Reseach And Design Flashcards
1
Q
Characteristics of a research study
Develop an idea into testable hypothesis
A
Defining the relevant variables and identifying the target population
2
Q
Characteristics of a research study
Choosing an appropriate research design
A
Choose appropriate research strategy and specific research design
3
Q
Characteristics of a research study
Selecting a sample
A
Determine how the sample will be selected from the population
4
Q
Characteristics of a research study
Conducting the survey
A
Investigator conduction studies and collects and records the data
5
Q
Characteristics of a research study
Analyzing to obtain data
A
Appropriate descriptive and inferential statistics techniques
6
Q
Characteristics of a research study
Reporting the results
A
Prepares a report of the research results
7
Q
Variable
A
Any characteristic behavior event or phenomenon that is capable of bearing or existing in at least two different states conditions or levels
8
Q
Constant
A
When a characteristic is restricted to a single state or condition
9
Q
Independent variable
A
Believe to affect or alter status of another variable
10
Q
Dependent variable
A
Referred to as treatment or intervention is symbolize with X. Is considered the outcome of the treatment
11
Q
Manipulated variables
A
Are considered the independent variable’s
12
Q
Organismic variables
A
Variables that cannot be controlled by the researcher or independent variable
13
Q
Nonexperimental research
A
Primarily to collect data about variables rather than to test hypothesis
14
Q
Observational studies
A
Observing behavior in a systematic way often in a naturalistic settings
15
Q
Case studies
A
Associated with an in-depth description and analysis of a single person also can entail an intensive investigation of a single institution agency community or social unit
16
Q
Surveys
A
Administering a questionnaire either in person or by phone or through mail subject to nonresponse bias
17
Q
Random assignment
A
Helps ensure that any observed differences between groups on the dependent variable or actually due to the effects of the independent variable
18
Q
Quasi-experimental research
A
Does not provide the investigator with the same degree of experimental control
19
Q
Ex post facto research
A
Involves assessing the effects of an independent variable after it has occurred
20
Q
Developmental research
A
Conducted to assess changes that occur as function of time
21
Q
Cross-sectional studies
A
Evaluate change over time by comparison groups of people of different ages at the same point in time
22
Q
Longitudinal studies
A
Investigate changed by assessing people belonging to the same age group over an extended period of time
23
Q
Cohort (generational) affects
A
Are occurring when observed differences between subjects of different ages are due to differences in experience or other factors rather then to increasing age
24
Q
Cross sequential (cohort sequential) study
A
Combines cross-sectional and longitudinal methods by assessing members of two or more age groups at two or more different times
25
Simple random sample and
Every member of the population has an equal chance of being included in the sample
26
Stratified random sampling
When the population of interest Sperrys in terms of specific strata or characteristics that are relevant to the research hypothesis and Vesta gate or can you stratified random sample and to ensure that each stratum is represented
27
Cluster sampling
Sampling entails select units or clusters of individuals rather than individuals and either including all individuals in those units in research study or randomly selecting individuals from each unit
28
Control group
Either no treatment control group or a placebo controlled group
29
Placebo control groups
Are exposed to nonspecific factors that are not uniqueto a particular therapy but are common to most
30
Basic questions
Is there a relationship between the independent and the dependent variable?
If so is the relationship a causal one?
31
Three factors that can cause variability in the studies dependent variable
The independent variable or experimental variance
Systematic error due to extraneous variables and
Random error due to random fluctuations in subjects experimental conditions and methods of measurement
32
True experimental study
Investigators control is maximized
33
True experimental research
Enhances a researchers ability to maximize their ability due to the independent experimental variable
34
Extraneous variable or confounding variable
Source of systematic error a very bold that is a relevant to the purpose of the research
35
Random assignment of subjects to treatment group
Equalize is the effect of all known and unknown extraneous variables
36
Holding the extraneous variables constant
Selecting subjects were homogeneous
37
Matching subjects on extraneous variables
Making the groups equivalent in terms of the extraneous variables
38
Building the extraneous variables into study
Including it in the study as an additional independent variable
39
Internal validity
The extent that it provides accurate answers to the first two research questions
40
External validity
The degree that it produces an accurate answer to the third question. Can the relationship between independent independent variables be generalized to other people setting times and operations?
41
Basic research questions
Is there a relationship between the independent independent variable?
If so, is the relationship a casual one? Can the relationship between independent and dependent variables be generalized to other people, settings, times and operations?
