Reviewer in Practical Research 2

- made by caitlin g., moira g. 

Lesson #1: Quantitative Research Designs

RESEARCH DESIGNS

Research Design

• serves as the structure of the study

• gives direction to the study and makes the research more systematic

• refers to the overall planning of the researcher which addresses research questions, including specifications for enhancing the study’s integrity

• two main research design:

DEFINITION OF QUANTITATIVE RESEARCH DESIGNS

Quantitative Research Design

• involves the collection of data to establish the relationship between and among the variables using statistical analysis

• used as a standard experimental method of most scientific disciplines

• it is one in which the inferential statistics are utilized to determine the results of the study

• according to Baraceros, you have to finalize your mind on the major aspects of your research study such as research topic, background of the study, research questions, hypotheses and so on... as well as the data collecting techniques in order for your research design to take place

• always remember: Quantitative Research will always focus on numbers, statistics, and relationships between variables

TYPES OF QUANTITATIVE RESEARCH DESIGNS

• experimental and non-experimental designs

Experimental Research Design

• is a quantitative research design that bases its research method on scientific activity called experiment, in which test or examination of a thing under manipulated or controlled environment is done to determine its validity or truthfulness

• True Experimental

• random selection of participants

• bias free selection that ensures objectivity of the results

• best way to examine causal relationship

• Quasi Experimental

• prone to bias caused by your purposive rather than random selection of participants

• this is used to investigate causal relationships when the researcher cannot (or doesn’t want to) randomly assign participants

• incapable of establishing cause-effect relationship

Non-Experimental Research Design

• quantitative research design that is capable of giving qualitative and quantitative data, but more on qualitative data. Hence, this is often used in the field or social sciences. 

• Survey

• describes the attitudes, preferences, views, feelings, opinions, and other behavioral patterns of a big number of people for arriving at a certain conclusion about societal concerns and issues

• Correlational

• shows extent and direction of variable relationships, that is whether a negative or positive relationship exists between or among them

• Descriptive

• depicts an image or a picture of an individual or a group, this type of research uses interviews, questionnaires, polls, and other similar instrument in order to gather data

• Comparative

• states the difference or similarities between or among people, things, objects, etc. It compares the variables but it does not focus on the relationship

Remember:

• In choosing a research design your main basis will be the purpose or objectives of your study

SAMPLE RESEARCH TITLE:

The Effects of Online Modality on Student’s Academic Performance

Experimental Design

• manipulate the IV (online modality)

• randomly assign participants to groups

• experimental group (in online class)

• control group (in face - to - face class)

• measure the DV (academic performance)

Non-Experimental Design

• Descriptive Design

• ask students if they are in online class or online distance learning

• get the record of the student’s grade

INTEGRATION:

• in order to choose our designs we should have a plan or an outline of our study

• to know what is exactly the purpose and objectives of our research study

Lesson #2: Quantitative Data Collection Technique

QUANTITATIVE DATA COLLECTION TECHNIQUE  

Data Collection

• a systematic process of gathering data

• to gain first hand knowledge

• one major component of any type of research

• accuracy or appropriation of your data gathering technique as well as the right instrument to collect data

What do we usually do when we collect data?

• we should know our data collection method

• we should know our data collection tools

Information

• processes, organized and structured data

• it provides context for data and enables decision making.

Data

• are pieces of information or facts known by people in this world

• appearing as measurable, numerical, and related to the metrical system, they are called quantitative data. 

• these data result from sensory experiences in which descriptive qualities, such as age, shape, speed, amount, weight, height, number, positions, and the like are measurable

Collecting Data

• stress is given to the accuracy or appropriateness of your data gathering technique as well as the right instrument to collect the data

DATA COLLECTION TECHNIQUE  

Observation

• the use of our sense organs

• you gather facts or information about people, things, place, events, and so on by watching and listening to them

• Controlled Observation 

• the researcher decides where the observation will take place, at what time, with which participants, in what circumstances and uses a standardized procedure

• participants are randomly allocated to each independent variable group.

• Naturalistic Observation

• a research method commonly used by psychologists and other social scientists. 

