Category Archives: Quantitative Research

Posts about descriptive, correlational, causal-comparative, and experimental research.

Market Analysis: The Pizza Study

What is market analysis? How is it done? This article describes how market analysis works using data on a pizza study.

After having defined marketing research in my previous post and giving an example conceptual framework for a pizza study, I decided to get into the details of market analysis using a standard multivariate statistical analysis tool. I saw the need of writing this article upon reading several articles on market analysis. There is a need to demonstrate what is market analysis.

Before everything else, the concept “market analysis” should be defined first.

What is market analysis and how is it used?

Market Analysis Defined

Marketing strategies work best when founded on a systematic evaluation of consumer preferences. What do consumers want? How do they respond to a product or service? Marketing research provides answers to these questions.

Hence, market analysis can be defined as the process of evaluating consumer preferences using a systematic approach such as marketing research, among others. Market analysis is a detailed examination of the elements or structure of the market.

Why is a market analysis done? An analysis is done to draw out important findings for interpretation, discussion and finally, a decision on what steps to make.

The Pizza Study

Once again, the conceptual framework given in the pizza study is given below to serve as a reference in the following discussion.

market analysis of a pizza study
Conceptual framework of the pizza study.

To find out what customers want, let us have a sample data of feedback from 200 pizza shop customers. To understand how analysis works, you need to read the article on variables as these are important units of analysis. If you already understand what variables are, then proceed to read the rest of the discussion.

Coding the Variables for Market Analysis

Let us have the following measures for the variables in this study namely pizza taste, service speed, and waiter courtesy:

Pizza Taste

1 – Very bad
2 – Bad
3 – Moderate
4 – Good
5 – Very good

Service Speed
1 – Satisfied
0 – Not satisfied

Waiter Courtesy
1 – Courteous
0 – Not courteous

Level of Satisfaction
Let us assume that the following Likert scale applies to the customer’s level of satisfaction:

1- Not at all satisfied
2 – Slightly satisfied
3 – Moderately satisfied
4 – Very satisfied
5 – Extremely satisfied

If for example, the customer is satisfied with pizza taste, service speed, and waiter courtesy; he rates everything “5.” If he is not satisfied with courtesy, then he might rate it a “0.”

Multiple Regression Analysis

Below is a data set representing the response of 200 pizza customers that serves as input to multiple regression analysis (you may try the data set if you know how to compute using multiple regression):

You may skip this table by clicking the link below:

Jump to the Results of Analysis.

A table summarizing the results of the pizza survey.

Customer #SatisfactionTasteSpeedCourtesy
15511
25411
34411
44411
54411
63500
75511
84511
95511
104411
114410
124411
134311
145311
154311
163401
174411
185411
195410
205410
214411
223501
234511
244411
253401
264511
275511
285411
294411
303401
313501
323501
333401
344411
354311
364210
375411
385311
395311
404311
414411
425411
434411
445311
454411
465411
474511
485511
494511
505511
514511
525411
534411
545411
554410
565511
573501
583501
593501
604411
614410
624410
634400
645511
655511
665511
675511
685411
695411
705411
714411
724411
734411
745311
754311
765311
774411
784411
794411
804511
814511
824510
835511
844411
854411
864411
874511
884511
894511
903501
914511
924411
934411
945411
954411
964411
974410
983401
993401
1003401
1013401
1024311
1034411
1044411
1054410
1064511
1074511
1084511
1095511
1104511
1114411
1124411
1135411
1144411
1154411
1164411
1175511
1185511
1195510
1205511
1215511
1225411
1235411
1245411
1255411
1265411
1275411
1284411
1294411
1304410
1314411
1324511
1334511
1344511
1354511
1364511
1375411
1385411
1395411
1405311
1414411
1424411
1434411
1444411
1454410
1464411
1474311
1484311
1494311
1503401
1514411
1524411
1534411
1543401
1554411
1564511
1574511
1584511
1595511
1605511
1615510
1625510
1635511
1645511
1654511
1664511
1674510
1684510
1694511
1704411
1715411
1725411
1735411
1745411
1754411
1764511
1774511
1784511
1794511
1804511
1815511
1825511
1835511
1845511
1854511
1864511
1874511
1884411
1893401
1904411
1914411
1924411
1934411
1945411
1955411
1965410
1974511
1984411
1994411
2003401

Result of the Regression Analysis

The following table presents the results of the multiple regression analysis using a simple spreadsheet software application with regression capability – Gnumeric. The first part shows the general relationship between the dependent and independent variables. The second part shows the details of the relationship between satisfaction score and pizza taste, service speed, and waiter courtesy.

