Summary
Psychology 253 content summary
- Course
- Psychology 253
- Institution
- Stellenbosch University (SUN)
Brief and in-depth summary of all content covered in psychology 253 lectures and textbook, including examples
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Psychology 253:Chapters 1-6
Notes by Layla
,Chapter 1
INTRODUCTION TO STATISTICS
The term statistics refers to a set of mathematical procedures for organizing, summarizing, and
interpreting information. Statistics is the study of data and its related aspects such as collection,
analysis, presentation, etc.
Specifically, statistics serve two general purposes:
1. Statistics are used to organize and summarize the information so that the researcher can see
what happened in the research study and can communicate the results to others.
2. Statistics help the researcher to answer the questions that initiated the research by determining
exactly what general conclusions are justified based on the specific results that were obtained.
It is ultimately concerned with trying to draw meaning from data.
IMPORTANT DEFINITIONS
- A population is the set of all the individuals of interest in a particular study.
- A sample is a set of individuals selected from a population, usually intended to represent the
population in a research study.
- A variable is a characteristic or condition that changes or has different values for different
individuals.
- Data (plural) are measurements or observations. A data set is a collection of measurements or
observations. A datum (singular) is a single measurement or observation and is commonly called
a score or raw score.
- A parameter is a value—usually a numerical value—that describes a population. A parameter is
usually derived from measurements of the individuals in the population.
- A statistic is a value—usually a numerical value—that describes a sample. A statistic is usually
derived from measurements of the individuals in the sample.
STATISTICAL METHODS
DESCRIPTIVE STATISTICS
Descriptive statistics are statistical procedures that are used to simplify and summarize data.
Descriptive statistics are techniques that take raw scores and organize or summarize them in a form
that is more manageable. Descriptive statistics are a vital first step in interpreting research data since
describing data is always the first step in any data-analysis method.
Often the scores are organized in a table or a graph so that it is possible to see the entire set of
scores. Alternatively, a set of scores are summarised by computing an average. No matter the
amount of scores, the average provides a single descriptive value for the entire set.
INFERENTIAL STATISTICS
Inferential statistics consist of techniques that allow us to study samples and then make
generalizations about the populations from which they were selected.
1
,Because populations are typically very large, it usually is not possible to measure everyone in the
population. A sample is often selected to represent the population. By analyzing the results from the
sample, researchers try to make general statements about the population. Typically, researchers use
sample statistics as the basis for drawing conclusions about population parameters.
However, although samples are generally representative of their populations, a sample provides
only limited information about the population. Thus, there usually is some discrepancy between a
sample statistic and the corresponding population parameter. This discrepancy is called sampling
error, and it creates the fundamental problem that inferential statistics must address.
VARIABLES
Variables can be quantitative or qualitative. Qualitative variables are called categorical variables.
Quantitative variables are measured on an ordinal, interval, or ratio scale and give us quantitative or
measurement data. On the other hand, qualitative variables are measured on a nominal scale and
give us qualitative or categorical data.
Variables can be dependent or independent. Independent variables are manipulated by the
experimenter and dependent variables are measured from the participants.
Variables can also be continuous or discrete. A discrete variable consists of separate, indivisible
categories. No values can exist between two neighbouring categories. A continuous variable, on
the other hand, has an infinite number of possible values that fall between any two observed values.
A continuous variable is divisible into an infinite number of fractional parts.
A continuous variable can be pictured as a continuous line. Since there are an infinite
number of possible points on the line without any gaps, it is always possible to find a third
value that is between any two points on the line. Two other factors apply to continuous
variables:
1. When measuring a continuous variable, it should be very rare to obtain identical
measurements for two different individuals. If the data show a substantial number of tied
scores, then you should suspect that the measurement procedure is crude or that the variable
is not really continuous.
2. When measuring a continuous variable, researchers must first identify a series of
measurement categories on the scale of measurement. Measuring weight
to the nearest pound, for example, would produce categories of 149 pounds,
150 pounds, and so on. To differentiate a weight of 150 pounds from the surrounding values
of 149 and 151, we must set up boundaries on the scale of measurement. These boundaries
are called real limits and are positioned exactly halfway between adjacent scores. Thus, a
score of 150 pounds is actually an interval bounded by a lower real limit of 149.5 at the
bottom and an upper real limit of 150.5 at the top. Any individual whose weight falls
between these real limits will be assigned a score of 150.
SCALES OF MEASUREMENT
Any data collection requires that we make measurements of our observations. These involve
assigning individuals or events to categories. The categories can be names, places, gender or sex,
employed/unemployed, etc. Categories can also take the form of numerical values, such as inches or
pounds. Categories are used to measure a variable and so the complete set of categories makes up a
scale of measurement.
2
, The relationships between the categories determine different types of scales. There are four different
types of scales.
1. Nominal —
These scales involve labelling and classifying individuals into categories. Examples of categories in
research may include: gender, diagnosis, and whether they belong to the experimental or control
group. For example, if you were measuring the academic majors for a group of college students, the
categories would be art, biology, business, chemistry, and so on. Although the categories on a
nominal scale are not quantitative values, they are occasionally represented by numbers. The
measurements from a nominal scale allow us to deter- mine whether two individuals are different,
but they do not identify either the direction or the size of the difference.
2. Ordinal —
These scales consist of categories which are organised in a sequence by size or
magnitude.Measurements can be taken in terms of rank in class, clothing sizes (S,M,L,XL), or
olympic medals, for example. The fact that categories form a organised sequence means that there is
a relationship between categories.
3. Interval —
Interval scales consist of ordered categories that are intervals of the same size, that is, the interval
between categories are exactly equal. Therefore, the difference between two values is meaningful.
These could be measurements of temperature, IQ, or gold scores (above/below par), for example.
On such a scale the zero point is always absent as it is seen as arbitrary.
4. Ratio —
Similar to interval scales, ratio scales also consist of ordered categories with equal intervals
between categories. The difference is that ratio scales have an absolute zero point, and therefore, it
is possible to have a result of 0. Such categories may be the number of correct answers, time to
complete task, increase in height since last year, etc.
DATA STRUCTURES 1-3
DATA STRUCTURE 1: DESCRIPTIVE RESEARCH (INDIVIDUAL VARIABLES)
Descriptive research or the descriptive research strategy involves measuring one or more
separate variables for each individual with the intent of simply describing the individual variables.
It is through statistics, then, that the observed variable is described. Researchers may use categories
and/or numerical variables.
When the results from a descriptive research study consist of numerical scores, such as the number
of hours spent studying each day, they are typically described by the statistical techniques. For
example, a researcher may want to know the average number of meals eaten at fast-food restaurants
each week for students at the college. Non-numerical scores are typically described by computing
the proportion or percentage in each category. For example, a recent newspaper article reported that
34.9% of American adults are obese, which is roughly 35 pounds over a healthy weight.
DATA STRUCTURE II: THE CORRELATIONAL METHOD
3
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