Overview of Quantitative Research Methods

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05/31/2016 Added

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An overview and introduction to quantitative research methods

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[00:00:01.101]Welcome to this overview
[00:00:02.401]of quantitative research methods.
[00:00:04.655]This tutorial will give you the big picture
[00:00:06.598]of quantitative research and introduce key concepts
[00:00:09.808]that will help you determine if quantitative methods
[00:00:11.834]are appropriate for your project study.
[00:00:15.869]First, what is educational research?
[00:00:18.741]Educational research is a process of scholarly inquiry
[00:00:21.903]designed to investigate the process of instruction
[00:00:24.437]and learning, the behaviors, perceptions, and attributes
[00:00:27.456]of students and teachers, the impact of institutional
[00:00:30.906]processes and policies, and all other areas
[00:00:33.403]of the educational process.
[00:00:35.769]The research design may be quantitative,
[00:00:37.889]qualitative, or a mixed methods design.
[00:00:40.963]The focus of this overview is quantitative methods.
[00:00:43.705]The general purpose of quantitative research
[00:00:45.816]is to explain, predict, investigate relationships,
[00:00:49.369]describe current conditions, or to examine possible
[00:00:52.069]impacts or influences on designated outcomes.
[00:00:56.775]Quantitative research differs from qualitative research
[00:00:59.386]in several ways.
[00:01:00.976]It works to achieve different goals
[00:01:02.747]and uses different methods and design.
[00:01:05.145]This table illustrates some of the key differences.
[00:01:08.111]Qualitative research generally uses a small sample
[00:01:10.803]to explore and describe experiences through the use
[00:01:13.763]of thick, rich descriptions of detailed data
[00:01:16.402]in an attempt to understand
[00:01:17.823]and interpret human perspectives.
[00:01:20.086]It is less interested in generalizing
[00:01:21.990]to the population as a whole.
[00:01:24.040]For example, when studying bullying,
[00:01:26.491]a qualitative researcher might learn about the experience
[00:01:29.143]of the victims and the experience of the bully
[00:01:31.651]by interviewing both bullies and victims
[00:01:33.754]and observing them on the playground.
[00:01:36.318]Quantitative studies generally use large samples
[00:01:38.928]to test numerical data by comparing or finding
[00:01:41.596]correlations among sample attributes
[00:01:43.533]so that the findings can be generalized to the population.
[00:01:47.256]If quantitative researchers were studying bullying,
[00:01:49.922]they might measure the effects of a bully on the victim
[00:01:52.472]by comparing students who are victims and students
[00:01:54.526]who are not victims of bullying
[00:01:56.140]using an attitudinal survey.
[00:01:59.470]In conducting quantitative research,
[00:02:01.559]the researcher first identifies the problem.
[00:02:04.287]For Ed. D. research this problem
[00:02:05.957]represents a gap in practice.
[00:02:08.404]For Ph. D. research, this problem
[00:02:09.950]represents a gap in the literature.
[00:02:12.217]In either case, the problem needs to be
[00:02:14.057]of importance in the professional field.
[00:02:16.968]Next, the researcher establishes the purpose of the study.
[00:02:20.410]Why do you want to do this study
[00:02:22.026]and what do you intend to accomplish?
[00:02:24.534]This is followed by research questions,
[00:02:26.582]which help to focus the study.
[00:02:28.936]Once the study is focused,
[00:02:30.410]the researcher needs to review both seminal works
[00:02:32.980]and current peer reviewed primary sources.
[00:02:36.072]Based on the research question
[00:02:37.886]and on a review of prior research,
[00:02:39.808]a hypothesis is created that predicts the relationship
[00:02:42.529]between the study's variables.
[00:02:45.289]Next, the researcher chooses a study design
[00:02:47.694]and method to test the hypothesis.
[00:02:50.341]These choices should be informed by a review
[00:02:52.521]of methodological approaches used to address
[00:02:55.066]similar questions in prior research.
[00:02:58.408]Finally, appropriate analytical methods
[00:03:00.566]are used to analyze the data.
[00:03:02.555]Allowing the researcher to draw conclusions
[00:03:04.505]and inferences about the data.
