PR 2| WEEK 1-2 Flashcards
refers to the overall strategy that a researcher uses to logically and coherently
integrate the various components of a study (Barrot, 2017).
RESEARCH DESIGN
can help you clarify the methods or techniques in finding answers to your research questions and in collecting data (Baraceros, 2017).
RESEARCH DESIGN
The appropriate choice of quantitative research design for your study is the initial step after conceptualizing your research topic (Baraceros,2017).
RESEARCH DESIGN
uses statistical analysis and mathematical computations in order to generate a conclusion (Arcinas, 2016).
RESEARCH DESIGN
4 ADVANTAGES of quantitative research designs ( Arcinas, 2016)
verify results
filter out external factors and produce unbiased results
verify qualitative researches and narrow down possible results
variables can easily be manipulated
4 DISADVANTAGES of quantitative research designs (Arcinas, 2016)
time consuming in data collection
difficult and expensive to do
requires statistical analysis
very little room for uncertainty
- most common design that observes and reports certain phenomenon or shows a picture of a group.
- describes the characteristics of the problem, phenomenon, situation, or group under study.
- answers the “what,” “when,” and “where” of a research problem. For this reason, it is popularly used in market research, awareness surveys, and opinion polls.
- no treatment/intervention (no manipulation happened) (Barrot, 2017).
- uses the demographic profile the respondents as basis of classifying the data.
Common study designs: - include comparative descriptive design
- cross-sectional
- longitudinal designs
Statistical tool used: - Mean, Median, Mode, and Percentage, Frequency
Examples: - The Role of Facebook in Combating Misinformation Online.
- Buying Power of Social Media Users in Online Marketing
Descriptive research design
- seeks for connection between one variable and how it affects another variable but not a “cause-and-effect” relationship
- no manipulation of variables (Barrot,2017).
Common study designs include: - descriptive correlational designs
- predictive designs
Statistical tools employed are inferential statistics such as: - Spearman’s rho (Spearman’s r)
- Pearson product-moment correlation (Pearson’s r) (Baraceros, 2017).
Examples: - Linear relationship between height and basketball performance
- Association between exam performance and time spent revising
Correlational Research Design
- aims to infer the causes of a phenomenon which have already occurred.
- no manipulation of variables and groups exposed to the presumed cause are compared to those who are not.
- looks at “after-the-fact” situation or scenariouses questionnaire (Barrot, 2017)
- employs Wilcoxon Signed-Rank Test as the non-parametric statistical tool for the test of hypothesis
Causal-comparative research design (Ex post facto)
- aims to establish a cause-and-effect relationships (Barrot, 2017)
- may or may not have a control group or subjects and subjects are not randomly assigned to groups (Baraceros, 2017)
- uses intact groups or already existing groups.
common study designs include : - pre and post-test designs
- post-test only designs
- interrupted time series designs
- non-equivalent designs (Cristobal & dela Cruz-Cristobal, 2017).
- The Mann-Whitney U test for ordinal or continuous groups for the hypothesis testing may be employed.
Quasi-experimental research design
- aims to establish cause-and-effect relationships and randomly assign individual
- participants/subjects to the treatment and control groups (Barrot, 2017).
common study designs include: - pre-test-post-test control group designs
- post-test only control group designs
- Solomon four-group designs (Cristobal & dela Cruz-Cristobal, 2017).
A test of hypothesis that employs parametric statistical tools such as: - t-test for independent sample (unpaired t-test)
- One-way Anova
- It is bias-free selection that ensures objectivity of the results (Baraceros, 2017).
Experimental research design
The way in which we select a sample of individuals to be research participants is critical.
How we select participants (random sampling) will determine the population to which we may
generalize our research findings.
The procedure that we use for assigning participants to different treatment conditions (random
assignment) will determine whether bias exists in our treatment groups
(Are the groups equal on all known and unknown factors?).
sampling
the process of selecting the subjects of the population to be included in the sample
A sample is a “smaller (but hopefully representative) collection of units from population used to determine truths about that population (Field, 2005)
Sampling
2 kinds of sampling
Probability Sampling
Non-Probability Sampling
- refers to the selection of a sample from a population is based on the principle of randomization (each member of the population has a known non-zero probability of being selected).
- each member of the population is given a chance of being included in the sampling
- Minimizes, if not eliminates selection bias.
Probability Sampling
- is a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection (members are selected from the population in some nonrandom manner).
- involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection
- prone to selection bias
Nonprobability Sampling
- every member of the population has an equal chance of being selected.
Simple Random Sampling
3 type of Simple Random Sampling
A . Fishbowl Technique
B. Lottery Method
C. Sampling with the use of Table of Random Numbers
This is done by simply writing the names or numbers of all the members of the
population in small rolled pieces of paper which are later placed in a container.
The researcher shakes the container thoroughly then draws n out of N pieces of
papers as desired for a sample.
Fishbowl Technique
similar to a fishbowl, however usually done in a lottery
Lottery Method
If the population is large, a more practical procedure is the use of
Table of Random Numbers which contains rows and columns or digits
randomly beginning at an arbitrary point in Random Numbers, closing your
eyes and haphazardly pointing at an entry in the Table.
Then proceed in any direction, vertically, horizontally coded elements in the
population.
Sampling with the use of Table of Random Numbers
This method of sampling is done by taking every kth element in the population.
It applies to a group of individuals arranged in a waiting line or in a methodical manner.
For instance, the objective is to get the opinion of employees regarding employee management relations, a sample size n will be selected from the list of employees arranged alphabetically or according to age, experience, position or academic rank.
By systematic Sampling, every kth employee from the listed order will be included in a sample.
If N is known, k be can be calculated as:
- where N, the population size
n, the sample size
Systematic Sampling
When the population can be partitioned into several strata (singular: stratum) or subgroups (example: gender, socio-economic status, educational attainment, section, year level, age group, by department, school affiliation, etc.)
it may be wiser to employ the stratified technique to ensure a representative of each group in the sample.
Random samples will be selected from each stratum.
Selecting a sample with this technique is quite difficult and costly since it requires a
complete listing, called frame
Stratified Random Sampling
2 kinds of stratified random sampling
1.Disproportionate Stratified Random Sampling
2.Proportionate Stratified Random Sampling
When the population is grouped into more or less homogeneous classes, that is, different groups but with a relatively common characteristic, then each can be sampled independently by taking equal number of elements from each stratum. This method is called simple random sampling.
Disproportionate Stratified Random Sampling
In some cases, the characteristic of the population is such that the proportions of the subgroups are grossly equal. The researcher may wish to maintain these
characteristics in the sample with the use of stratified proportion technique.
Proportionate Stratified Random Sampling