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2ND QUARTER > PRACRES ALL > Flashcards

PRACRES ALL Flashcards

1
Q

A technique which the researcher uses to gather and generate data about the subjects of their study.

A

Method

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2
Q

Measurement tools designed to obtain data on a topic of interest from research subjects

A

Research Instrument

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3
Q

Analysis of data that have originally been collected either for a different purpose or by a different researcher or organization.

A

Secondary Analysis

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4
Q

Research that involves searching for and extracting information and evidence from original archives.

A

Archival Research

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5
Q

Conversations that are designed to obtain specific kinds of information.

A

Individual Interview

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6
Q

Types of Interview: can be in the form of normal conversations of a freewheeling exchange of ideas.

A

Unstructured

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7
Q

Types of Interview: the conduct of questioning follows a particular progression and has a well-defined content.

A

Structured

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8
Q

Types of Interview: there is a specific set of questions but there are also follow up questions that are not included in the list of questions.

A

Semi-structured

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9
Q

A list containing a set of structured questions to serve as a guide for collecting information or data during the actual interview.

A

Interview Schedule

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10
Q

Interviews conducted with several respondents simultaneously

A

Group Interview

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11
Q

Selected by researchers according to specific criteria

A

Focus Groups

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12
Q

Makes use of ratings or numerical rankings

A

Observation Checklist

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12
Q

Belongs to a group that exists independently of the study.

A

Natural Groups

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13
Q

Resembles interview schedules but are less restrictive in structure to allow the respondents a more free and dynamic exchange of ideas.

A

Topic Guides

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14
Q

Data collection technique in which the researcher makes use of all the five senses in examining the subject in a natural setting or naturally occurring situation.

A

Observations

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15
Q

Allows for more general descriptive documentation

A

Observation Guides

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16
Q

Research methods by which researchers create controllable environments to test hypothesis

A

Laboratory Experiments

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17
Q

Refers to research that is undertaken in the real world, where the confines of a laboratory setting are abandoned in favour of a natural setting.

A

Field Studies

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18
Q

Study of the behaviour of systems in using programs run on a computer.

A

Computer Modelling/Simulation

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19
Q

Refers to the collection, recording and analysis of information on a particular subject, an area or a group of people.

A

Survey

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20
Q

Implies a form containing a list of ready-made questions, delivered to people for obtaining statistical information.

A

Questionnaires

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21
Q

involves the separation of medicinally active portions of plant or animal tissues from the inactive or inert components by using selective solvents in standard extraction procedures.

A

Extraction

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22
Q

It is a liquid or a suitable solvent chosen for an effective extraction process.

A

Menstruum

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23
Q

It is an insoluble or inert drug material that is left behind at the end of the extraction process

A

Marc

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24
Q

a device used in chemical laboratories for the efficient and gentle removal of solvents from samples by evaporation.

A

Rotary Evaporation

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24
Q

In this process, the whole or coarsely powdered crude the drug is placed in a stoppered container with the solvent and allowed to stand at room temperature for a period of at least 3 days with frequent agitation until the soluble matter has dissolved.

A

Maceration

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24
Q

It is the mixture of both the extracted drug material and the solvent of extraction.

A

Micelle

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25
Q

form of maceration in which gentle heat is used during the process of extraction. It is used when moderately elevated temperature is not objectionable

A

Digestion

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26
Q

prepared by macerating the crude drug for a short period of time with cold or boiling water.

A

Infusion

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27
Q

In this process, the crude drug is boiled in a specified volume of water for a defined time; it is then cooled and strained or filtered.

A

Decoction

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28
Q

A volume of the microbial inoculum is applied evenly across the surface of the agar plate to inoculate it.

A

Agar Well Methods

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29
Q

A standard inoculum of the test microorganism is used to inoculate agar plates. Following that, 6mm-diameter filter paper discs are placed on the agar surface, each containing the test substance in the desired concentration.

A

Disk Diffusion Method

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30
Q

Agar-plots or cylinders are cut aseptically using a sterile cork borer from the desired strain and placed on another plate that has already been injected with the test microbe.

A

Agar Plug Method

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31
Q

The poisoned food approach is typically used to assess a substance’s ability to fight mold.

A

Poisoned Food Method

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32
Q

Dosing by oral gavage involves passing a feeding needle through the mouth and into the esophagus.

A

Mouse Oral Gavage

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33
Q

Involves amputating less than 2 mm of mice tail. Blotting of blood using filter paper until bleeding ceased.

A

Tail Bleeding Time Test

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34
Q

The basic principle is that each compound absorbs or transmits light over a certain range of wavelengths.

A

Spectrophotometry

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35
Q

mechanical test measuring the maximum amount of compressive load a material can bear before fracturing.

A

Compressive Strength Test

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36
Q

common materials test in which the test sample is subjected to a controlled tension load until it breaks.

A

Tensile Strength Test

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37
Q

Basically means “measure what is intended to be measured”

A

Validity

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38
Q

is the extent to which a study establishes a trustworthy cause-and-effect relationship between a treatment and an outcome.