Part 1. Regression Statistics
Multiple R0.66
R^20.44
Standard Error0.47
Adjusted R^20.43
Observations200
Part 2. Details
CoefficientsStandard Errort-Statisticsp-Value
Intercept2.91920.269110.84860.0000
Taste0.03260.05260.62080.5355
Speed1.32450.107612.31000.0000
Courtesy−0.01610.1098−0.14690.8834

Notice that the overall relationship has R values. Among these R values, the most important for interpretation is the Adjusted R^2 value. This value represents the relationship between variables of the study. The value obtained here is 0.43. This means 43% of the variation in satisfaction score is accounted for by the three variables.

Closer scrutiny of the details in Part 2 reveals that service speed significantly relates to satisfaction score. The red font indicates this significant relationship (for better understanding, please read the post on how to determine the significance of statistical relationships).

Interpretation of the Results

Based on the results of the statistical analysis, we can say with confidence that among the variables studied, service speed relates significantly to customer satisfaction. If you look closely at the entries in the data set, for every 5 or 4 satisfaction score, a 1 corresponds to service speed, meaning, the customer is satisfied with service speed. Take note, however, that this interpretation holds true only to the particular location where the study transpired.

Given this result, the marketing manager, therefore, should focus on the improvement of service speed to satisfy customers. This simple information can help the pizza business grow and gain a competitive edge. Market analysis guides decision-making and avoids incurring the unnecessary cost associated with the hit-and-miss approach.

Cite this article as: Regoniel, Patrick A. (May 21, 2016). Market Analysis: The Pizza Study. In SimplyEducate.Me. Retrieved from http://simplyeducate.me/2016/05/21/market-analysis-pizza-study/

Marketing Research Conceptual Framework

What is marketing research? How do you come up with your conceptual framework on marketing research? This article defines the concept and provides a simplified example.

One of the readers of my article on how to develop a conceptual framework asked if I could provide an example conceptual framework on marketing research. I am quite interested in applying the principles of marketing research on my entrepreneurial venture such as in creating and running this website.

It so happened I came across a book on marketing research in BOOKSALE while looking for textbooks on statistics. The book is on sale, so I pulled out my wallet and shelled out a little investment for my hungry brain. The title of the book is a straightforward “Essentials of Marketing Research” by William Zikmund.

I set aside 15 minutes to read the book right after my jogging session. I did this thinking that my mind could actively absorb the contents of the highly academic book after pumping a lot of oxygen during vigorous exercise. In fact, Hillman (2008) noted the beneficial effect of aerobic exercise to cognition. Exercise not only improves physical health but also academic performance.

To come up with a conceptual framework for marketing research, I find it necessary to define marketing research first.

Marketing Research Defined

Zikmund (1999) defines marketing research as a systematic and objective process of generating information to aid in marketing decisions. This process includes specifying what information is required, designing the method for collecting information, managing and implementing the collection of data, analyzing the results, and communicating the findings and their implications.

The most important thing in this definition is that marketing research, as in any research venture, helps business owners or marketing managers make decisions. Marketing research sheds light on customer’s preferences, the long-range profitability of business operations, and other product-oriented concerns.

Successful companies like Google, Microsoft, IBM, among other well-known businesses must be employing excellent marketing research activities to keep their edge. Decisions related to their products and services are not haphazardly done. Managers decide with calculated risks.

Example Conceptual Framework on Marketing Research

One of the popular marketing research activities focuses on product quality and services. I illustrate product and service research with a personal experience below.

A few years back, I answered a simple questionnaire soliciting my feedback on the product and services of a pizza shop. The questionnaire sought my rating of pizza taste, service speed, and the courtesy of the server.

We can plot the paradigm of the study as follows:

marketing research example
The paradigm of the pizza study showing the independent and the dependent variables.

The paradigm above shows the conceptual framework of the study. It is an abstract representation of what the pizza manager or consultant has in mind. It shows the variables that the researcher shall examine to determine which of the three variables correlate most with customer satisfaction.

Why were the three independent variables namely pizza taste, service speed, and waiter courtesy selected? A review of the literature on customer satisfaction may have revealed that these variables are determinants of customer satisfaction. But in the particular location where the pizza restaurant operates, any of these variables may be more important than the other. A study found out that customer preferences vary geographically. This finding implies that clients in one place may prioritize courtesy over taste. In one location, customers may put a premium on service speed. In another location, customers may not mind much either the speed or courtesy but the taste.

So how will the marketing manager use the findings of the study in the given example? If for example, customers in the location I’m in prioritizes service speed, then the appropriate action should be to improve the speed of pizza delivery without compromising taste and courtesy.

This example illustrates the importance of marketing research in making decisions that can help businesses grow. Research findings guide marketing managers on what steps to take to improve their business operations.

Reference

Hillman, C. H., Erickson, K. I., & Kramer, A. F. (2008). Be smart, exercise your heart: exercise effects on brain and cognition. Nature Reviews Neuroscience, 9(1), 58-65.

Zikmund, W. (1999). Essentials of marketing research. Dryden Press. 422 pp.

