEPTS

Statistical inference is the act of

generalizing from a sample to a

population with calculated degree of

certainty.

PARAMETERS AND STATISTICS

It is essential that we draw distinctions

between parameters and statistics.

A parameter is a numerical description

of a population characteristic.

A statistic is a numerical description of

a sample characteristic.

Parameter

Population, µ, σ, p

Statistic = Sample, x̄ , s, p

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REVIEW Example: Sex: M = 1 F=2 TYPES OF ANALYSIS: Descriptive – limited to the description of the particular group being studied. - a conclusion cannot be applied to cases outside the study group. Inferential – application of the findings or conclusions from a small group to a large group from which the smaller group was drawn. TYPES OF VARIABLES: 1. Qualitative/Categorical Attributes are in terms of categories. • • Example: sex - Male / Female religious affiliation: Roman Catholic / INC / Baptist / Islam / ETC. 2. Quantitative/Numerical Attributes are in terms of counts or measurements Distinctions: a. Discrete Variable - uses the process of counting to generate data - values of attributes are in terms of whole numbers only Example: owned Number of t-shirts b. Continuous Variable uses the process of measuring to generate data. - values of attributes may have fractional or decimal parts. 2. Ordinal Level Possesses rank order characteristics - The categories must still be mutually exclusive and exhaustive, but they also indicate the order of magnitude of some variable - Precise differences between ranks do not exist Example: Likert-type scale Strongly agree = 1 Agree = 2 Indifferent = 3 Disagree = 4 Strongly disagree = 5 3. Interval Level - Has all the properties of the ordinal scale - A given interval (distance) between scores has the same meaning anywhere on the scale - Intervals provide information about how much better one value is compared with another - H ...
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