The Simple ExperimentHow researcher

The Simple Experiment
How researchers Find Cause-and-Effect relationships
By Kendra Cherry
Simple experiments can be useful when looking for causal relationships.
When researchers are trying to determine if changes in one variable lead to changes in another variable, they must perform experiments in order to establish a causal relationship.Other research methods (such as correlational studies) can be used to establish that a relationship between to variables exists , but an actual experiment is necessary to establish that it is acause – and – effect type of relationship.
Experiments can be extremely complex and included a multitude of variables However,one of the most basic methods is to use what is known as a simple experiment .
What is a Simple Experiment ?
A simple experiment can establish cause-and-effect , so this type of study is often used to determine the effect of a treatment.For examples ,researchers might want to determine if administering a certain type of medicine leads to an improvement of symptoms . In simple experiment,study participants are randomly assigned to One of two groups .Generally , one group is the control group and receives no treatment while the other group is the experimental group and receives the treatment .
Parts of a simple experiment
The simple experiment is composed of a few key elements :-
The experimental hypothesis : A statement that predicts that the treatment will cause an effect . The experimental hypothesis will always be phrased as a cause-and-effect statement for example , researchers might propose a hypothesis that : “Administration of Medicine effect of Medicine A will result in areduction of symptoms of Disease b” .
The null hypothsis : A hypothesis that the experimental treatment will have no effect on participants or dependent variables . it is important to note that failing to find an impact another variable that the researchers are not measuring in the current experiment .
The independent variable : The treatment variable that is manipulated by the experimenter .
The dependent variable : The response that the experimenter is measuring .
The control group : This group is made up of individuals who are randomly assigned to a group but donot receive the treatment .The measures takes from the control group are then compared to those in the experimental group to determine if the treatment had an effect .

The experimental group : This group is made up of individuals who are randomly assigned to the group and then receive the treatment . The scores of these participants are compared to those in the control group to determine if the treatment had an effect .
Determining the results of a Simple Experiment
Once the data from the simple experiment has been gathered , researchers then compare the results of the experimental group to those of the control group to determine if the treatment had an effect .How do researchers determine this effect ? Due to the always present possibility of errors , we can never be 100% sure of the relationship between two variables After all , There might always exist some unknown variables that we are unaware of or unable to measure that might nevertheless have an influence over the outcomes .Despite this ever – present problem , there are ways to determine if there most likely is a meaningful relationship.

Experimenters use inferential statistics to determine if the results of experiment are meaningful .Inferential statistics is a branch of science that deals with drawing inferences about a population based upon measures taken from a representative sample of that population .
The key to determining if a treatment had an effect is to measure the statistical significance. Statistical significance shows that the relationship between the variables is probably not due to mere chance and that a real relationship most likely exists between the two variables.
Statistical significance is often represented like this :
P

Results (Arabic) 1:

تجربة بسيطةكيف يجد الباحثون السبب والنتيجة علاقاتبالكرز كندراتجارب بسيطة يمكن أن تكون مفيدة عندما تبحث عن علاقات سببية.عندما يحاول الباحثون تحديد إذا التغييرات في متغير واحد إلى التغييرات في متغير آخر، يجب إجراء التجارب بغية إقامة علاقة سببية.يمكن استخدام أساليب البحث الأخرى (مثل دراسات علائقية) إثبات أن وجود علاقة بين المتغيرات، ولكن تجربة الفعلية اللازمة إثبات أنه هو أكاوسي----وتأثير نوع من العلاقة.تجارب يمكن أن تكون معقدة للغاية، وشملت العديد من المتغيرات ومع ذلك، واحد الأساليب الأساسية استخدام ما يعرف بتجربة بسيطة.ما هي "تجربة بسيطة"؟تجربة بسيطة يمكن إثبات السبب والنتيجة، حيث غالباً ما يتم استخدام هذا النوع من الدراسة تحديد مدى تأثير العلاج.للحصول على أمثلة، قد تريد الباحثين تحديد إذا كانت الدولة القائمة بنوع معين من الدواء تؤدي إلى تحسن أعراض. في تجربة بسيطة، يتم تعيين المشاركين في الدراسة عشوائياً إلى واحدة من هاتين المجموعتين.عموما، فريق واحد هو السيطرة على المجموعة، ويتلقى أي علاج في حين المجموعة الأخرى المجموعة التجريبية ويتلقى العلاج.أجزاء من تجربة بسيطةتجربة بسيطة وتتكون من عدد قليل من العناصر الرئيسية:-الفرضية التجريبية: بيان أن يتنبأ بأن العلاج سوف تتسبب في تأثير. الفرضية التجريبية سوف دائماً تكون صيغته كبيان السبب والنتيجة على سبيل المثال، والباحثين قد تقترح الفرضية القائلة أن: "سيؤدي إلى الإدارة لتأثير الطب الطب A أريدوكتيون أعراض المرض ب".هيبوثسيس null: فرضية أن العلاج التجريبي لن يكون أي تأثير على المشتركين أو تعتمد على المتغيرات. من المهم أن نلاحظ أن الفشل في العثور على تأثير متغير آخر لم يتم قياس الباحثون في التجربة الحالية.المتغير المستقل: متغير العلاج التي يتم التلاعب بها من قبل المجرب.المتغير التابع: الرد الذي هو قياس المجرب.مجموعة المراقبة: تتكون هذه المجموعة من الأفراد الذين يكلفون عشوائياً إلى مجموعة ولكن donot تلقي العلاج.ثم يتم مقارنة تحيط التدابير من السيطرة على المجموعة لتلك الموجودة في المجموعة التجريبية لتحديد إذا كان العلاج أثر.المجموعة التجريبية: تتكون هذه المجموعة من الأفراد الذين تم تعيينها عشوائياً إلى المجموعة وثم الحصول العلاج. العشرات من هؤلاء المشاركين مقارنة بتلك التي في المجموعة عنصر التحكم لتحديد ما إذا كان العلاج أثر.تحديد نتائج "تجربة بسيطة"مرة واحدة وقد تم جمع البيانات المستمدة من تجربة بسيطة،، ثم قارن الباحثون نتائج المجموعة التجريبية لتلك المجموعة عنصر التحكم لتحديد ما إذا كان العلاج أثر.كيف تحديد الباحثين هذا التأثير؟ نظراً لإمكانية دائماً الحالية من أخطاء، نحن يمكن أن يكون متأكداً 100 ٪ من العلاقة بين متغيرين بعد كل، قد توجد هناك دائماً بعض المتغيرات غير معروف التي نحن على علم أو غير قادر على قياس قد تكون مع ذلك تأثير على النتائج.وعلى الرغم من هذا من أي وقت مضى – المشكلة، هناك طرق لتحديد ما إذا كان هناك على الأرجح علاقة ذات مغزى.استخدام المجربون استنتاجي الإحصاءات لتحديد ما إذا كانت نتائج التجربة ذات معنى.استنتاجي الإحصاءات هي فرع العلم الذي يتعامل مع استخلاص النتائج حول عدد سكان استناداً إلى التدابير المتخذة من عينة تمثيلية للسكان.المفتاح لتحديد ما إذا كان علاج أثرا لقياس الدلالة الإحصائية. دلالة إحصائية تبين أن العلاقة بين المتغيرات يرجع ربما ليس من قبيل الصدفة، وأنه توجد علاقة حقيقية على الأرجح بين اثنين من المتغيرات.وكثيراً ما يمثل دلالة إحصائية مثل هذا:ف < 0.05فقيمه أقل من 0.05 تشير إلى إذا كانت نتائج خاصة تعزى إلى مجرد الصدفة، يكون احتمال الحصول على هذه النتائج سوف تكون أقل من 5% أحياناً، ينظر إلى قيم P أصغر مثل P < 0.01.وهناك عدد من الوسائل المختلفة لقياس الدلالة الإحصائية.نوع الاختبار الإحصائية المستخدمة يعتمد إلى حد كبير على نوع تصميم البحوث التي تم استخدامها.القضية 3في هذه الحالة، الوسيط متغير مستمر والمتغير المستقل انقسام. فعلى سبيل المثال، قد يكون المتغير المستقل رشيد مقابل الخوف – إثارة الموقف تغير الرسالة وقد تكون مديرة الاستخبارات كما تقاس باختبار الذكاء. رسالة تثير الخوف قد يكون أكثر فعالية لمنخفض – مواضيع الذكاء، بينما الرسالة الرشيد قد تكون أكثر فعالية فيما يتعلق بارتفاع معدل الذكاء المواضيع. لقياس آثار الوسيط في هذه الحالة، يجب علينا أولوية كيف يختلف تأثير المتغير المستقل كوظيفة من وظائف الوسيط. من المستحيل تقييم فرضية عامة أن يتغير تأثير المتغيرات المستقلة كوظيفة من وظائف الوسيط لأن الوسيط قد العديد من المستويات.يعرض الرقم 2 ثلاث طرق المثالية التي يغير الوسيط أثر المتغير المستقل على المتغير التابع.، أولاً، أثر المتغير المستقل على تغييرات متغير تابع خطيا فيما يتعلق بالوسيط فرضية الخطي يمثل تغيير التدريجي والمطرد في أثر المتغير المستقل على المتغير التابع كما يغير مشرف. هذا شكل من أشكال الاعتدال التي يفترض عموما استشهادات الوظيفة الثانية في الشكل هي الدالة التربيعية. فعلى سبيل المثال، رسالة تثير الخوف قد يكون عموما أكثر فعالية من رسالة الرشيد لجميع منخفضة – الذكاء-المواضيع، ولكن كلما زاد معدل الذكاء، فالرسالة التي تثير الخوف يفقد ميزته ورسالة الرشيد أكثر فعالية.