Cite this article as: Regoniel, Patrick A. (May 20, 2016). Marketing Research Conceptual Framework. In SimplyEducate.Me. Retrieved from http://simplyeducate.me/2016/05/20/marketing-research/

Statistical Sampling: How to Determine Sample Size

How do you determine the sample size required for your specific study? This is an important question considering that the answer determines how much effort you should devote to your research as well as how much money you have to allocate for it. This article explains how sample size should be estimated to obtain the optimal sample size.

As you would not want to sacrifice accuracy for convenience, and to make your research worthwhile, having the correct sample size makes your research more credible. If you sample too little, your results may not be reliable. If you sample too large a size, you will also be spending too much.

Sampling is especially true to quantitative studies, as it tries to define or describe a population by studying a part of it. But how many should be enough?

Here are important considerations when estimating the correct sample size.

4 Measures Required to Estimate Sample Size

Statisticians agree that you have to be familiar with at least four things before you draw a sample from your population. These are enumerated and described below.

1. Size of the Population

As a researcher, you should be familiar with your target population’s size. It is therefore necessary that you define your population so that you can approximate or find ways to estimate the total population and get the optimal size possible.

Let’s say you would want to find out the tourists’ average willingness to pay to access or see a natural park in view of estimating the value of the natural park’s aesthetic value. This means that your population should be the number of tourists who visit the park in one year if you are discussing an annual turnout of visitors. You can get this number from the tourism office especially if park access is for a fee.

Since you cannot interview all of the tourists, a sample may be drawn at a certain point in time which you will determine yourself, bearing in mind the peak and the off seasons to avoid bias. Familiarity with your population, therefore, is a must.

2. Margin of Error or Confidence Interval

Margin of error refers to the range of values that is acceptable to you as you estimate of the population’s mean or average value. What is the percentage of error that you will allow to give you the level of confidence you need? Whatever value you get in estimating say, the mean of your population is not an absolute number. You should allow for little deviations that are statistically acceptable and serve your purpose.

An analogy to illustrate the margin of error is like a hunter trying to hit a deer with his arrow. He aims for the heart but in the process hits the areas within 3 inches of the heart, either below, above, at the left or at the right. That is okay, because what he really wants is to be able to bring the deer home for his meal. Hitting the parts surrounding the heart serves the purpose of going home with the booty. Hitting the lungs or the other internal parts next to the heart can immobilize it.

3. Confidence Level

Confidence level is a little bit confused with margin of error. This is your level of certainty that your estimated mean (the statistic) will fall within the confidence interval that you have set for the estimate.

Again, back to the analogy of hitting the deer with an arrow. The question is “How confident is the archer in hitting the areas surrounding the heart?” If he is really a very good archer, he might say that out of 100 arrows, he is certain that 95 of this would hit the area within 3 inches of the heart. That’s his confidence level or percentage of certainty.

In statistics, the convention is to have a confidence level of either 95% or 99%. The former is a commonly used standard.

Assuming that your population has a normal distribution, the confidence level corresponds to a value of the z-distribution. A z-distribution is a standard normal distribution, meaning, the population approximates a bell-shaped curve.

4. Standard Deviation

The standard deviation is how spread out the numbers are from the mean. To make this concept clear, let’s go back to the hunter example.

Let’s say the hunter shot a target with a bullseye 500 times. As he is a very good archer, most of the arrows would have landed near or at the center but for sure, not always at the center. Those arrows that missed the bullseye are similar to the deviations from the mean. The way the arrows spread from the center indicates deviations from the average.

So how far will the arrows released by the hunter deviate from the center? We don’t know unless we measure the distance of each of the arrows from the center. But we don’t have time to measure all of the 500 arrows he released so we might as well take a sample, say 20 arrows. Those 20 arrows might show that the deviation from the bullseye is within 4 inches. So this value can be used to predict the deviation of the 500 arrows consequently released.

Getting the population standard deviation from 20 samples is analogous to a pilot study of the population. A portion of the population may be studied to estimate the population standard deviation. If it is not possible to do so, it is common practice that a standard deviation of 0.5 is used in estimating sample size.

The population standard deviation is computed by getting the square root of the variance. The variance is the average of the squared differences from the mean. This is denoted by the formula given below:

population standard deviation
Fig. 1 Population standard deviation.

Using Confidence Level, Standard Deviation and Margin of Error to Estimate the Sample Size

If you are now ready with at least three measures to estimate sample size, i.e., margin of error, confidence level and standard deviation, then you are now ready to estimate the sample size you need. For example, let’s have the following data:

Given:
Confidence level: 2.326 (the corresponding value in the z table indicating 99% of the population is accounted for)
Standard deviation: 0.5 (assuming that the population standard deviation is unknown)
Margin of error: 5% or 0.05

The following equation is used to compute the sample size:

estimating sample size
Fig. 2. Formula to estimate sample size.

