Class Notes (1,100,000)
CA (650,000)
SFU (10,000)
PSYC (1,000)
Lecture 1

# PSYC 210 Lecture Notes - Lecture 1: Statistical Inference, Descriptive Statistics, Standard Deviation

Department
Psychology
Course Code
PSYC 210
Professor
Snjezana Huerta
Lecture
1

Page:
of 8
Topic I: Introductory Material
â€˘ Syllabusâ€“ Introductions
â€“ Course objectives and organizationâ€“ Creating an environment conducive to learning â€“ Student
deliverables and evaluationâ€“ Course schedule
â€˘ Describing Data â€“ Terminology
â€“ Format of datasets
â€˘ Measurement in Psychologyâ€“ Stevensâ€™ scales of measurementâ€“ Data transformations (& their
implications) â€“ Some issues and considerations
Statistics
The word statistics is often used to refer to the:1. Set of procedures or rules used to summarize
some
characteristic or relation in a sample and 2. Resulting outcomes(The term does not refer to the
data.)
There are two classes of statistics covered in this course:
â€˘ Descriptive statistics: Describe and summarize the data
â€˘ Inferential statistics: Characteristics of the sample are used to make inferences about the
populationâ€™s characteristics
Describing Data: Some Distinctions
â€˘ Parametersversusstatisticsâ€˘ Variablesversusconstantsâ€˘ Dichotomousversuspolytomousvariables
â€˘ Discreteversuscontinuousdataâ€˘ Quantitativeversusqualitativedata
Describing Data: Some Distinctions
PARAMETER /
STATISTIC
A numerical quantity that summarizes a characteristic of the population
A numerical quantity that summarizes a characteristic of the sample
find more resources at oneclass.com
find more resources at oneclass.com
-denoted using Roman letters e.g., the mean level of some variable, X, at the
-denoted using Greek letters
population level (ÎĽX) and for a sample (X )
This is an unique identifier, assigned by the researcher to link related information and maintain
anonymity
Describing Data: Some Distinctions
ID Group
VARIABLES and CONSTANTS
Age(G) (X; in mo.)
Math (M/100)
ScaleUp (C)
Variables
Constant
Describing Data: Some Distinctions Dichotomous versus polytomous variables
â€“ Dichotomous Variables: only 2 response options
e.g.:â€˘ pass/failâ€˘ yes/noâ€˘ clinical/notclinical â€˘ 0/1
â€“ Polytomous Variables: more than 2 response options
e.g.:
â€˘never/sometimes/always
â€˘blonde/red/brunette/black/grey/white/other
â€˘0/1/2/3/4/5/6/7
Describing Data: Some Distinctions
Discrete Data
find more resources at oneclass.com
find more resources at oneclass.com
Continuous Data
â€˘ Attributes or objects that can be represented with a scale consisting of whole units only
â€˘ Attributes or objects that can be represented with a scale that includes fractions or parts of a
whole
(no fractions)
e.g., categories, frequencies or counts
e.g., weight, height, relative frequencies or proportions
Describing Data: Some Distinctions
Qualitative Data
Quantitative Data
â€˘ Cases are differentiated in terms of two or more qualities or categories
â€˘ Cases are differentiated in terms of the quantities of an attribute they possess
â€˘ Measurement in Psychologyâ€“ Stevensâ€™ scales of measurementâ€“ Data transformations (& their
implications) â€“ Some issues and considerations
Measurement in Psychology What is Measurement?
â€˘ The systematic assignment of numbers to phenomena of interest (e.g., objects or responses; see
Howell, 2007; Stevens, 1936, 1946)
â€˘ The ordering of data using a particular scale; that is, we seek â€śa scale adequate to the
measurement of the [phenomenon of interest]â€ť (Stevens, 1936, p. 405)
â€˘ There are different systems of rules for assigning numbers depending on the kind of
information collected; Stevens (1946)Ń° identified four scales of measurement
Ń°Stevens wrote a number of articles on the issue of measurement; this is but one.
Measurement in Psychology Why Measure?
â€˘ For the purposes of representing and, possibly, comparing phenomena â€“
â€śtorepresentaspectsoftheempiricalworldâ€ť(Stevens,1946,p.677)â€“