• this technique involves observing involves studying the spontaneous behavior of participants in natural surroundings

• the researcher simply records what they see in whatever way they can.

• Participant Observation

• is a variant of the above (natural observations) but here the researcher joins in and becomes part of the group they are studying to get a deeper insight into their live

Survey

• you obtain facts of information about the subject or object of your research through the data gathering instruments of interview and questionnaires

• Questionnaire Method

• paper containing series of questions formulated for an individual and independent answering by several respondents to obtain statistical information

• Interview Method

• makes you ask a set of questions

• yes /no or expressed by rating/no. of scale

Experiment

• you want to discover the reason behind the effects on the subjects

• its a method of collecting data data where you give the subject a sort of condition or treatment

• types of experiments in data collection instrument

• Laboratory experiments can be standard testing methods, independently developed procedures or laboratory scale model investigations.

• Experiments carried out in the field require careful planning and coordination. Higher contingencies in terms of budget and time must be considered. Controlling and monitoring the changing parameters on site could also involve other parties and the law. 

• Computer numerical models are another method to carry out experiments. There are various computer codes that can be utilized to construct a model. 

Content Analysis

• you search through several oral or written forms of communication to find answers to your research questions

• you not only examine printed materials but you also analyze information

• types of content analysis in data collection instrument

• Formal Content Analysis Approach

• samples of texts are collected and categorized through a certain system or method

• Textual Content Analysis Approach

• language structures (words, phrases, clauses, sentences, and paragraphs) are examined to determine their effects on the readers.

• Thematic Content Analysis Approach

• records or documents are analyzed to discover the purposes or motives of the communication media in producing such kind of documents or records.

• Audience Content Analysis Approach

• communication media records or concepts are studies to find out how meaningful, acceptable, or unacceptable the media contents are to the audience

MEASUREMENTS SCALES FOR QUANTITATIVE DATA  

Nominal Scale

• categorizing people based on gender, religion, and position, etc.

• categories (no ordering or direction)

• example: numbers assigned to runners

• qualitative data

Ordinal Scale

• ranking or arranging the classified variables to determine who should be the 1st, 2nd, 3rd, and so on

• ordered categories (ranking scallions, or order)

• example: rank orders of winners

• qualitative data

Interval Scale

• showing equal intervals or differences of people’s views or attitudes like the Likert Attitude Scale

• differences between measurements but no true zero

• example: performance rating on a 0 to 10 scale

• quantitative data

Ratio Scale

• rating something from zero to a certain point performance in Math subject – a grade of 90% (from 0 to 100%)

• differences between measurements, true zero exist

• example: time to finish 

• quantitative data

Lesson #3: Quantitative Data Analysis

QUANTITATIVE DATA ANALYSIS

Quantitative Data Analysis in Research

• offers objectivity, precision, statistical power, and generalizability

• enables researchers to quantify relationships, make comparison, and draw evidence-based conclusions

•  time consuming

• involves series of examinations, classifications, mathematical computations and graphical recording

• through and advances planning is needed for this major aspect of your study

•  analyzing data that are number based

• data that can be easily converted into numbers without losing any meaning

• quantitative data analysis is the process of breaking down complex topic to a smaller part to gain better understanding of it

What is Quantitative Data used for?

•  to measure difference between groups

•  to assess relationships between variables

•  to test hypotheses scientifically

STEPS IN QUANTITATIVE DATA ANALYSIS

I.  Preparing the Data

• 5 substeps of preparing the data

1. Compilation

• bringing all together the collected data

•  checking, cleaning, and preparing the data

• arranging data in a particular order

2. Editing

• checking the completeness and accuracy of the answers of your respondents

3. Coding

• transforming the statement into symbol

• number or alphabet

4. Classification

• classifying the data in particular order, class or group

5. Tabulations

•  recording data in mathematical terms

• in symbol >>> terms

II. Analyzing the Data 

1. Descriptive Statistics

• researcher will describe the basic features of the data and researcher makes a symbol summary regarding the data

• frequency distribution

• measures of central tendency

• standard deviation

• examples:

• percentage of mark

•  average mark of a particular student in a class

•  mean, median, range, mode, and standard deviation

• solve for mean, median, mode and range

• Heart Rate (bpm): 75, 80, 70, 90, 72, 85, 78, 82, 88

• Mean: 79.11 (round off two decimal places)

• Median: 82

• Mode: no mode

• Range: 20

2. Advanced Qualitative Analytical Methods

• Correlation - uses statistical analysis to yield results that describe the relationship of two variables. The results are incapable of establishing causal relationships

• Analysis of Variance (ANOVA) – the results of this statistical analysis are used to determine the difference in the means. 