A

Internal Validity

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39
Q

refers to how well the outcome of a study can be expected to apply to other settings.

A

External Validity

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40
Q

The extent that measurement instrument items linguistically and analytically look like what is supposed to be measured.

A

Face Validity

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41
Q

The extent that measurement instrument items are relevant and representative of the largest construct.

A

Content Validity

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42
Q

The extent that measures of different constructs diverge or minimally correlate with one another.

A

Construct Discriminant Validity

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43
Q

The extent that different measures of the same construct converge or strongly correlate with one another.

A

Construct Convergent Validity

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44
Q

The extent that a measure predicts another measure.

A

Criterion Predictive Validity

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45
Q

The extent that a measure simultaneously relates to another measure that it is supposed to relate to.

A

Criterion Concurrent Validity

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46
Q

The extent that a measure is related to the scores on another, already established in the past.

A

Criterion Postdictive Validity

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47
Q

Refers to the consistency of a measure

A

Reliability

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48
Q

Can be measured using Test-retest correlation

A

Test-retest Reliability

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49
Q

Can be measured by split-half, using Kuder-Richardson test (Score, mean, and SD is needed)

A

Internal Consistency

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50
Q

Can be measured by Cohen’s Kappa or Fleiss Kappa

A

Inter-Rater Reliability

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51
Q

Selection of people for a study may result in unintended differences related to the variable (Selection Bias)

A

Subject Characteristics

52
Q

Data collection or intervention in specific locations may lead to alternative result interpretations.

A

Location Threat

53
Q

Loss of subjects in a study may introduce bias and affect the study outcome.

A

Mortality Threat

54
Q

Changes in the instrument (including scoring procedure) may impact study results.

A

Instrumentation Threat

55
Q

Characteristics of data gatherers can influence study outcomes.

A

Data Collector Characteristics

56
Q

Data collectors or scorers may unconsciously distort the collected data.

A

Data Collector Bias

57
Q

Use of a pre-test may create a “practice effect” influencing study results or participant responses to an intervention.

A

Testing Threat

58
Q

Unforeseen or unplanned events during the study may impact the results.

A

History Threat

59
Q

Changes during an intervention may be due to the passing of time rather than the intervention itself.

A

Maturation Threat

60
Q

The way subjects view a study, and their participation is a threat to internal validity.

A

Subject Attitude

61
Q

participants change their behaviours when they are aware of their role as research subjects.

A

John Henry and Hawthorne effect

62
Q

The experimental group may be treated in unintended ways, giving them an undue advantage, and affecting the results.

A

Implementation Threat

63
Q

Caused by selecting subjects based on extreme scores or characteristics, leading to potential distortions in the results.

A

Regression Threat

64
Q

a faulty data collection method can lead to gathering of incorrect and invalid data.

A

Inability to Answer Research Questions

65
Q

With manipulated and biassed data from the project, other researchers may find it difficult to get the same data or result, despite following the same procedure for each method used in the study.

A

Effect on the Repeatability and Validity of Research

66
Q

Data derived from faulty data collection tend to distort the truth regarding the research question being addressed in the project.

A

Distorted Findings

67
Q

Once distorted data collected through a faulty procedure is published, other researchers who will read this research may be misled.

A

Misleading Other Researchers

68
Q

Policies that will be formulated based on faulty and distorted data will be highly compromised.

A

Compromising Decisions for Public Policy

69
Q

may refer to the activities that can be done before data collection begins.

A

Quality assurance

70
Q

includes the activities that must be done during and after data collection.

A

Quality control

71
Q

deals with the collection, organization, analysis of data and drawing of inferences from the samples to the population.

A

Statistics

72
Q

Any information that has been collected, observed, generated or created to validate original research findings.

A

Data

73
Q

A variable is an essential component of any statistical data. It is unique, and can differ in quantity or quality from another member of the same sample or population.

A

Variable

74
Q

Classifies data into categories in which no order or ranking can be imposed on the data.

A

Nominal

75
Q

Categories can be ranked, though precise differences between each ranking can’t be determined.

A

Ordinal

76
Q

Data can be ranked with precise differences in between, but there is no meaningful zero.

A

Interval

76
Q

Possesses the same characteristics of interval level but with the existence of a true zero.

A

Ratio

77
Q

Laerd Statistics (2018) mentions the ambiguities in classification of variables in which “in some cases, the measurement scale for data is ordinal, but the variable is treated as continuous.”

A

Ambiguities in Classification of Variables

78
Q

Try to describe the variables in a sample or population. It consists of the collection, organization, summarization, and presentation of data.

A

Descriptive Statistics

79
Q

Use a random sample of data taken from a population to describe and make inferences about the whole population.

A

Inferential Statistics

80
Q

also known as the arithmetic mean
sum of all scores divided by the number of scores.