الوظيفة الثالثة في الشكل 2 دالة خطوة.في بعض مستوى الذكاء الحرجة، يصبح الرسالة عقلانية أكثر فعالية من رسالة تثير الخوف.يتم اختبار هذا النمط من ديتشوتوميزينج الوسيط في النقطة حيث هو خطوة من المفترض أن يحدث والدعوى كما هو الحال في حالة (1).لسوء الحظ. عادة النظريات في علم النفس الاجتماعي ليست دقيقة بما يكفي لتحديد نقطة الدقيق الذي يحدث الخطوة في الدالة.The linear hypothesis is tested by adding the product of moderator and the dichotomous independent variable to the regression equation , as described by Cohen and Cohen (1983) and cleary and Kessler(1982) , so if the independent variable is noted as X , the moderator as Z , and the dependent variables as Y , Y is regressed on X , Y and XZ . Moderator effects are indicated by the significant effect of XZ while X and Z are controlled The simple effects of the independent variablefor different levels of moderator can be measured and tested by procedures described by Aiken and West (1986) . (Measurement error in the moderator requires the same remedies as measurement errir in the independent variable under case 2 .)The quadratic moderation effect can be tested by dichotomizing the moderator at the point at which function is presented to accelerate .If the function is quadratic , as in Figure 2 , the effect of independent variable should be greatest for those who are high on the moderator .Alternatively quadratic moderation can be tested by hierarchical regression procedures described by Cohen and Cohen (1983) . Using the same notation as in the previous paragraph , Y is regressed on X , Z ,XZ ,Z² , and XZ² ,the test of quadratic moderation is given by the rest of XZ² . The interpretation of this complicated regression equation can be aided by graphing or tabling the oriented values for various values of X and Z .Case 4 :-In this case both the moderator variable and the independent variable are continuous . If one believes that the moderator alters the independent – dependent variable relation in a step function (the bottom diagram in figure 2) , One can dichotomize the moderator at the point where the step takes place . After dichotomizing the moderator , the Pattern becomes Case 2 . The measure if the effect of the independent variable is a regression Co-efficient .If One presumes that the effect of the independent variable (X) on the dependent variable (Y) varies linearly or quadratically with respect to moderator (Z) , The product variable approach described in Case 3 should be used . For Quadratic moderation , The moderator squared must be introduced .One should consult Cohen and Cohen (1983) and cleary and Kessler (1982) for assistance in setting up the interpreting these regressions .The presence of measurements error in either the moderator or independent variable under Case 4 greatly complicates the analysis . Busemeyer and Jones (1983) assumed the moderation is linear and so can be captured by an XZ product term . they showed that measuring multiplicative interactions when on of the variables has measurement error results in low power in the test of interactive effect .Methods present y Kenny and Judd (1984) can be used to make adjustments fore measurement error in the variables , resulting in proper estimates of interactive effects , However m These methods require that the variables from which the product variable is formed have normal distributions .The Nature Mediator VariablesAlthough the systematic search for moderator variables is relatively recent , psychologists have long recognized the importance of mediating variables . Woodworth`s (1928) S-O-R model which recognizes that an active organism intervenes between stimulus and response , is perhaps the most generic formulation of mediation hypothesis . The central idea in this model is that the effects of stimuli on behavior are mediated by various transformation processes internal to the organism theorists as diverse as Hull , Tolman , and Lewin shared belief in the importance of postulating entities or processes the intervene between input and output . ( Skinn