• Regression – has some similarities with correlation, in that it also shows the nature of relationship of variables but gives a more extensive result than that of correlation. Aside from indicating the presence of a relationship between two variables. 

III. Drawing the Interference of data (inferential Statistics)

• Inferential Statistics

• making the conclusion and judgement of the data

• examples:

• researcher wants to know the relationship of two things

• researcher wants to know the association of two things

• researcher wants to know the difference of two things

•  researcher wants to know the mark, range, average between the male and female students as well as the height and weight of the students

• Population

• the entire group

• Sample

• portion of the group you can actually access

• Descriptive Statistics

•  focuses on describing the sample

• Inferential Statistics

• aims to make prediction about the population

IV.  Interpretation of the Data

•  deep examination of the result

•  critical examination of the result

•  explain the result and making that result in an understandable manner

Lesson #4:Statistical Method

STATISTICS

Statistics

• pertains to your acts of collecting and analyzing numerical data

• doing statistics means performing some arithmetic procedures. Statistics demands much time and effort but it involves analysis, planning, interpreting, and organizing data in relation to the design of your research output.

• Statistical Methods are ways of gathering, analyzing, and interpreting variable or fluctuating numerical data

• Goal of Statistics analysis is to answer two questions: 

1. Is there a significant effect/association difference between the variables of interest? (i.e., can we reject the null hypothesis?) 

2. Is there an effect/association/difference – how big is it? 

• How do statistics relate to research? 

• Statistical Methods are very much needed for scientific research. 

• In fact, the results acquired from research studies are meaningless raw data unless analyzed with statistical tools. 

• Research begins when there is a research question

Statistical Methodologies

• Descriptive Statistics

• describes a certain aspect of data set by making you calculate the mean, median, mode and standard deviation

• does not tell anything about the population

• Inferential Statistics

• it is a branch of statistics that focuses on conclusions, generalization, prediction, interpretations, hypotheses, and the like

• Not as simple as descriptive statistics

• Analysis begins with the sample, and then, based on your findings about the sample, you make inferences or assumptions about the population. 

• It focuses on conclusions, generalizations, predictions, interpretations, hypotheses, and the like.

Statistical Data Analysis 

• Univariate Analysis - analysis of one variable

• Bivariate Analysis - analysis of two variable (independent and dependent variables) 

• Multivariate Analysis - analysis of multiple relations between multiple variables

STATISTICAL METHOD OF BIVARIATE ANALYSIS

Correlation of Covariation

• Describes the relationship between two variable and also tests the strength and significance of their linear relation

Cross Tabulation

• it follow the format of matrix that is made up of lines of numbers, symbols, and other expression

• by displaying the frequency and percentage, it explains the reason behind the relationship of two variables and the effect of one variable to another variable

Measure of Correlation

• Correlation Coefficient - this is a measure of the strength and direction of the linear relationship between variables and likewise gives the extent of dependence between two variables - meaning the effect of one variable on the other variable 

• Regression - it determines the existence of variable relationships, but does more than this by determining:

1. which from the independent and dependent variable can signal the presence of another variable 

2. how strong the relationship between the two variables

3. when an independent variable is statistically significant as a soothsayer of predictor

CORRELATION COEFFICIENT

Pearson Product-Moment Correlation (Pearson r)

• measures the strength and direction of the linear relationship of two variables and of the association between intervals and ordinal variables 

• another way to think of the Pearson correlation coefficient (r) is as a measure of how close the observations are to a line of best fit 

• the Pearson correlation coefficient also tells you whether the slope of the line of best fit is negative, r is negative, and when the slope is positive, r is positive. 