[00:03:06.261]And answer the research question that was originally posed.
[00:03:11.260]In quantitative research, research questions
[00:03:13.662]are typically descriptive, relational, or causal.
[00:03:17.081]Descriptive questions constrain the researcher
[00:03:19.345]to describing what currently exists.
[00:03:21.899]With a descriptive research question,
[00:03:23.811]one could examine perceptions or attitudes
[00:03:26.136]as well as more concrete variables such as achievement.
[00:03:29.933]For example, one might describe a population of learners
[00:03:33.138]by gathering data on their age, gender, socioeconomic status
[00:03:37.282]and attributes towards their learning experiences.
[00:03:41.215]Relational questions examine the relationship
[00:03:43.867]between two or more variables.
[00:03:45.853]The x variable has some linear
[00:03:47.578]relationship to the y variable.
[00:03:49.997]Causal inferences cannot be made from this type of research.
[00:03:53.712]For example, one could study the relationship
[00:03:56.052]between student's study habits and achievements.
[00:03:58.864]One might find that students using certain kinds
[00:04:01.083]of study strategies demonstrate greater learning.
[00:04:03.864]But one could not state conclusively
[00:04:05.639]that using certain study strategies will
[00:04:07.575]lead to or cause higher achievement.
[00:04:11.210]Causal questions, on the other hand,
[00:04:12.944]are designed to allow the researcher
[00:04:14.868]to draw a causal inference.
[00:04:17.293]A causal question seeks to determine
[00:04:19.236]if a treatment variable in a program had an effect
[00:04:21.227]on one or more outcome variables.
[00:04:23.567]In other words, the x variable influences the y variable.
[00:04:27.719]For example, one could design a study that answered
[00:04:30.290]the question of whether a particular
[00:04:31.865]instructional approach caused students to learn more.
[00:04:35.152]The research question serves as
[00:04:36.462]a basis for posing a hypothesis.
[00:04:39.037]A predicted answer to the research question
[00:04:41.151]that incorporates operational definitions
[00:04:43.180]of the study's variables and is rooted in the literature.
[00:04:46.793]An operational definition matches a concept
[00:04:49.016]with a method of measurement,
[00:04:50.760]identifying how the concept will be quantified.
[00:04:53.586]For example, in a study of instructional strategies
[00:04:56.705]the hypothesis might be that students of teachers
[00:04:59.171]who use strategy x will exhibit greater learning
[00:05:01.731]than students of teachers who do not.
[00:05:04.233]In this study, one would need to operationalize learning
[00:05:07.272]by identifying a task or instrument
[00:05:09.220]that would measure learning.
[00:05:10.890]This approach allows the researcher
[00:05:12.677]to create a testable hypothesis.
[00:05:15.994]Relational and causal research relies on
[00:05:18.168]the creation of a null hypothesis.
[00:05:20.988]A version of the research hypothesis that predicts
[00:05:23.190]no relationship between variables,
[00:05:25.448]or no effect of one variable on another.
[00:05:28.506]When writing the hypothesis for a quantitative question,
[00:05:31.275]the null hypothesis and the research
[00:05:33.097]or alternative hypothesis use parallel sentence structure.
[00:05:37.281]In this example, the null hypothesis states that
[00:05:40.430]there will be no statistical difference between groups.
[00:05:43.829]While the research or alternative hypothesis states
[00:05:46.457]that there will be a statistical difference between groups.
[00:05:50.010]Note also that both hypothesis statements
[00:05:52.311]operationalize the critical thinking skills variable
[00:05:55.530]by identifying the measurement instrument to be used.
[00:05:59.675]Once the research questions and hypotheses are solidified,
[00:06:02.985]the researcher must select a design
[00:06:04.855]that will create a situation in which the hypotheses
[00:06:07.559]can be tested and the research questions answered.
[00:06:11.252]Ideally, the research design will isolate
[00:06:12.981]the study's variables and control for intervening variables
[00:06:16.322]so that one can be certain
[00:06:17.701]of the relationships being tested.
[00:06:20.012]In educational research however,
[00:06:21.769]it is extremely difficult to establish sufficient controls
[00:06:25.017]in the complex social settings being studied.