42
A study has Internal validity
When it allows an investigator to determine if there is a causal relationship between independent and dependent variables
43
Threats to internal validity
| Maturation
Any biological or physiological change that occurs within the subject during the course of the study as a function of time
Best controlled to include more than one group
44
Research
Defined as the empirical systematic investigation of the relationship between two or more variables
45
Threats to internal validity
| History
History threatens the studies internal validity when an external event systematically affects the status of the subjects on the dependent variable
Best controlled by including more than one group
46
Threats to internal validity
| Testing
Taking a test can alter person's performance on the test when it is re-administered
47
Instrumentation
Changes in accuracy or sensitivity of the measuring device or procedures during the course of study can confound the study results
48
Threats to internal validity
| Statistical regression
The tendency of extreme scores on a measure to regress toward the mean when the measure is readministered to the same group of people
49
Regression of the mean
Statistical regression is sometimes thought to be synonymous with Galton notion
50
Threats to internal validity
| Selection
Is a threat to his studies internal validity whenever the method used to assign subjects to treatment groups result in systematic differences between the groups at the beginning of the study
51
Threats to internal validity
| Attrition our mortality
When subjects who dropped out of one's group differ in an important way from subjects who dropped out of other group
52
Threats to internal validity
| Interaction with selection
Groups are initially non-equivalent, selection can act alone and/or can interact with other factors to threaten the studies internal validity
53
External validity
Has validity when it's finding can be generalized to other people, settings, and conditions.
54
Threats to external validity
| Interaction between testing and treatment
The administration of a pretest can sensitize subject to the purpose of the research study and thereby alter their reaction to the independent variable
55
Threats to external validity
| Interactions between selections and treatment
Subjects and food in a research study can have characteristics that make them responded to the independent variable in a particular way
56
Threats to external validity
| Reactivity – reactive arrangements
Research participants can respond to an independent variable in a particular way simply because they know their behavior is being observed
57
Hawthorne effect
Tendency of subjects to perform better because of attention they are receiving as a research participant or evaluation apprehension
58
Demand characteristics
Behavior of research participants can be altered by cues and experimental setting that inform the subject of the purpose of the study or suggest what behaviors are expected of them
59
Between group designs
Used the effect of different level of an independent variable are assessed by administering each level to a different group of subjects and then comparing the status or performance of the groups on the independent variable
60
Factorial design
Whenever a set includes two or more independent variables
61
Within subject design
All levels of the independent variable are administered sequential to all subjects
62
Single group timeseries design
One type of within subject design
63
Mixed designs
Combines between groups and within subject methodologies
64
Single subject design
Combines behavioral principles with the technique of experimental psychology to solve socially – relevant problems
65
Characteristics of single subject designs
Includes at least one baseline – no treatment phase and one treatment phase. Helps control any maturation affects
66
A B design
A. Phase and single treatment
| B. Phase
67
Reversal design ABA, ABAB, etc.
The A.B. design can be expanded to include more than one baseline phase or more than one baseline and more than one treatment phase the extension of the A.B. design are called reversal or withdrawal designs
68
Multiple baseline design
Maybe unethical
Does not require withdrawal of treatment during the course of the study but instead involve sequential applying the treatment either two different behaviors of the same subject called multiple baseline across behaviors or to the same subject in a different setting
69
Formative evaluation
Obtaining the information needed to determine if the program is being implemented as intended or whether any modifications are needed so that the program can achieve its objectives
70
Summative evaluations
Entails assessing the program is affecting us in determining if the program should be continued or expanded
71
Continuous variable
Theoretically can take an infinite number of values on the measurement scale
72
Discrete variable
Can assume only a finite number of values
73
Qualitative variable
Places people in unordered categories
74
Quantitative variable
Permits comparison of people in terms of order
75
Nominal scale
Measurement divides variables into an ordered categories i.e. sex of sales people
76
Ordinal scale
More mathematically complex than a nominal scale. Divides observations into categories but also provides information on order of those categories. Likert type scale one for strongly agree seven for strongly disagree
77
Likert scale
A forced answer scale ranging categories from 1 to 10
78
Interval scales
Has a property of order as well as the property of equal intervals between successive points on the measurement scale e.g. IQ tests are usually considered a representative of the interval scale
79
Ratio scale
Most mathematically complex of the four measurement scales. It has properties of order and equal intervals as well as properties of an absolute zero point.makes it possible to multiply and divide racial scores to determine more precisely how much more or less of a characteristic one person has to another. Example Kelvin scale
80
Descriptive statistics
Used to describe or samurais a distribution of set data
81
Frequency distribution
Summarizing the data in terms of the number of frequency observations in each case
82
Cumulative frequency
Indicate the total number of observation that falls at or below each category or score
83
Shapes of distribution
| Normal curve
Systematic bell shaped and defined by specific mathematical formula
84
Shapes of distribution
| Kurtisis
Refers to the relative peaked nice hike or flatness of the distribution
85
Shapes of distribution