• when r is 1 or -1, all the points fall exactly on the line of best fit

• formula

r = N∑xy - (∑x)(∑y)[N∑x2-(∑x)2][N∑y2-(∑y)2]

where:

N = number of pairs of scores

∑xy = sum of the products of paired scores

∑x = the sum of x scores

∑y = the sum of y scores

∑x2 = the sum of squared x scores

∑y2 = the sum of squared  y scores

example: 

Spearman’s Rho (Spearman’s r)

• the test to measure the dependence of the dependent variable on the independent variable

• it is the nonparametric version of the Pearson correlation coefficient

• your data must be ordinal, interval, or ratio

• Spearman’s returns a value from -1 to a where:

• +1 = a perfect positive correlation between ranks

• -1 = a perfect negative correlation between ranks

• 0 = no correlation between ranks

• formula: 

p=1-6∑d12n(n2-1)

r = regression

t = t-test value

n-2 = degree of freedom

example: 

Chi-Square

• to test the null hypothesis

• it tests whether or not a relationship exists between or among variables, and tells the probability that the relationship is caused by chance

• formula:

x2=∑(0-E)2E

where:

O = observed frequency

E = expected frequency

∑ = summation

x2 = chi-square value

example: 

Preference of men and women regarding their pets at home. Use a 5% significance level.

Using the chi-square table: 

• df (degree of freedom) 

  • N = 2 

  • df = n -1 

  • df = 1 

  • cv = 3.841

Interpretation: 4.102 > 3.841 Therefore reject the null hypothesis. There is a significant relationship between their choice of pets at home.

t-test (ANOVA/Analysis of Variance)

• evaluates the probability that the mean of the population from where the sample was drawn

• it also tests the difference of two means, the sample and population mean

• formula:

t=rn-21-r2

where:

r = regression

t = t-test value

n-2 = degree of freedom

example: 

A researcher investigated the relationship between family income and savings. Using the data from 15 families, the computed r between income and savings was found to be 0.76. is the computed r significant at a 0.05 level of significance? Can we conclude that the relationship exists?

Step #1: Identify the Ho and H1

- H0: There is no significant difference between family income and savings. r = 0 

- H1: There is a significant difference between family income and savings. r ≠ 0 

Step #2: Compute for the Value of t

t=rn-21-r2

t=0.7615-21-(0.76)2

t=4.22

Step #3: Use the t-table. Compare the computed t-value with the critical value of it

Step #4: Make a Decision

Step #5: Result (Interpretation)

- Therefore, we can conclude that the relationship between income and savings really exists in the population

TYPES OF ANOVA

1. Anova - One Way or F-Test

• ANOVA (Analysis of Variance) or F-test is used to compare 3 or more groups - It is used to show if there is a significant difference between the groups. 

• compares the means of two or more independent groups in order to determine whether there is statistical evidence that associated population means are significantly different. Oneway ANOVA is a parametric test. 

• the test is also known as: One-Factor ANOVA, One-Way Analysis of Variance, Between Subjects ANOVA

• the variable used in this test are known as: 

• Dependent Variable

• Independent Variable (also known as the grouping variable or factor) - this variable divides cases into two or more mutually exclusive levels or group 

example: 

A researcher investigated to which temperature is it ideal to take the exam - cold, normal (room temperature), or hot

Hypotheses: 

• H0 = µ1 = µ2= µ3 

• H1 = µ1 ≠ µ2 ≠ µ3

Step #1: Compute the Mean Value of each group 

Step #2: Complete the table

k = number of treatment conditions 

n = number of scores in each treatment 

N = total number of scores 

T = total for each treatment condition 

G = sum of all scores in the study 

SS = sum of squares

Step 3: Complete the ANOVA table 

F = divide MS between treatments and within treatments 

Step #4: Look for the tabular value for F

Step 5: Compare F- statistic (computed value) to critical value (from the table)

• F = 12. 67 

• Table F = 3.88 If the F value is greater that the table F, reject H0. 

• Conclusion: There is a significant difference on the exam scores between the 3 groups of room temperatures. 

2. Ancova

• study of two or more dependent variables that are concealed with one another

3. Mancova

• multiple analyses of one or more independent variables and one dependent variable to see if the independent variables affect one another.