[00:06:27.846]In our example of investigating the impact
[00:06:30.099]of a certain instructional strategy in the classroom
[00:06:32.599]on student achievement,
[00:06:33.964]each day the teacher uses a specific instructional strategy.
[00:06:38.074]After school some of the students
[00:06:39.820]in her class receive tutoring.
[00:06:42.134]Other students have parents that are very
[00:06:43.759]involved in their child's academic progress
[00:06:45.873]and provide learning experiences in the home.
[00:06:49.193]These students may be do better
[00:06:50.580]because they received extra help,
[00:06:52.505]not because the teacher's instructional
[00:06:54.220]strategy is more effective.
[00:06:56.482]Unless the researcher can control for the intervening
[00:06:58.992]variable of extra help, it will be impossible
[00:07:01.918]to effectively test the study's hypothesis.
[00:07:05.868]Quantitative research designs
[00:07:07.329]can fall into two broad categories:
[00:07:09.573]experimental and quasi-experimental.
[00:07:12.738]Classic experimental designs are those that randomly
[00:07:15.196]assign subjects to either a control
[00:07:16.988]or treatment comparison group.
[00:07:19.472]The researcher can then compare the treatment group
[00:07:21.729]to the control group to test for an intervention's effect.
[00:07:24.987]Known as a between subject design.
[00:07:27.833]It is important to note that the control group
[00:07:29.760]may receive a standard treatment
[00:07:31.400]or may receive a treatment of any kind.
[00:07:34.589]Quasi-experimental designs do not
[00:07:36.311]randomly assign subjects to groups,
[00:07:38.140]but rather take advantage of existing groups.
[00:07:40.750]A researcher can still have a control and comparison group
[00:07:44.046]but assignment to the groups is not random.
[00:07:46.796]The use of a control group is not required.
[00:07:49.014]However, the researcher may choose a design
[00:07:51.311]in which a single group is pre and post tested.
[00:07:54.251]Known as a within subjects design.
[00:07:56.477]Or a single group may receive only a post test.
[00:08:00.322]Since quasi-experimental designs lack random assignment,
[00:08:03.664]the researcher should be aware of the threats to validity.
[00:08:07.325]Educational research often attempts to measure abstract
[00:08:10.589]variables such as attitudes, beliefs, and feelings.
[00:08:14.368]Surveys can capture data about these
[00:08:16.282]hard to measure variables,
[00:08:17.740]as well as other self reported information
[00:08:19.951]such as demographic factors.
[00:08:21.838]A survey is an instrument used to collect
[00:08:23.615]verifiable information from a sample population.
[00:08:27.199]In quantitative research,
[00:08:28.700]surveys typically include questions that ask respondents
[00:08:31.636]to choose a rating from a scale,
[00:08:33.758]select one or more items from a list,
[00:08:35.882]or other responses that result in numerical data.
[00:08:40.319]Studies that use surveys or tests
[00:08:42.281]need to include strategies that establish
[00:08:44.234]the validity of the instrument used.
[00:08:46.425]There are many types of validity that need to be addressed.
[00:08:50.048]Face validity.
[00:08:51.092]Does the test appear at face value
[00:08:53.052]to measure what it is supposed to measure?
[00:08:56.047]Content validity.
[00:08:57.904]Content validity includes both item validity
[00:09:00.017]and sampling validity.
[00:09:02.048]Item validity ensures that the individual test items
[00:09:04.872]deal only with the subject being addressed.
[00:09:07.857]Sampling validity ensures that the range
[00:09:09.720]of item topics is appropriate to the subject being studied.
[00:09:13.709]For example,
[00:09:14.779]item validity might be high
[00:09:16.230]but if all the items only deal
[00:09:17.861]with one aspect of the subjects,
[00:09:19.686]then sampling validity is low.
[00:09:22.341]Content validity can be established by having
[00:09:24.511]experts in the field review the test.
[00:09:27.631]Concurrent validity.
[00:09:29.186]Does a new test correlate with an older,
[00:09:31.171]established test that measures the same thing?
[00:09:34.375]Predictive validity.
