Methodology

Front 1

Explain empirical definition in experimental setting:

Back 1

Empirical definition (operational definition) is how to measure or observe a object or an event.

Front 2

There are in general two kinds of empirical (operational) definitions:

Back 2

1. Measured:
Describes how a "variable" will me measured

2. Experimental:
Spells out details (operations) of the investigator`s manipulation of a variable. This definition contains clear implications for experimental manipulation.

Front 3

Describe the sequence of hypothesis formulation:

Back 3

Problem--> obstacle---> idea--> hypothesis

A problem is encountered and you intellectually confront it until an idea develops about how the problem works and what is wrong. A hypothesis is stated and testing is possible.

Front 4

Define the concept of measurement:

Back 4

Science is unthinkable without measurement which constitutes numbers to attributes and objects. It stands between theory and experience, and its values can be tested empirically according to some rules.

Front 5

State the permissible mathematical operation of different level of measurement, and state the relations between real numbers and scale values:

Back 5

a) Nominal: Different scores represent different types of the construct, but we cannot say that 3 represents more then 1 (PMM= none)
b) Ordinal: Higher scores represent greater amount of an attribute, but we cannot say that the distance between numbers corresponds to reality. We cannot say how much of a quality an event has. (PMM= equation of unequality)
c) Intervall: Higher scores represents and equal distance between numbers, represents equal distance in reality. But there is no absolute zero and we cannot say an event has x2 more of an attribute than another. (PMM= addition and subtraction)
d) Ratio: Has absolute zero, numbers are ordered, internals between numbers are equal. (PMM=all)

Front 6

In measurement, what are relationship between real numbers and scale values?

Back 6

When a scaling rule is specified and numbers are allocated to properties of an object the numbers turn to scale values.

Front 7

Construct:

Back 7

Is a concept that has been created for scientific purposes (e.g. mass, energy, achievement)

Front 8

Concept:

Back 8

Express an abstraction formed by generalization from particulars.

Front 9

Variables:

Back 9

Is a symbol or a name of a characteristic that takes on a numerical value

Front 10

Relationships between construct, concept and variables:

Back 10

You can convert a construct to a variable by giving it a operational definition. Concepts cannot be measured, variables can.

Front 11

Basic components of scientific orientation: (10)

Back 11

1. Finding general rules by systematically constructing theoretical structures
2. Testing its inner consistency
3. A critical attitude towards findings, and assertions (statements) based on them
4. To actively search for flaws and inconsistencies in one`s reasoning
5. Viewing explanations as tentative stages in a never ending process of successive approximations---> adopting a skeptical attitude towards findings.
6. Making verifiable statements (avoiding metaphysical ones)
7. Willingness to do without answers, when satisfactory ones are not available
8. Accepting the fact that certainty is absurd
9. Ability to encounter competing answers to a question and to test among them
10. Differentiate causal and correlation relationships

Front 12

What is the aim and method of scientific orientation:

Back 12

Aim: Theory

Method:
Produce hypothesis on the grounds of current theory and test them objectively without any personal preconceptions. Systematically eliminating all possible causes except one of them.

Front 13

Scientific orientation should be:

Back 13

Systematic (Organized with a logical sequence through the procedure and collecting of data)
Empirical (subjective beliefs must be checked against objective reality)
Controlled
Critical

Front 14

Classify and describe variables:

Back 14

1. Categorical (qualitative) variable: Classificatory variable--> objects differ in kind, not in degree.
2. Dichotomous or polytomous: e.g. Sex (male, female)
3. Continuos (numerical) variable: Objects differ in degree, not in kind. Distinction can be made "more or less", e.g. depression.

Front 15

Casual relationship, 4 sets of variables may operate:

Back 15

1. Variable that may cause change
2. Outcome variable (effect of the outcome of the variables)
3. Variable which affect the link between cause and effect
4. Variable that are responsible for linking cause and effect

Front 16

External validity:

Back 16

Concernes generalizations from samples to populations:
a) Population validity:
Extent to which the results can be generalized from the sample used in the study to a large group of similar population- target population

b) Ecological validity:
Generalization across populations refers to extent to which the results of an experiment can be extrapolated to different populations or generalized from the set of environment conditions in the experiment, to other environmental conditions.

Front 17

Internal validity, meaning:

Back 17

Control for all influences between the groups being compared in an experiment, except for the experimental group. Anything affecting the control of the design becomes a problem of internal validity.

Front 18

Internal validity: (8)

Back 18

1. Maturation refers to factors that may influence finding of the time elapsed
2. History: events that occur at the same time as the study
3. Testing pre testing may give the subject info
4. Instrumentation refers to changes in instrument, measuring device, changes in those who might be observers or raters
5. Statistical regression presents a thread a thread to internal validity when subjects are assigned to a group because of their extreme scores (low/high)
6. Differential selection is bias in selecting individuals
7. Selection- maturation interaction: failure to select groups of comparable maturity levels
8. Contamination: Bias occurring when researchers have previous knowledge concerning the subjects in the experiment and/ or subjects might be influenced by treatment, lack of treatment, or by his private hypothesis about investigators intentions.