| Platkurtic
Went to distribution is flatter
86
Shapes of distribution
| Leptokurtic
Went to distribution is peaked
87
Shapes of distribution
| Skewed distribution
More than half of the observations fall on one side of the distribution
88
Shapes of distribution
| Positively skewed distribution
Most of the scores are in the negative low side of the distribution the positive tail is extended
89
Shapes of distribution
| Negatively skewed distribution
Most scores are located on the positive high school side of the distribution and the negative tail is extended due to the presence of a few low scores
90
Measures of central tendency
| Mode
The score category that occurs the most frequently
91
Measures of central tendency
| Median
The score the divides a distribution in half when the data has become ordered from low to high. Useful for it it's in sensitivity to outliers and open ended distributions where there are no specific upper and lower limits
92
Multimodal
Two or more scores are categories that occur equally often
93
Bimodal
When two scores are equal
94
Susceptibility to sampling fluctuations
This means that, if a large number of samples are randomly selected from the population, the mold can be expected to vary considerably from sample to sample.
95
Measures of central tendency
| Arithmetic mean
M-X
Mean equals the sum of the means divided by number of means
Used for its lease to susceptibility to sample and fluctuation usually provides an unbiased estimate of the population mean
96
Measure of variability
| Range
Calculated by simply subtracting the lowest score in the distribution from The highest score
97
Measure of variability
| Variance mean square
Is a more thorough measurement of variability then the range because it's calculations includes all the scores in the distribution rather than just the highest and lowest dance is calculated using
98
Measure of variability
| Standard deviation
The standard deviation is calculated by taking the square root of the variance
99
Population parameters and sample statistics
Estimates population values based on obtain samples
100
Characteristics of sampling distribution
Due to the effects of random chance factors, it is unlikely that any sample will perfectly represent the population from which it was drawn
101
Sampling error
inaccuracies
102
Inferential statistics test
Indicates where the obtain samples to distichs falls in the appropriate sampling distribution
103
Rejection region
Region of unlikely values lights on both tales of the sampling distribution
104
Retention region
Regions of likely values lies in the central portion of the sampling distribution
105
Null hypothesis is rejected
Obtain samples statistics is in the rejection region
106
No hypothesis is retained
Statistical tests indicate that the sample statistic lives in the retention region
107
Alfa or level of significance
0.01 or 0.05 represents the sampling distribution in the rejection region and the remaining percentage represents the retention region
108
Type I error = alpha
Occurs when an investigator rejects a true null hypothesis
109
Type II error = beta
Occurs when an investigator retains a false null hypothesis
110
Beta
Directly calculated for a particular study depending on the Alpha
111
Inverse relationship
As the probability of making a Type I error increases the probability of making a Type II error Increases and vice versa
112
Statistical power
A statistical test enables an experimenter to reject a faults Null hypothesis
113
Using a one tailed test when appropriate
One tail test is more powerful than a two-tailed test
114
Using a parametric test
Parametric statistical tests such as a t-test or ANOVA or more powerful than nonparametric test
115
Homoscedacity
The variance of the population that the different groups represented are equal
116
Critical values and degrees of freedom
Test that allows investigator to determine whether the obtains sample value is in the rejection or retention region of the sampling distribution this is done by comparing the tested to sticks to a critical value which is the number that corresponds to the boundary that divides the sampling distribution into rejection retention
117
Chi– square test
Used to analyze the frequency of observation of a nominal variable test
118
ANOVA
Analysis of variance is used to compare two or more means
119
One-Way ANOVA
Is used want to study includes one independent variable and two or more dependent groups
120
Factor analysis of variance
An extension of the one way ANOVA that is employed want to study includes two or more independent variables
121
Assumptions
Use of the Pearson r and most other qualifications require that three assumptions be met
122
Linearity
Assumption that there is a linear relationship between the variables relationship between the X-Men why can be summarized in a straight line
123
Unrestricted range
Use of the Persons r is also based on the assumptions that there is an unrestricted range of scores on both variables
124
Homoscrdasticity
The third assumption is that the range of why scores is about the same for the values of X
125
Threats to internal validity
| History
History threatens the studies internal validity when an external event systematically affects the status of the subjects on the dependent variable
Best controlled by including more than one group
126
Threats to internal validity
| Testing
Taking a test can alter person's performance on the test when it is re-administered
127
Instrumentation
Changes in accuracy or sensitivity of the measuring device or procedures during the course of study can confound the study results
128
Threats to internal validity
| Statistical regression
The tendency of extreme scores on a measure to regress toward the mean when the measure is readministered to the same group of people
129
Regression of the mean
Statistical regression is sometimes thought to be synonymous with Galton notion
130
Threats to internal validity
| Selection
Is a threat to his studies internal validity whenever the method used to assign subjects to treatment groups result in systematic differences between the groups at the beginning of the study
131
Threats to internal validity
| Attrition our mortality
When subjects who dropped out of one's group differ in an important way from subjects who dropped out of other group
132
Threats to internal validity
| Interaction with selection
Groups are initially non-equivalent, selection can act alone and/or can interact with other factors to threaten the studies internal validity
133
External validity
Has validity when it's finding can be generalized to other people, settings, and conditions.