Substituting given values to the equation:

Sample size = ((2.326)² x 0.5(0.5))/(0.05)²
= (5.4103 x 0.25)/ 0.0025
= 1.3526/0.0025
= 541.04 ~ 542 (always round up to the higher integer number)

Therefore, if your research requires interviewing people, the estimated number of interviewees is 542.

References

Niles, R. (n.d.). Standard deviation. Retrieved on 18 February 2015 from http://www.robertniles.com/stats/stdev.shtml

Smith, S. (2013). Determining Sample Size: How to Ensure You Get the Correct Sample Size. Retrieved on 19 February 2015 from http://www.qualtrics.com/blog/determining-sample-size/

©2015 February 22 P. A. Regoniel

A Sample of Conceptual Framework with Statement of the Problem

This article shows how a conceptual framework, along with the corresponding statement of the problem, is organized and written in a dissertation. Take a look at the example on how it is done and try to make one for your paper. You may also use this in your thesis.

You may be thinking about too many theories to base your study on. However, a conceptual framework in built on a theory that serves as the basis for your study. Once you have decided which theory to adopt, try to figure it out if the phenomenon, with all the associated variables in your study, can be best explained by that theory. The example below illustrates how this works.

Example of a Conceptual Framework

This study zeroes in on the professional development activities for teachers by espousing the idea that the classroom performance of teachers is a critical factor for student academic performance. The researcher based her assumption from Weiner’s Attribution Theory that external and internal factors can improve performance.

For example, students may attribute their academic performance to their teachers (external factor) while the teachers may attribute their teaching performance to in-service trainings (external factor) and perhaps, to their teaching efficacy, job satisfaction, and attitude towards the teaching profession (internal factors). These relationships are illustrated in Figure 1.

conceptual framework
Figure 1. Paradigm showing the relationships among the variables in this study.

Statement of the Problem

The purpose of this study is to provide baseline data on in-service training for English, Mathematics, and Science Fourth Year High School teachers from School Year 2006 up to 2010. Also, a professional development model for teachers is proposed.

Specifically, this study sought answers to the following questions:

1. What are the most familiar in-service training activities among teachers? And what are their insights about these activities as to: (a) applicability in the classroom, (b) importance in the teaching profession, and (c) impact on student performance?

2. What feedback do teachers have of the in-service training programs attended in terms of (a) perception, and (b) satisfaction?

3. What are the teachers’ level of teaching efficacy, job satisfaction, and attitude towards the teaching profession?

4. What is the performance of the fourth year high school students in their Subject Achievement Tests in three subject areas: English, Mathematics, and Science during the first semester of SY 2010-2011?

5. Are the teachers’ perception and satisfaction regarding the in-service training programs predictors of their levels of teaching efficacy, job satisfaction, and attitude towards the teaching profession?

6. Are the teachers’ levels of teaching efficacy, job satisfaction, and attitude towards the teaching profession predictors of their student performance in the Subject Achievement Tests?

7. What enhanced professional development model for teachers can be developed on the basis of the results of this study?

Now, you have learned how a theory is used, and how the questions in the statement of the problem are formulated. Take note that the questions in the statement of the problem are arranged according to the flow of conceptual framework. First, it has questions on inventory of in-service training activities, followed by the feedback. The next question is about teacher factors, then results of student performance. The last question relates to the development of the enhanced professional development model.

Can you make it? Yes, you can!

© 2015 January 19 M. G. Alvior

Conceptual Framework: A Step by Step Guide on How to Make One

What is a conceptual framework? How do you prepare one? This article defines the meaning of conceptual framework and lists the steps on how to prepare it. A simplified example is added to strengthen the reader’s understanding.

In the course of preparing your research paper as one of the requirements for your course as an undergraduate or graduate student, you will need to write the conceptual framework of your study. The conceptual framework steers the whole research activity. The conceptual framework serves as a “map” or “rudder” that will guide you towards realizing the objectives or intent of your study.

What then is a conceptual framework in the context of empirical research? The next section defines and explains the term.

Definition of Conceptual Framework

A conceptual framework represents the researcher’s synthesis of literature on how to explain a phenomenon. It maps out the actions required in the course of the study given his previous knowledge of other researchers’ point of view and his observations on the subject of research.

In other words, the conceptual framework is the researcher’s understanding of how the particular variables in his study connect with each other. Thus, it identifies the variables required in the research investigation. It is the researcher’s “map” in pursuing the investigation.

As McGaghie et al. (2001) put it: The conceptual framework “sets the stage” for the presentation of the particular research question that drives the investigation being reported based on the problem statement. The problem statement of a thesis presents the context and the issues that caused the researcher to conduct the study.

The conceptual framework lies within a much broader framework called theoretical framework. The latter draws support from time-tested theories that embody the findings of many researchers on why and how a particular phenomenon occurs.