Front 19

Define extraneous variables:

Back 19

They compete with the independent variables and affect the outcome. Variables that haven’t been considered in the research design

4 types (not part of the actual experiment but influence the study):
- Moderator
- Control
- Confounding
- Intervening

Front 20

How would you handle (control) extraneous variables in an experimental setting:

Back 20

1. Elimination
2. Randomization (chose several groups in a random fashion)
3. Take the extraneous variable and build it into the experiment

Front 21

Describe random probability sampling:

Back 21

a) SImple random: equal chance (flipping coin)
b) Stratified random: subdivision of population into homogenous groups from which random samples are drawn (man/women)
c) Cluster sampling: the most used, is the successive sampling of units (country--> city---> region---> hospital---> pasient)

Front 22

Non- random probability sampling:

Back 22

a) Quota: actively putting together representative group
b) Judgmental: who can provide the best information
c) Convenience sampling

Front 23

Formulate:
a) Research hypothesis
b) Research design
c) List potential moderating, control and confounding variables to investigate the following statement: "Fluoxetin in the treatment of postnatal depression"

Back 23

a) (3 steps: From the general problem to research hypothesis to operational hypothesis)
b) (The design is the plan and the structure of the investigation in order to conceive and obtain answers to research question)--> "Treat women + control group)

c) Postnatal depression= dependent
Floexitin= independent variable
Moderator= another medicament
Confounding variable= additional medications that the mother has not told the doctor about

Front 24

Describe the difference between quasi- experimental design and experimental design:

Back 24

Quasi experimental design differs from experimental design by not having random sampling and control group.

Front 25

Define the role of facts in scientific inquiry:

Back 25

A theory should be built on a body of facts. Facts= scientific consensus regarding the nature of a single trait of an object/event or the relationship between objects/events, postulated indisputable basic trait of the world perceived scientifically.
The scientific aim is to discover facts. (Find out facts and add them to the already consisting body of facts)

Front 26

Explain the difference between induction and deduction in scientific inquiry:

Back 26

2 forms of logic science:
1) Deduction: a priory theory is connected to empirical instances "If all P`s are Q`s and this is a P therefore, it is also Q"
(Collective observation---> special case)

2) Induction: individual facts are collected and pulled together to form manageable set of generalizations, which act as a theory. (Special case--> collective observation)

Front 27

Formulate research hypothesis and research design to investigate the following problem: "Is taking estrogen after menopause associated with lower risk of coronary disease?"
List dependent, independent, control and potential intervening and confounding variables:

Back 27

Research hypothesis: "Yes estrogen will cause lower risk of coronary disease"
Research design: Treat women+ control group
Dependent variable: Heart disease
Independent variable: Estrogen
Control variables: Women after menopause
Confounding variables: the disease is caused by age.

Front 28

An independent variable is the presumed cause of the:

Back 28

Dependent variable

Front 29

List levels of measurement:

Back 29

a) Meaning of scale values
b) The permissible mathematical methods (PMM)
c) The limits
d) The relations between real numbers and scale values

Front 30

State the necessary steps for a scale of measurement constitution:

Back 30

Can data be put in order?--> no---> categorical (Nominal)

Do the data have units? --> no --> categorical (Ordinal)

Does the data have true zero? --> no--> Interval

Ratio---> Do the data come from measuring or counting things?
Measuring= Continuos
Counting= Discrete

Front 31

Basic types of research objectives:

Back 31

1. Evaluation of measurement instruments (needed if publishing research report)
2. Description of population or some clinical phenomenon, no fixed design
3. Exploration of relationships (determine how clinical phenomenas interacts) e.g. factors suspected to influence lower back pain
4. Comparison between groups (attempting to define a cause of an effect between variables)- we have some hypothesis about it and are trying to manipulate it

Front 32

Two main characteristics of hypothesis:

Back 32

1. Hypothesis is declarative and states relationship between independent or dependent variables
2. It must be able to be tested and its variables must be observed

Front 33

Special criteria for a hypothesis evaluation:

Back 33

1. Should use a technical language
2. Readers should understand the variables that are being used
3. Readers should understand the characteristics of the variables
4. Readers should understand the population that is beings used and the outcome

Front 34

Mnemonic for linear sequence in research:

Back 34

Down Falls Doris---> Superficial Groin Sprain--> Doctor Rychle

Front 35

Down Falls Doris--> Superficial Groin Sprain--> Doctor Rychle

Back 35

1. Define a research problem
2. Formulate hypothesis
3. Design study
4. Select samples and instruments
5. Gather data
6. Statistically analyse the data
7. Draw conclusions
8. Report results