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RESEARCH 115 Flashcards

1
Q

DATA MANAGEMENT AND ANALYSIS

A

QUALITATIVE

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2
Q

It is a non-numerical organization & interpretation of data to discover themes or patterns that are found in the field notes, interviews, recorded conversations, etc.

A

QUALITATIVE ANALYSIS

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3
Q

characteristics of qualitative analysis

A
  1. No universal rule
  2. Thick descriptions
  3. Enormous work
  4. Reduces data for reporting purposes
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4
Q

if it will describe the phenomenon in detail, specific, & contains all descriptions needed by the readers to understand phenomenon

A

THICK DESCRIPTION

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5
Q

should contain INFORMATION, INTENTION, & MEANING & SUBSEQUENT ACTIONS that can evolve from the phenoemna

A

THICK DESCRIPTION

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6
Q

FLOWCHARTS OF THE STUDY (qualitative)

A

Identification of the study
↓
Participants inclusion criteria
↓
Data Gathering Method
↓
TRANSCRIPTION OF INTERVIEWS
↓
Validating
↓
DATA ANALYSIS
↓
Documentation
↓
Dissemination of Findings

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7
Q

STEPS (qualitative)

A
  1. Read thoroughly & repeatedly-decipher meanings
  2. Clustering of similar responses
  3. Formulate themes & subthemes
  4. Construct a major theme & subtheme
  5. Presentation of themes & subthemes to participants
  6. Formulation of conceptual map after the participants have approved the themes
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8
Q

STRATEGIES IN ANALYZING OR HANDLING QUALITATIVE DATA:

A
  1. PRE-ANALYSIS ACTIVITIES
  2. ANALYSIS ACTIVITIES
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9
Q

record immediately relevant data

A

PRE-ANALYSIS ACTIVITIES

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10
Q

immersion

A

ANALYSIS ACTIVITIES

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11
Q

re-reading in search of themes

A

ANALYSIS ACTIVITIES

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12
Q

THEMATIC ANALYSIS

A

Researcher tries to RECOVER and UNCOVER what is embodied in the content

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13
Q

THEME

A

A phrase that described the fundamental meaning (essence) or significance in a selected portion in a narrative data

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14
Q

Someone who uses ______ makes SPECIFIC OBSERVATIONS and THEN DRAWS A GENERAL CONCLUSION

A

INDUCTIVE REASONING

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15
Q

is a SPECIFIC CONCLUSION FOLLOWS A GENERAL THEORY

A

DEDUCTIVE REASONING

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16
Q

Every quiz has been easy. Therefore, the test will be easy.

A

example of INDUCTIVE REASONING

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17
Q

All students in this class play guitar. SAM IS A STUDENT OF THIS CLASS. -Therefore, Sam plays guitar.

A

example of DEDUCTIVE REASONING

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18
Q

Thematic analysis includes:

A
  1. Deductive Process
  2. Inductive Process
    *Integration process
  3. Eclectic Approach
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19
Q

the researcher develops category based on subthemes that will represent each subproblems

A

Deductive Process

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20
Q

the researcher tackles one category at a time

A

Inductive Process

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21
Q

related them to subproblems

A

Inductive Process

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22
Q

use of metaphors or figures of speech

A

Inductive Process

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23
Q

study of relationship or linkages between themes within the subproblems or across the different subproblems