Step by Step Guide on How to Make the Conceptual Framework

Before you prepare your conceptual framework, you need to do the following things:

  1. Choose your topic. Decide on what will be your research topic. The topic should be within your field of specialization.
  2. Do a literature review. Review relevant and updated research on the theme that you decide to work on after scrutiny of the issue at hand. Preferably use peer-reviewed and well-known scientific journals as these are reliable sources of information.
  3. Isolate the important variables. Identify the specific variables described in the literature and figure out how these are related. Some abstracts contain the variables and the salient findings thus may serve the purpose. If these are not available, find the research paper’s summary. If the variables are not explicit in the summary, get back to the methodology or the results and discussion section and quickly identify the variables of the study and the significant findings. Read the TSPU Technique on how to skim efficiently articles and get to the important points without much fuss.
  4. Generate the conceptual framework. Build your conceptual framework using your mix of the variables from the scientific articles you have read. Your problem statement serves as a reference in constructing the conceptual framework. In effect, your study will attempt to answer a question that other researchers have not explained yet. Your research should address a knowledge gap.

Example of a Conceptual Framework

Statement number 5 introduced in an earlier post titled How to Write a Thesis Statement will serve as the basis of the illustrated conceptual framework in the following examples.

Thesis statement: Chronic exposure to blue light from LED screens (of computer monitors and television) deplete melatonin levels thus reduce the number of sleeping hours among middle-aged adults.

The study claims that blue light from the light emitting diodes (LED) inhibit the production of melatonin, a hormone that regulates sleep and wake cycles. Those affected experience insomnia; they sleep less than required (usually less than six hours), and this happens when they spend too much time working on their laptops or viewing the television at night.

conceptual framework
Fig. 1 The research paradigm illustrating the researcher’s conceptual framework.

Notice that the variables of the study are explicit in the paradigm presented in Figure 1. In the illustration, the two variables are 1) number of hours devoted in front of the computer, and 2) number of hours slept at night. The former is the independent variable while the latter is the dependent variable. Both of these variables are easy to measure. It is just counting the number of hours spent in front of the computer and the number of hours slept by the subjects of the study.

Assuming that other things are constant during the performance of the study, it will be possible to relate these two variables and confirm that indeed, blue light emanated from computer screens can affect one’s sleeping patterns. (Please read the article titled “Do you know that the computer can disturb your sleeping patterns?” to find out more about this phenomenon) A correlation analysis will show whether the relationship is significant or not.

e-Book on Conceptual Framework Development

Due to the popularity of this article, I wrote an e-Book designed to suit the needs of beginning researchers. This e-Book answers the many questions and comments regarding the preparation of the conceptual framework. I provide five practical examples based on existing literature to demonstrate the procedure.

So, do you want a more detailed explanation with five practical, real-life examples? Get the 52-page e-Book NOW!




REFERENCE

McGaghie, W. C.; Bordage, G.; and J. A. Shea (2001). Problem Statement, Conceptual Framework, and Research Question. Retrieved on January 5, 2015 from http://goo.gl/qLIUFg

©2015 January 5 P. A. Regoniel

Cite this article as: Regoniel, Patrick A. (January 5, 2015). Conceptual Framework: A Step by Step Guide on How to Make One. In SimplyEducate.Me. Retrieved from http://simplyeducate.me/2015/01/05/conceptual-framework-guide/

Quantitative Methods: Meaning and Characteristics

What are quantitative methods of research? What is its definition, when are these methods used and what are its characteristics?

This article defines quantitative methods and lists seven characteristics that discriminate these research methods from qualitative research approaches.

The methods used by researchers may either be quantitative or qualitative. The decision to select the method largely depends on the researcher’s judgment as well as the nature of the research topic. Some research topics are better studied using quantitative methods while others are more appropriately explored using qualitative methods.

Recently, many researchers use both methods, thereby the era of using mixed methods in research arose as a more desirable and encompassing approach in understanding phenomena. Qualitative methods may be used to explore a phenomenon and identify factors for a quantitative study. Or, a quantitative study may identify research areas that require the application of qualitative methods to provide an in-depth understanding of the phenomenon at hand or when the use of quantitative methods is insufficient to answer questions that relate to human behavior such as feelings, values, and beliefs.

J. Pizarro has already described qualitative research in this site, so this article focuses on quantitative methods, its meaning and characteristics.

What are quantitative methods?

Quantitative methods are those research methods that use numbers as its basis for making generalizations about a phenomenon. These numbers originate from objective scales of measurement of the units of analysis called variables. Four types of measurement scale exist namely nominal, ordinal, ratio, and interval (see 4 Statistical Scales of Measurement).

The data that will serve as the basis for explaining a phenomenon, therefore, can be gathered through surveys. Such surveys use instruments that require numerical inputs or direct measurements of parameters that characterize the subject of investigation (e.g. pH, dissolved oxygen, salinity, turbidity, and conductivity to measure water quality). These numbers will then be analyzed using the appropriate statistical application software to unravel significant relationships or differences between variables. The output serves as the basis for making the conclusions and generalizations of the study.