[00:09:35.539]Does the test correlate with another related measure?
[00:09:38.624]For example, GRE tests are used at many colleges
[00:09:41.527]because these schools believe that a good grade
[00:09:43.649]on this test increases the probability
[00:09:45.961]that the student will do well at the college.
[00:09:48.832]Linear regression can establish
[00:09:50.379]the predictive validity of a test.
[00:09:53.128]Construct validity.
[00:09:54.599]Does the test measure the construct
[00:09:56.546]it is intended to measure?
[00:09:58.728]Establishing construct validity can be a difficult task
[00:10:01.443]when the constructs being measured are abstract.
[00:10:04.549]But it can be established by conducting
[00:10:06.350]a number of studies in which you test
[00:10:08.356]hypotheses regarding the construct.
[00:10:10.428]Or by completing a factor analysis
[00:10:12.185]to ensure that you have the number of constructs
[00:10:14.657]that you say you have.
[00:10:16.947]In addition to ensuring the validity of instruments,
[00:10:19.583]the quantitative researcher needs
[00:10:21.306]to establish their reliability as well.
[00:10:24.192]Strategies for establishing reliability include,
[00:10:27.615]Test-Retest.
[00:10:28.807]Correlate scores from two different
[00:10:30.449]administrations of the same test.
[00:10:33.431]Alternate forms.
[00:10:35.025]Correlate scores from administrations
[00:10:36.937]of two different forms of the same test.
[00:10:39.761]Split-half reliability,
[00:10:41.704]treats each half of one test or survey
[00:10:44.215]as a separate administration
[00:10:45.785]and correlates the results from each.
[00:10:49.120]Internal consistency,
[00:10:51.142]uses Cronbach's coefficient alpha
[00:10:53.404]to calculate the average of all possible split halves.
[00:10:57.798]Quantitative research almost always relies
[00:10:59.883]on a sample that is intended to be representative
[00:11:02.128]of a larger population.
[00:11:04.621]There are two basic sampling strategies,
[00:11:06.725]random and non-random.
[00:11:08.847]And a number of specific strategies
[00:11:10.492]within each of these approaches.
[00:11:12.786]This table provides examples
[00:11:14.263]of each of the major strategies.
[00:11:17.370]The next section of this tutorial
[00:11:19.003]provides an overview of the procedures in conducting
[00:11:21.709]quantitative data analysis.
[00:11:23.859]There are specific procedures
[00:11:25.223]for conducting the data collection.
[00:11:27.236]Preparing for and analyzing data,
[00:11:29.159]presenting the findings,
[00:11:30.870]and connecting to the body of existing research.
[00:11:34.534]This process ensures that the research
[00:11:36.137]is conducted as a systematic investigation
[00:11:38.392]that leads to credible results.
[00:11:41.901]Data comes in various sizes and shapes.
[00:11:44.509]And it is important to know about these
[00:11:46.531]so that the proper analysis can be used on the data.
[00:11:49.596]In 1946, S.S. Stevens first described the properties
[00:11:53.075]of measurement systems that allowed decisions
[00:11:55.173]about the type of measurement and about the attributes
[00:11:57.267]of objects that are preserved in numbers.
[00:11:59.531]These four types of data are referred to as
[00:12:01.959]nominal, ordinal, interval,
[00:12:04.810]and ratio.
[00:12:06.523]First let's examine nominal data.
[00:12:09.151]With nominal data there is no number value
[00:12:11.266]that indicates quantity.
[00:12:12.816]Instead, a number has been assigned
[00:12:14.534]to represent a certain attribute
[00:12:16.739]like the number one to represent male
[00:12:18.906]and the number two to represent female.
[00:12:21.828]In other words, the number is just a label.
[00:12:24.576]We could also assign numbers to represent
[00:12:26.568]race, religion, or any other categorical information.
[00:12:30.536]Nominal data only denotes group membership.
[00:12:34.407]With ordinal data, there is again no indication of quantity.
[00:12:37.889]Rather, a number is assigned for ranking order.
[00:12:41.000]For example, satisfaction surveys often ask respondents
[00:12:44.032]to rank order their level of satisfaction
[00:12:46.183]with services or programs.