134
Threats to external validity
| Interaction between testing and treatment
The administration of a pretest can sensitize subject to the purpose of the research study and thereby alter their reaction to the independent variable
135
Threats to external validity
| Interactions between selections and treatment
Subjects and food in a research study can have characteristics that make them responded to the independent variable in a particular way
136
Threats to external validity
| Reactivity – reactive arrangements
Research participants can respond to an independent variable in a particular way simply because they know their behavior is being observed
137
Hawthorne effect
Tendency of subjects to perform better because of attention they are receiving as a research participant or evaluation apprehension
138
Demand characteristics
Behavior of research participants can be altered by cues and experimental setting that inform the subject of the purpose of the study or suggest what behaviors are expected of them
139
Between group designs
Used the effect of different level of an independent variable are assessed by administering each level to a different group of subjects and then comparing the status or performance of the groups on the independent variable
140
Factorial design
Whenever a set includes two or more independent variables
141
Within subject design
All levels of the independent variable are administered sequential to all subjects
142
Single group timeseries design
One type of within subject design
143
Mixed designs
Combines between groups and within subject methodologies
144
Single subject design
Combines behavioral principles with the technique of experimental psychology to solve socially – relevant problems
145
Characteristics of single subject designs
Includes at least one baseline – no treatment phase and one treatment phase. Helps control any maturation affects
146
A B design
A. Phase and single treatment
| B. Phase
147
Reversal design ABA, ABAB, etc.
The A.B. design can be expanded to include more than one baseline phase or more than one baseline and more than one treatment phase the extension of the A.B. design are called reversal or withdrawal designs
148
Multiple baseline design
Maybe unethical
Does not require withdrawal of treatment during the course of the study but instead involve sequential applying the treatment either two different behaviors of the same subject called multiple baseline across behaviors or to the same subject in a different setting
149
Formative evaluation
Obtaining the information needed to determine if the program is being implemented as intended or whether any modifications are needed so that the program can achieve its objectives
150
Summative evaluations
Entails assessing the program is affecting us in determining if the program should be continued or expanded
151
Continuous variable
Theoretically can take an infinite number of values on the measurement scale
152
Discrete variable
Can assume only a finite number of values
153
Qualitative variable
Places people in unordered categories
154
Quantitative variable
Permits comparison of people in terms of order
155
Nominal scale
Measurement divides variables into an ordered categories i.e. sex of sales people
156
Ordinal scale
More mathematically complex than a nominal scale. Divides observations into categories but also provides information on order of those categories. Likert type scale one for strongly agree seven for strongly disagree
157
Likert scale
A forced answer scale ranging categories from 1 to 10
158
Interval scales
Has a property of order as well as the property of equal intervals between successive points on the measurement scale e.g. IQ tests are usually considered a representative of the interval scale
159
Ratio scale
Most mathematically complex of the four measurement scales. It has properties of order and equal intervals as well as properties of an absolute zero point.makes it possible to multiply and divide racial scores to determine more precisely how much more or less of a characteristic one person has to another. Example Kelvin scale
160
Descriptive statistics
Used to describe or samurais a distribution of set data
161
Frequency distribution
Summarizing the data in terms of the number of frequency observations in each case
162
Cumulative frequency
Indicate the total number of observation that falls at or below each category or score
163
Shapes of distribution
| Normal curve
Systematic bell shaped and defined by specific mathematical formula
164
Shapes of distribution
| Kurtisis
Refers to the relative peaked nice hike or flatness of the distribution
165
Shapes of distribution
| Platkurtic
Went to distribution is flatter
166
Shapes of distribution
| Leptokurtic
Went to distribution is peaked
167
Shapes of distribution
| Skewed distribution
More than half of the observations fall on one side of the distribution