7 Characteristics of Quantitative Methods

Seven characteristics discriminate qualitative methods of research from qualitative ones.

  1. Data gathering instruments contain items that solicit measurable characteristics of the population (e.g. age, the number of children, educational status, economic status).
  2. Standardized, pre-tested instruments guide data collection thus ensuring the accuracy, reliability and validity of data.
  3. For more reliable data analysis, a normal population distribution curve is preferred over a non-normal distribution. This requires a large population, the numbers of which depend on how the characteristics of the population vary. This requires adherence to the principle of random sampling to avoid researcher’s bias in interpreting the results that defeat the purpose of research.
  4. The data obtained using quantitative methods are organized using tables, graphs, or figures that consolidate large numbers of data to show trends, relationships, or differences among variables. This fosters understanding to the readers or clients of the research investigation.
  5. Researchers can repeat the quantitative method to verify or confirm the findings in another setting. This reinforces the validity of groundbreaking discoveries or findings thus eliminating the possibility of spurious or erroneous conclusions.
  6. Quantitative models or formula derived from data analysis can predict outcomes. If-then scenarios can be constructed using complex mathematical computations with the aid of computers.
  7. Advanced digital or electronic instruments are used to measure or gather data from the field.

Reference

University of Southern California (2015). Quantitative methods. Retrieved on 3 January, 2015 from http://goo.gl/GMiwt

© 2015 January 3 P. A. Regoniel

A Research on In-service Training Activities, Teaching Efficacy, Job Satisfaction and Attitude

This article briefly discusses the methodology used by Dr. Mary Alvior in the preparation of her dissertation focusing on the benefits of in-service training activities to teachers. She expounds on the results of the study specifically providing descriptive statistics on satisfaction of in-service training to them and how this affected teaching efficacy, job satisfaction, and attitude in public school in the City of Puerto Princesa in the Philippines.

Methodology

This study utilized the research and development method (R&D) which has two phases. During the first phase, the researcher conducted a survey and a focus group interview in order to triangulate the data gathered from the questionnaires. Then, the researcher administered achievement tests in English, Mathematics and Science. The results found in the research component were used as bases for the design and development of a model. The model was then fully structured and improved in the second phase.

The participants were randomly taken from 19 public high schools in the Division of Puerto Princesa City, Palawan. A total of fifty-three (53) teachers participated in the study and 2,084 fourth year high school students took the achievement tests.

The researcher used three sets of instruments which underwent face and content validity. These are

  1. Survey Questionnaires for Teacher Participants,
  2. Guide Questions for Focus Group Interview, and
  3. Teacher-Made Achievement Tests for English, Mathematics, and Science.

The topics in the achievement tests were in consonance with the Philippine Secondary Schools Learning Competencies (PSSLC) while the test items’ levels of difficulty was in accordance with Department of Education (DepEd) Order 79, series of 2003, dated October 10, 2003.

Results of Descriptive Statistics

Teachers’ insights on in-service training activities

Seminar was perceived to be the most familiar professional development activity among teachers but the teachers never considered it very important in their professional practice. They also viewed it applicable in the classroom but it had no impact on student performance.

Aside from seminar, the teachers also included conference, demo lesson, workshop and personal research as the most familiar professional development activities among them.

Nonetheless, teachers had different insights as to which professional development activities were applicable in the classroom. Science teachers considered team teaching, demo lesson, and personal research, but the English and Mathematics teachers considered demo lesson and workshop, respectively.

With regard to the professional development activities that were viewed very important in their professional practice and had great impact on student performance, all subject area teachers answered personal research. However, the Mathematics teachers added lesson study for these two categories while the teachers in Science included team teaching as a professional activity that had great impact on student performance.

Moreover, teachers had high regard for the INSET programs they attended and perceived them effective because they were able to learn and developed themselves professionally. They were also highly satisfied with the training they have attended as indicated in the mean (M=3.03, SD=.34). Particularly, they were highly satisfied with the content, design, and delivery of in-service training (INSET) programs, and with the development of their communication skills, instruction, planning, and organization.

Teachers’ teaching efficacy, job satisfaction and attitude

Teachers had high level of teaching efficacy (M=3.14, SD=.27) particularly on student engagement, instructional strategies, and classroom management but not in Information Communication and Technology (ICT). It seems that they were not given opportunities to hone their skills in ICT or they were not able to use these skills in the classrooms. Likewise, they had an average level of job satisfaction (M=2.91, SD=.27) and had positive attitude towards their teaching profession (M=2.88, SD=.44).

In conclusion, there are professional activities that are viewed very important in teaching and there are also which have great impact on students’ academic performance.  In addition, the study found the inclusion of ICT in teaching and for professional development.

To know more about the model derived from this study, please read 2 Plus 1 Emerging Model of Professional Development for Teachers.