[00:12:48.503]The next level of measurement is interval data.
[00:12:51.079]With interval data there are equal distances
[00:12:53.070]between two values,
[00:12:54.224]but there is no natural zero.
[00:12:56.612]A common example is the Fahrenheit temperature scale.
[00:12:59.691]Differences between the temperature measurements
[00:13:01.770]make sense but ratios do not.
[00:13:04.245]For instance, 20 degrees Fahrenheit is not
[00:13:06.645]twice as hot as 10 degrees Fahrenheit.
[00:13:09.177]You can add and subtract interval level data,
[00:13:11.442]but they can not be divided or multiplied.
[00:13:14.936]Finally, we have ratio data.
[00:13:17.059]Ratio is the same as interval,
[00:13:19.086]however ratios, means, averages,
[00:13:21.627]and other numerical formulas
[00:13:23.107]are all possible and make sense.
[00:13:25.714]Zero has a logical meaning,
[00:13:27.476]which shows the absence of or having none of.
[00:13:31.527]Examples of ratio data are height, weight, speed,
[00:13:34.995]or any quantities based on a scale with a natural zero.
[00:13:39.326]In summary,
[00:13:40.297]nominal data can only be counted,
[00:13:42.572]ordinal data can be counted and ranked,
[00:13:45.288]interval data can also be added and subtracted,
[00:13:48.358]and ratio data can also be used
[00:13:49.968]in ratios and other calculations.
[00:13:52.323]Determining what type of data you have
[00:13:54.691]is one of the most important aspects
[00:13:56.287]of quantitative analysis.
[00:13:59.391]Depending on the research question,
[00:14:01.157]hypotheses, and research design,
[00:14:02.883]the researcher may choose to use descriptive
[00:14:05.421]and or inferential statistics to begin to analyze the data.
[00:14:09.441]Descriptive statistics are best illustrated
[00:14:11.411]when viewed through the lens of America's past times.
[00:14:14.367]Sports, weather, economy, stock market,
[00:14:17.019]and even our retirement portfolio are presented
[00:14:19.344]in a descriptive analysis.
[00:14:21.656]Basic terminology for descriptive statistics
[00:14:23.857]are terms that we are most familiar in this discipline.
[00:14:27.184]Frequency, mean, median,
[00:14:29.225]mode,
[00:14:30.058]range,
[00:14:30.891]variance, and standard deviation.
[00:14:34.229]Simply put, you are describing the data.
[00:14:36.965]Some of the most common graphic representations of data
[00:14:39.737]are bar graphs, pie graphs, histograms,
[00:14:42.428]and box and whisker graphs.
[00:14:45.188]Attempting to reach conclusions and make causal inferences
[00:14:47.936]beyond graphic representations or descriptive analyses
[00:14:51.634]is referred to as inferential statistics.
[00:14:54.457]In other words, examining the college enrollment
[00:14:56.792]of the past decade in a certain geographical region
[00:14:59.542]would assist in estimating what the enrollment
[00:15:01.775]for the next year might be.
[00:15:04.673]Frequently in education,
[00:15:05.851]the means of two or more groups are compared.
[00:15:08.695]When comparing means to assist
[00:15:10.393]in answering a research question,
[00:15:12.061]one can use a within-group, between-groups,
[00:15:14.874]or mixed subjects design.
[00:15:17.123]In a within-group design, the researcher compares
[00:15:19.847]measures of the same subjects across time.
[00:15:22.493]Therefore, within group.
[00:15:24.421]Or under different treatment conditions.
[00:15:26.639]This can also be referred to as a dependent group design.
[00:15:30.446]The most basic example of this type
[00:15:32.080]of quasi-experimental design
[00:15:34.062]would be if a researcher conducted
[00:15:35.753]a pre test of a group of students,
[00:15:37.640]subjected them to a treatment,
[00:15:39.385]and then conducted a post test.
[00:15:41.474]The group has been measured at different points in time.
[00:15:45.082]In a between-group design,
[00:15:46.742]subjects are assigned to one of
[00:15:48.374]the two or more groups.
[00:15:50.227]For example, control, treatment one, treatment two.