A

Mean

81
Q

can be defined as a research process that involves repeated observations of the same groups of people, whether that’s employees, a specific group of customers, or another audience group to see if their opinions have changed since you first collected their data.

A

Longitudinal Survey

82
Q

a type of research design in which you collect data from many different individuals at a single point in time

A

cross-sectional survey

83
Q

the middle of a distribution in a ranked data

A

Median

84
Q

the most frequently occurring variable in a distribution

A

Mode

85
Q

a measure of how spread out is the distribution.

A

Variance

86
Q

Square root of variance.

A

Standard Deviation

87
Q

The highest value minus the lowest value.

A

Range

88
Q

Gaussian distribution
described as a continuous, bell-shaped, distribution of a variable

A

Normal Distribution

89
Q

is a decision-making process for evaluating claims about a population.

A

Hypothesis Testing

90
Q

is a tentative statement about the relationship between variables.

A

Hypothesis

91
Q

denotes that there is no relationship (difference) between the population variables in question.

A

Null Hypothesis

92
Q

denotes that there is a relationship (difference) between the population variables in question.

A

Alternative Hypothesis

93
Q

a value calculated from a statistical test which describes the probability of obtaining the observed results, assuming that the null hypothesis is true.

A

P-Value

93
Q

P-Value: scientific and medicine-related

A

0.01%

94
Q

P-value: educational research

A

0.05%

95
Q

P-value: Other studies

A

0.10%

96
Q

Rejecting the null hypothesis when it is true.

A

Type I Error

97
Q

Occurs if you do not reject the null hypothesis when it is false.

A

Type II Error

98
Q

A distribution that is asymmetrical because the data values trail off more sharply on one side than on the other.

A

Skew Distribution

99
Q

It is measure of the asymmetry of distribution

A

Skewness

100
Q

Less than -1 or greater than +1, the distribution

A

highly skewed

101
Q

Between -1 or -0.5 or between +0.5 and +1

A

moderately skewed

102
Q

-0.5 between +0.5

A

skewed approximately

103
Q

It measures the thickness of the tails and the center of the distribution

A

Kurtosis

104
Q

Type of Kurtosis: <0 (negative)

A

Platykurtic (N)

105
Q

Type of Kurtosis: 0 (or close to 0)
+/- 0.01 - +/- 0.09

A

Mesokurtic (0)

106
Q

Type of Kurtosis: >0 (positive)

A

Leptokurtic (N)

106
Q

Most commonly used type of statistical analysis. This analysis is referred to as parametric statistical analysis because the findings are inferred to the parameters of normally distributed populations

A

Parametric Test

107
Q

Both test for normality
P-value > 0.05

A

Kolmogorov-Smirnov Test and Shapiro-Wilks Test

108
Q

The assumption of normality which specifies that the means of the sample group are normally distributed.

A

Assumptions

109
Q

Test for equal variances

A

Levene’s Test

110
Q

Parametric Test: Used to compare a sample mean to a specific value.

A

One sample T-test

111
Q

Parametric Test: Can be used when the two groups under comparison are independent of each other.

A

Unpaired or Independent T-test

111
Q

Parametric Test: Can be used when the two groups under comparison are dependent on each other.

A

Paired or Dependent T-test

112
Q

Parametric Test: Used to determine one or more factors and their interactions differed significantly from that expected by chance.

A

One-way ANOVA

113
Q

Parametric Test: One-way ANOVA; Unequal Variances

A

Welch’s Test

114
Q

Parametric Test: One-way ANOVA; Equal Variances

A

Fisher’s Test

115
Q

Types of Post-hoc test (parametric)

A

Bonferonn’s
Tukey’s
Scheffe’s

116
Q

Parametric Test: To examine both the separate and the combined effects of the variables on the same measure of behavior.

A

Two-way ANOVA

117
Q

Parametric Test: Association between two groups; Assesses the linear relationship between two variables.

A

Pearson Correlation Coefficient

118
Q

statistical analysis or distribution free techniques
It can be used in studies that do not meet the first two assumptions

A

Non-parametric Test

119
Q

Non-parametric test: one sample

A

Wilcoxon Signed Rank’s Test

120
Q

Non-parametric test: two sample; Dependent
Non-parametric counterpart of the paired t-test.

A

Wilcoxon Signed Rank’s Test

120
Q

Non-parametric test: two sample; Used to compare differences between two independent groups when the dependent variable is.

A

Mann-Whitney U Test

121
Q

Non-parametric test: three or more sample; One factor
This test determines whether three or more independent samples belong to the same population based on medians.

A

Kruskal-Wallis H Test

122
Q

Non-parametric test; Association between Two Groups

A

Spearman’s Rank Order of Correlation

122
Q
A
123
Q

Type of Post-hoc test under Kruskal-wallis

A

Dunn test,
Conover test,
Nemenyi test
Dwass–Steel–Critchlow–Fligner test

124
Q

Non-parametric test: three or more sample; two factor Rank-based, non-parametric test for several related samples

A

Friedman Test

2ND QUARTER (14 decks)

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