© 2014 December 29 M. G. Alvior

Heart Rate Analysis: Example of t-test Using MS Excel Analysis ToolPak

This article discusses a heart rate t-test analysis using MS Excel Analysis ToolPak add-in. This is based on real data obtained in a personally applied aerobics training program.

Do you know that there is a powerful statistical software residing in the common spreadsheet software that you use everyday or most of the time? If you have installed Microsoft Excel in your computer, chances are, you have not activated a very useful add-in: the Data Analysis ToolPak.

See how MS Excel’s data analysis function was used in analyzing real data on the effect of aerobics on the author’s heart rate.

Statistical Analysis Function of MS Excel

Many students, and even teachers or professors, are not aware that there is a powerful statistical software at their disposal in their everyday interaction with Microsoft Excel. In order to make use of this nifty tool that the not-so-discerning fail to discover, you will need to install it as an Add-in to your existing MS Excel installation. Make sure you have placed your original MS Office DVD in your DVD drive when you do the next steps.

You can activate the Data Analysis ToolPak by following the procedure below (this could vary between versions of MS Excel; this one’s for MS Office 2007):

  1. Open MS Excel,
  2. Click on the Office Button (that round thing at the uppermost left of the spreadsheet),
  3. Look for the Excel Options menu at the bottom right of the box and click it,
  4. Choose Add-ins at the left menu,
  5. Click on the line Analysis ToolPak,
  6. Choose Excel Add-in in the Manage field below left, then hit Go, and
  7. Check the Analysis ToolPak box then click Ok.

You should now see the Data Analysis function at the extreme right of your Data menu in your spreadsheet. You are now ready to use it.

Using the Data Analysis ToolPak to Analyze Heart Rate Data

The aim of this statistical analysis is to test whether there’s really a significant difference in my heart rate eight months ago and last week. This is because in my earlier post titled How to Slow Down Your Heart Rate Through Aerobics, I mentioned that my heart rate is getting slower through time because of aerobics training. But I used the graphical method to plot a trend line. I did not test whether there is a significant difference in my heart rate or not, from the time I started measuring my heart rate compared to the last six weeks’ data.

Now, I would like to answer the question is: “Is there a significant difference in heart rate eight months ago and last six week’s record?”

Student’s t-test will be used to analyze 18 readings taken eight months ago and the last six weeks as data for comparison. I measured my heart rate upon waking up (that ensures I am rested) during each of my three-times a week aerobics sessions.

Why 18? According to Dr. Cooper, the training effect accorded by aerobics could be achieved within six weeks, so I thought my heart rate within six weeks should not change significantly. So that’s six weeks times three equals 18 readings.

Eight months would be a sufficient time to effect a change in my heart rate since I started aerobic running eight months ago. And the trend line in the graph I previously presented shows that my heart rate slows down through time.

These are the assumptions of this t-test analysis and the reason for choosing the sample size.

The Importance of an F-test

Before applying the t-test, the first test you should do to avoid a spurious or false conclusion is to test whether the two groups of data have a different variance. Does one group of data vary more than the other? If they do, then you should not use the t-test. Nonparametric methods such as Mann-Whitney U test should be used instead.

How do you make sure that this may not be the case, that is, that one group of data varies more than the other? The common test to use is an F-test. If no significant difference is detected, then you can go ahead with the t-test.

Here’s an output of the F-test using the Analysis ToolPak of MS Excel:

F test
Fig. 1. F-test analysis using the Analysis ToolPak.

Notice that the p-value for the test is 0.36 [from P(F<=f) one-tail]. This means that one group of data does not vary more than the other.

How do you know that the difference in variance in the two groups of data using the F-test analysis is not significant? Just look at the p-value of the data analysis output and see whether it is equal to or below 0.05. If it is 0.06 or higher, then the difference in variance is not significant and t-test could now be used.

This result signals me to go on with the t-test analysis. Notice that the mean heart rate during the last six weeks (i.e., 50.28) is lower than that obtained six months ago (i.e. 53.78). Is this really significant?

Result of the t-test

I had run a consistent 30-points per week last August and September 2013 but now I accumulate at least a 50-point week for the last six weeks. This means that I almost doubled my capacity to run. And I should have a significantly lower heart rate than before. In fact, I felt that I can run more than my usual 4 miles and I did run more than 6 miles once a week for the last six weeks.

Below is the output of the t-test analysis using the Analysis ToolPak of MS Excel:

t test
Fig. 2. t-test analysis using Analysis ToolPak.

The data shows that there is a significant difference between my heart rate eight months ago and the last three weeks. Why? That’s because the p-value is lower than 0.05 [i.e., P(T<=t) two-tail = 0.0073]. There’s a remote possibility that there is no difference in heart rate 8 months ago and the last six weeks.