[00:15:54.363]Ideally the sampling and assignment groups would be random.
[00:15:57.552]Which would make this an experimental design.
[00:16:00.235]The researcher can then compare the means
[00:16:01.826]of the treatment group to the control group.
[00:16:04.253]When comparing two groups,
[00:16:05.689]the researcher can gain insight
[00:16:07.093]into the effects of the treatment.
[00:16:09.193]In a mixed subjects design,
[00:16:11.064]the researcher is testing for significant differences
[00:16:13.465]between two or more independent groups
[00:16:15.911]while subjecting them to repeated measures.
[00:16:19.732]Choosing a statistical test to compare groups
[00:16:22.159]depends on the number of groups,
[00:16:23.976]whether the data are nominal, ordinal, or interval,
[00:16:26.965]and whether the data meet the assumptions
[00:16:28.776]for parametric tests.
[00:16:30.785]Non-parametric tests are typically used
[00:16:32.759]with nominal and ordinal data.
[00:16:34.848]While parametric tests use interval and ratio level data.
[00:16:38.566]In addition to this,
[00:16:39.697]some further assumptions are made for parametric tests,
[00:16:42.490]that the data are normally distributed in the population,
[00:16:45.382]that participant selection is independent
[00:16:47.887]and the selection of one person
[00:16:49.417]does not determine the selection of another.
[00:16:51.804]And that the variances of the groups
[00:16:53.379]being compared are equal.
[00:16:55.494]The assumption of independent participant selection
[00:16:58.029]cannot be violated but the others are more flexible.
[00:17:02.311]The t-test assesses whether the means of two groups
[00:17:04.878]are statistically different from each other.
[00:17:07.537]This analysis is appropriate whenever you
[00:17:09.696]want to compare the means of two groups.
[00:17:11.942]And especially appropriate as the method of analysis
[00:17:14.547]for a quasi-experimental design.
[00:17:17.063]When choosing a t-test,
[00:17:18.349]the assumptions are that the data are parametric.
[00:17:22.252]The analysis of variance, or ANOVA,
[00:17:24.342]assesses whether the means of more than two groups
[00:17:26.665]are statistically different from each other.
[00:17:28.886]When choosing an ANOVA,
[00:17:30.133]the assumptions are that the data are parametric.
[00:17:33.047]The chi-square test can be used
[00:17:34.686]when you have nonparametric data
[00:17:36.340]and want to compare differences between groups.
[00:17:39.515]The Kruskall-Wallis test can be used
[00:17:41.155]when there are more than two groups
[00:17:42.635]and the data are nonparametric.
[00:17:46.185]Correlation analysis is a set of statistical tests
[00:17:48.652]to determine whether there are linear relationships
[00:17:50.934]between two or more sets of variables
[00:17:53.289]from the same list of items or individuals.
[00:17:55.803]For example, achievement and performance of students.
[00:17:59.157]The tests provide a statistical yes or no
[00:18:01.303]as to whether a significant relationship
[00:18:03.406]or correlation exists between the variables.
[00:18:06.778]A correlation test consists of calculating
[00:18:08.897]a correlation coefficient between two variables.
[00:18:12.163]Again, there are parametric and nonparametric choices
[00:18:14.775]based on the assumptions of the data.
[00:18:17.154]Pearson r correlation is widely used in statistics
[00:18:20.318]to measure the strength of the relationship
[00:18:22.031]between linearly related variables.
[00:18:24.535]Spearman rank correlation is a nonparametric test
[00:18:27.314]that is used to measure the degree of association
[00:18:29.548]between two variables.
[00:18:31.930]Spearman rank correlation test does not assume
[00:18:34.297]any assumptions about the distribution.
[00:18:37.018]Spearman rank correlation test is used
[00:18:38.995]when the Pearson test gives misleading results.
[00:18:42.284]Often a Kendall Tau is also included
[00:18:44.165]in this list of nonparametric correlation tests
[00:18:46.844]to examine the strength of the relationship
[00:18:48.801]if there are less than 20 rankings.
[00:18:51.844]Linear regression and correlation
[00:18:53.699]are similar and often confused.