168
Shapes of distribution
| Positively skewed distribution
Most of the scores are in the negative low side of the distribution the positive tail is extended
169
Shapes of distribution
| Negatively skewed distribution
Most scores are located on the positive high school side of the distribution and the negative tail is extended due to the presence of a few low scores
170
Measures of central tendency
| Mode
The score category that occurs the most frequently
171
Measures of central tendency
| Median
The score the divides a distribution in half when the data has become ordered from low to high. Useful for it it's in sensitivity to outliers and open ended distributions where there are no specific upper and lower limits
172
Multimodal
Two or more scores are categories that occur equally often
173
Bimodal
When two scores are equal
174
Susceptibility to sampling fluctuations
This means that, if a large number of samples are randomly selected from the population, the mold can be expected to vary considerably from sample to sample.
175
Measures of central tendency
| Arithmetic mean
M-X
Mean equals the sum of the means divided by number of means
Used for its lease to susceptibility to sample and fluctuation usually provides an unbiased estimate of the population mean
176
Measure of variability
| Range
Calculated by simply subtracting the lowest score in the distribution from The highest score
177
Measure of variability
| Variance mean square
Is a more thorough measurement of variability then the range because it's calculations includes all the scores in the distribution rather than just the highest and lowest dance is calculated using
178
Measure of variability
| Standard deviation
The standard deviation is calculated by taking the square root of the variance
179
Population parameters and sample statistics
Estimates population values based on obtain samples
180
Characteristics of sampling distribution
Due to the effects of random chance factors, it is unlikely that any sample will perfectly represent the population from which it was drawn
181
Sampling error
inaccuracies
182
Inferential statistics test
Indicates where the obtain samples to distichs falls in the appropriate sampling distribution
183
Rejection region
Region of unlikely values lights on both tales of the sampling distribution
184
Retention region
Regions of likely values lies in the central portion of the sampling distribution
185
Null hypothesis is rejected
Obtain samples statistics is in the rejection region
186
No hypothesis is retained
Statistical tests indicate that the sample statistic lives in the retention region
187
Alfa or level of significance
0.01 or 0.05 represents the sampling distribution in the rejection region and the remaining percentage represents the retention region
188
Type I error = alpha
Occurs when an investigator rejects a true null hypothesis
189
Type II error = beta
Occurs when an investigator retains a false null hypothesis
190
Beta
Directly calculated for a particular study depending on the Alpha
191
Inverse relationship
As the probability of making a Type I error increases the probability of making a Type II error Increases and vice versa
192
Statistical power
A statistical test enables an experimenter to reject a faults Null hypothesis
193
Using a one tailed test when appropriate
One tail test is more powerful than a two-tailed test
194
Using a parametric test
Parametric statistical tests such as a t-test or ANOVA or more powerful than nonparametric test
195
Homoscedacity
The variance of the population that the different groups represented are equal
196
Critical values and degrees of freedom
Test that allows investigator to determine whether the obtains sample value is in the rejection or retention region of the sampling distribution this is done by comparing the tested to sticks to a critical value which is the number that corresponds to the boundary that divides the sampling distribution into rejection retention
197
Chi– square test
Used to analyze the frequency of observation of a nominal variable test
198
ANOVA
Analysis of variance is used to compare two or more means
199
One-Way ANOVA
Is used want to study includes one independent variable and two or more dependent groups
200
Factor analysis of variance
An extension of the one way ANOVA that is employed want to study includes two or more independent variables
201
Assumptions
Use of the Pearson r and most other qualifications require that three assumptions be met
202
Linearity
Assumption that there is a linear relationship between the variables relationship between the X-Men why can be summarized in a straight line
203
Unrestricted range
Use of the Persons r is also based on the assumptions that there is an unrestricted range of scores on both variables
204
Homoscrdasticity
The third assumption is that the range of why scores is about the same for the values of X