Lesson #5:Sampling Procedure

SAMPLING PROCEDURE

Sampling

• manner of selecting the subjects/objects of a research study from a bigger group

• it means choosing the respondents or subjects from a large population to answer your questions

• the entire population is involved but for your research study, you only choose a part of the whole

Population

• technical term in research which means a big group of people from where you choose the sample or the chosen set of people to represent your study

Sampling Frame

• it is  the list of the members of your study to which you want to generalize or apply your findings about the sample

Sampling Unit

• term referring to every individual in the population

Remember!

• The sampling, as well as the research results, expected to speak about the entire population. Unless this does not refer to the population, in general, the sample selection procedure has no scientific value

FACTORS AFFECTING SAMPLE SELECTION

Sample Size

• how big should the sample be?

• see to it that the sample truly represents the entire population 

• when using the right sampling technique such as the randomized one, your chances of getting a sample reflecting 95% distribution of the population or of a sample representing the whole population is highly probable

• this acceptable level of probability of the representativeness of the sample is called confidence level or 0.5 level

Sampling Technique

• probability sampling and non-probability sampling

• the first one uses a random selection and the second, a purposive or controlled selection. Probability sampling that gives all population members equal opportunity to be chosen as people to constitute the sample is a precise way of sampling. Based on pure chance, it is unbiased or an accurate manner of selecting the right people to represent the population. 

• bias is the leading factor in choosing your respondents. This is one of the causes of sampling errors. The other errors in sampling are attributed to your procedure in sampling.

Heterogeneity of Population

• Heterogeneous population - individuals with varied population (large sample)

• Homogenous Population - lots of uniformalities in abilities exist (small sample size)

Statistical Technique

• the accuracy of the sample depends also on how precise or accurate your methods are in calculation the numbers used in measuring the chosen samples

• any error in your use of any statistical method or computing numbers representing the selected subjects will turn in unfounded results

Time and Cost

• choosing samples makes you deal with one big population, with each member of this large group needing your attention, time, and effort, let along  the amount of money you will spend for the material you will need making the sampling frame

SAMPLE METHODS

The sampling method are of two groups:

1. Probability Sampling - your selection of respondents on pure chance

1. Simple Random Sampling 

• best type of probability sampling through which you can choose a sample from a population.

• using pure-chance selection, you assure every member the same opportunity to be in the sample. 

• the only basis of including or excluding a member is by chance or opportunity - Happens through any of these two methods (Burns, 2012): 

1.  Have a list of all members of the population; write each name on a card and choose cards through a pure-chance selection. 

2. Have a list of all members; give a number to member and then use randomized or unordered numbers in selecting names from the list

2. Systematic Sampling 

• For this kind of probability sampling, chance and system are the ones to determine who should compose the sample. 

• For instance, if you want to have a sample of 150, you may select a set of numbers like 1 to 15, and out of a list of 1,500 students, take every 15th name on the list until you complete the total number of respondents to constitute your sample. 

3. Stratified Sampling 

• The group comprising the sample is chosen in a way that such a group is liable to subdivision during the data analysis stage. 

• A study needing group-by-group analysis

4. Cluster Sampling 

• This is a probability sampling that makes you isolate a set of persons instead of individual members to serve as sample members. 

• For example, if you want to have a sample of 120 out of 1,000 students, you can randomly select three sections with 40 students each to constitute the sample.

2. Non-Probability Sampling - your selection of respondents on purpose (purposefully)

1. Quota Sampling 

• you resort to quota sampling when you think you know the characteristics of the target population very well. In this case, you tend to choose sample members possessing or indicating the characteristics of the target population. 

2. Voluntary Sampling

• since the subjects you expect to participate in the sample selection are the ones volunteering to constitute the sample, there is no need for you to do any selection process.

3. Purposive Sampling

• choosing respondents with good background knowledge or with great enthusiasm about the research

4. Availability Sampling 

• picking out people who are easy to find or locate and willing to establish contact with you 

5. Snowball Sampling 

• selecting samples from several alternative samples, like drug dependents, human traffickers, street children, and others whose dwelling places are not easily located as they move from palace to place. Participants in the study were tasked to recruit the others members for the study