[00:18:56.230]Sometimes your methodologist will encourage
[00:18:58.181]you to examine both the calculations.
[00:19:01.196]Calculate linear correlation if you measured both variables,
[00:19:04.447]x and y.
[00:19:05.701]Make sure to use the Pearson parametric
[00:19:07.972]correlation coefficient if you are certain
[00:19:10.203]you are not violating the test assumptions.
[00:19:13.174]Otherwise, choose the Spearman nonparametric
[00:19:15.656]correlation coefficient.
[00:19:17.411]If either variable has been
[00:19:18.535]manipulated using an intervention,
[00:19:20.462]do not calculate a correlation.
[00:19:22.907]While linear regression does indicate the nature
[00:19:25.171]of the relationship between two variables,
[00:19:27.315]like correlation,
[00:19:28.601]it can also be used to make predictions.
[00:19:30.722]Because one variable is considered explanatory,
[00:19:33.829]while the other is considered a dependent variable.
[00:19:37.518]Establishing validity is a critical part
[00:19:39.809]of quantitative research.
[00:19:41.651]As with the nature of quantitative research,
[00:19:43.946]there is a defined approach or process
[00:19:45.940]for establishing validity.
[00:19:47.556]This also allows for the finding's transferability.
[00:19:50.583]For a study to be valid,
[00:19:52.039]the evidence must support the interpretations of the data.
[00:19:55.123]The data must be accurate
[00:19:56.544]and their use in drawing conclusions
[00:19:58.412]must be logical and appropriate.
[00:20:01.073]Construct validity concerns whether what you did
[00:20:03.332]for the program was what you wanted to do.
[00:20:05.671]Or whether what you observed was what you wanted to observe.
[00:20:08.689]Construct validity concerns whether the operationalization
[00:20:11.786]of your variables are related to the theoretical concept
[00:20:14.898]you are trying to measure.
[00:20:16.483]Are you actually measuring what you want to measure?
[00:20:19.890]Internal validity means that you have evidence
[00:20:21.999]that what you did in the study,
[00:20:23.617]i.e. the program,
[00:20:25.013]caused what you observed,
[00:20:26.280]i.e. the outcome,
[00:20:27.308]to happen.
[00:20:28.579]Conclusion validity is the degree
[00:20:30.202]to which conclusions drawn about
[00:20:31.838]relationships in the data are reasonable.
[00:20:35.268]External validity concerns the process of generalizing.
[00:20:38.423]Or the degree to which the conclusions in your study
[00:20:40.908]would hold for other persons in other places
[00:20:43.168]and at other times.
[00:20:45.437]Establishing reliability and validity to your study
[00:20:48.466]is one of the most critical elements
[00:20:50.020]of the research process.
[00:20:52.762]Once you have decided to embark upon the process
[00:20:55.208]of conducting a quantitative study,
[00:20:57.095]use the following steps to get started.
[00:20:59.995]First, review research studies that have been conducted
[00:21:02.679]on your topic to determine what methods were used.
[00:21:05.916]Consider the strengths and weaknesses
[00:21:07.527]of the various data collection and analysis methods.
[00:21:11.009]Next, review the literature
[00:21:12.466]on quantitative research methods.
[00:21:14.484]Every aspect of your research
[00:21:16.227]has a body of literature associated with it.
[00:21:19.172]Just as you would not confine yourself
[00:21:21.026]to your course textbooks for your
[00:21:22.628]review of research on your topic,
[00:21:24.347]you should not limit yourself to your course texts
[00:21:26.505]for your review of methodological literature.
[00:21:29.045]Read broadly and deeply from the scholarly literature
[00:21:31.663]to gain expertise in quantitative research.
[00:21:34.846]Additional self paced tutorials have been developed
[00:21:37.385]on different methodologies and techniques
[00:21:39.344]associated with quantitative research.
[00:21:41.936]Make sure that you complete all of the self paced
[00:21:44.541]tutorials and review them as often as needed.
[00:21:47.025]You will then be prepared to complete
[00:21:48.892]a literature review of the specific methodologies
[00:21:51.298]and techniques that you will use in your study.

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Overview of Quantitative Research Methods

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