I ignored the other p-value because it is one-tail. I just tested whether there is a significant difference or not. But because the p-value in one-tail is also significant, I can confidently say that indeed I have obtained sufficient evidence that aerobics training had slowed down my heart rate, from 54 to 50. Four beats in eight months? That’s amazing. I wonder what will be the lowest heart rate I could achieve with constant training.

This analysis is only true for my case as I used my set of data; but it is possible that the same results could be obtained for a greater number of people.

© 2014 April 28 P. A. Regoniel

How to Slow Down Your Heart Rate Through Aerobics

Do you have a fast heart rate, i.e., more than 80 beats per minute? Chances are, you are either stressed or not getting enough exercise. Find out how aerobics can slow down your heart rate.

I have this nagging question in mind since I decided to undertake an aerobics program using Dr. Kenneth Cooper’s book on aerobics. This is about one’s heart rate getting slower when regularly exercising. Did my heart rate actually slow down because aerobics exercise has become an integral part of my weekly routine?

On page 101 of Dr. Cooper’s book aptly titled “aerobics,” he mentioned that the heart is such a magnificent engine that, when given less work, will work faster and less efficiently. When you make more demands on it through aerobics, it will become more efficient. That means that for a deconditioned man who does not exercise at all, his resting rate is about 80 or more while a conditioned man who exercises regularly, will have a resting heart rate of about 60 beats per minute or less. In 24 hours at rest, a deconditioned man’s heart will have to beat more than a conditioned man. He went on to explain things about the heart and how it becomes stronger and more efficient with training.

While browsing information along this topic, I found out that top athletes have heart rates of less than 30. Miguel Indurain, a top cyclist has a heart rate of 28.

Does Aerobics Slow Down Heart Rate?

I love to do a simple research to test this information although I am aware that there were already studies done to answer this question. I would like to answer the question using myself as the subject of the study and to see my progress. This is my case.

I will deliberately skip the review of literature and go directly to the objective of this experiment. My research question is:

Does aerobics slow down the heart rate through time?

My Method

I decided that I will use the graphical approach to find out if my heart rate indeed is slowing down through time. This is what researchers call a time series analysis. Will the heart rate trend be going down?

I recorded my heart rate each time I check my blood pressure upon waking up in the morning using an OMRON REM-1 wrist blood pressure monitor. So, I have added information that I will include in this article – my blood pressure.

I started recording the BP information and heart rate last August 8, 2013 up to this time. I do this routine before my 6 o’clock am run so it’s basically my resting heart rate after 6-8 hours of sleep. There were no significant changes in my lifestyle (i.e., no changes in diet, medication, workload, among other things) since I embarked on the aerobics program.

I plotted data gathered for eight months although I have done aerobics since January 2013. But then I failed to record heart rate or BP data until August 2013.

Results

I found out interesting information after plotting the data in Excel. This is easily done by plotting the date and corresponding BP values and heart rate in one row. I clicked on the Insert menu then hit the Line graph and selected the cells for date, diastolic, systolic, and heart rate values.

Indeed, my heart rate decreased through time as indicated by the heart rate trend line. However, I noticed that the trend for blood pressure goes towards the opposite direction. Both the systolic and diastolic pressure follow an upward trend (Figure 1).

graph of the heart rate and blood pressure
Fig. 1. Graph of my blood pressure and heart rate from August 19, 2013 to April 19, 2014.

What does this result suggest? This may mean that as the heart grows stronger (low heart beat), the pressure it exerts on the blood vessels also increases. On the other hand, this suggests that my blood vessels become less elastic through time.

This finding requires further reading – a review of literature focused on the relationship between the heart rate of a healthy person and his blood pressure. Is this trend the same for all people who engaged in aerobics and experienced the training effect?

Training effect is the body’s adaptation to a training program manifested by improvement in functional capacity and strength. In my case, this simply means that I am able to run a 6 kilometer stretch of road without stopping to rest. When I started the aerobics program last January 2013, I can barely finish a mile and my legs ached.

Well, whatever the increasing blood pressure means, what is important is that I found out that aerobics does decrease the heart rate through time. On March 4, 2014, I recorded my lowest heart rate ever: 44.  And I confirmed this by manually counting my pulse in one minute. And I also discovered that I can lower it at will by breathing deeply.

Where does this training bring me? An athlete friend invited me to join a 10K run last February 23, 2014. He noticed that I jog regularly and assured me that I will be able to finish the distance. I explained that I have been jogging just to address a health issue and is not that confident to test my performance. On second thought, I said why not?

I realized I can make the distance and gained confidence that I could be a marathoner. In fact, I’ve already joined and finished two 10-kilometer runs clocking 1:05 and 1:00, respectively. And I aim to finish the upcoming 10K run next month in less than an hour. This was made possible through serious self-training and with determination.

Do you have high blood pressure? Or easily feel tired after a few exertions? Try aerobics and take control of your health.

Just a note of caution: before engaging in strenuous exercise, have a medical check up to rule out any heart problem.

© 2014 April 19 P. A. Regoniel