A

Integration process

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24
Q

blending of independent and dependent variables

A

ECLECTIC APPROACH

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25
REDUCING DATA
ECLECTIC APPROACH
26
INTERPRETATION OF QUALITATIVE DATA
A. EMIC PERSPECTIVE B. ETIC PERSPECTIVE
27
'Insider's View' (Participant)
EMIC PERSPECTIVE
28
The way the members describe their own experiences or perceive themselves
EMIC PERSPECTIVE
29
'Outsider's View'
ETIC PERSPECTIVE
30
The way the researcher describe experiences of the participants
ETIC PERSPECTIVE
31
Data analysis
QUANTITATIVE
32
separation of data into parts for the purpose of answering research questions & communicating the answers to others
DATA ANALYSIS
33
depend on specific question, research design, data collection method, & levels of measurement
DATA ANALYSIS
34
2 types/branches of DATA ANALYSIS (quantitative)
1. Descriptive Analysis 2. Inferential Analysis
35
use to describe individual variables of the study
DESCRIPTIVE ANALYSIS
36
find out the relationship or difference of the variables under study
INFERENTIAL ANALYSIS
37
2 methods in research
Qualitative and Quantitative
38
2 design in research
Experimental and Non-experimental
39
4 types of descriptive analysis
Frequency Percentage Mean, Median, Mode Standard Deviation
40
2 types of inferential analysis
Parametric Non-parametric
41
types of PARAMETRIC (Homogeneity)
T-test Annova Pearson Moment Correlation Coefficient
42
independent dependent
T test
43
2/more respondents
Anova
44
not quality unlike parametric
non-parametric
45
Steps in testing hypothesis:
1. States the Ho (Null hypothesis) 2. Select a level of significance (0.05) 3. Determine the statistical formula to be used 4. Identify the critical values or tabular values 5. Calculate value and apply the test 6. Compare the values 7. Interpretation
46
2 types of level of significance:
a. Type 1error (Alpha error) b.Type ll error (Beta error)
47
the researcher rejects a Ho that is really TRUE
Alpha error
48
failure of a researcher to reject a Ho which is really FALSE
Beta Error
49
CV>/=TV
Reject Ho- SIGNIFICANT
50
CV
Accept Ho- NOT SIGNIFICANT
51
4 Levels of interpretation
LEVEL I LEVEL II LEVEL III LEVEL IV
52
giving meaning to the numerical data contained in the table
LEVEL I
53
describe statistical formula
LEVEL II
54
possible reasons or effects of data
LEVEL III
55
Retrieve studies presented in RRL
LEVEL IV
56
overall, general
level I
57
highest & lowest
level II
58
implication
level III
59
collaboration
level IV
60
* means
SIGNIFICANT
61
2 formula
Fiszer's Scheffe's
62
natural, naturalistic
QUALITATIVE
63
deductive, general to specific
QUANTITATIVE
64
inductive, specific to general
QUALITATIVE
65
No matter how well the objectives are written, or how clever the items, the quality and usefulness of an examination is predicated on ____ & ____.
VALIDITY and RELIABILITY
66
It refers to the degree which the tool measures, what it is intended to measure
VALIDITY
67
It refers to whether the instrument or scale is quantifying what it claims to
VALIDITY
68
Ex. “Weight” scale measure body weight and it is valid.
VALIDITY
69
*Means “repeatability” or “consistency” *It is the ability to an instrument to consistently measure what it is suppose to measure *A measure is considered reliable if it would give us the same result over and over again (assuming that what we are measuring isn’t changing!)
RELIABILITY
70
Levels of Measurement
1. NOMINAL 2. ORDINAL 3. INTERVAL 4. RATIO
71
The nominal level of measurement applies to data that consist of names, labels, or categories. There are no implied criteria by which the data can be ordered from smallest to largest.
NOMINAL
72
Ex. Gender Nationality Phone number Course Responses: Yes/No
NOMINAL
73
The ordinal level of measurement applies to data that can be arranged in order. However, differences between data values either cannot be determined or are meaningless.
ORDINAL
74
Ex. Satisfaction Level IQ Level Self-Esteem Level Degree of Pain Monthly Income Classification
ORDINAL
75
The interval level of measurement applies to data that can be arranged in order. In addition, differences between data values are meaningful. It has no inherent (natural) zero starting point.
INTERVAL
76
Temperature Year IQ Score
INTERVAL
77
The ratio level of measurement applies to data that can be arranged in order. In addition, both differences between data values and ratios of data values are meaningful. Data at the ratio level have a true zero.
ratio
78
Age Height Test Score Salary Distance
ratio
79
The ____ is the probability of rejecting Ho when it is true.
Level of Significance
80
This is the probability of a type I error, denoted by 𝛼.
Level of Significance
81
A ______ is a test statistic value beyond which we reject the null hypothesis; often called a cutoff.
Critical Value
82
The ______ refers to the area in the tails of the comparison distribution in which we reject the null hypothesis if our test statistic falls there.
critical region
83
Ho is true, the probability that the test statistic will take on values as extreme assumings or more extreme than the observed test statistic (computed from sample data) is called the _____of the test.
Probability Value (p-value)
84
In statistical tests, a _______ result means that the null hypothesis has been rejected, which means that the result is very unlikely to have occurred merely by chance.
significant
85
Steps in Hypothesis Testing
1. State the null and alternative hypothesis. 2. Set the level of significance. 3. Compute the test statistic. 4. Determine the critical value or p-value. 5. Make the decision.
86
______ if the absolute computed value is greater than or equal to the absolute tabular/critical value. |Computed Value| ≥ |Critical Value|
Reject Ho
87
____ if the absolute computed value is less than the absolute tabular value. |Computed Value| < |Critical Value|
Do not reject Ho
88
____ if the p-value is less than or equal to the level of significance. p-value ≤ 𝜶
Reject Ho
89
_____ if the p-value is greater than the level of significance. p-value > 𝜶
Do not reject Ho
90
needs to provide a brief but comprehensive summary of the contents of your paper.
abstract
91
It provides an overview of the paper and helps readers decide whether to read the full text.
abstract
92
Limit your abstract to ______ words.
250
93
_____ address essential paper elements, such as the following: * research topic * population * method * application of results or findings
Keywords
94
written one line below the abstract indented (like a regular paragraph) italic (but not bold)
Keywords

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