Results (Arabic) 2:

The Simple Experiment
How researchers Find Cause-and-Effect relationships
By Kendra Cherry
Simple experiments can be useful when looking for causal relationships.
When researchers are trying to determine if changes in one variable lead to changes in another variable, they must perform experiments in order to establish a causal relationship.Other research methods (such as correlational studies) can be used to establish that a relationship between to variables exists , but an actual experiment is necessary to establish that it is acause – and – effect type of relationship.
Experiments can be extremely complex and included a multitude of variables However,one of the most basic methods is to use what is known as a simple experiment .
What is a Simple Experiment ?
A simple experiment can establish cause-and-effect , so this type of study is often used to determine the effect of a treatment.For examples ,researchers might want to determine if administering a certain type of medicine leads to an improvement of symptoms . In simple experiment,study participants are randomly assigned to One of two groups .Generally , one group is the control group and receives no treatment while the other group is the experimental group and receives the treatment .
Parts of a simple experiment
The simple experiment is composed of a few key elements :-
The experimental hypothesis : A statement that predicts that the treatment will cause an effect . The experimental hypothesis will always be phrased as a cause-and-effect statement for example , researchers might propose a hypothesis that : “Administration of Medicine effect of Medicine A will result in areduction of symptoms of Disease b” .
The null hypothsis : A hypothesis that the experimental treatment will have no effect on participants or dependent variables . it is important to note that failing to find an impact another variable that the researchers are not measuring in the current experiment .
The independent variable : The treatment variable that is manipulated by the experimenter .
The dependent variable : The response that the experimenter is measuring .
The control group : This group is made up of individuals who are randomly assigned to a group but donot receive the treatment .The measures takes from the control group are then compared to those in the experimental group to determine if the treatment had an effect .

The experimental group : This group is made up of individuals who are randomly assigned to the group and then receive the treatment . The scores of these participants are compared to those in the control group to determine if the treatment had an effect .
Determining the results of a Simple Experiment
Once the data from the simple experiment has been gathered , researchers then compare the results of the experimental group to those of the control group to determine if the treatment had an effect .How do researchers determine this effect ? Due to the always present possibility of errors , we can never be 100% sure of the relationship between two variables After all , There might always exist some unknown variables that we are unaware of or unable to measure that might nevertheless have an influence over the outcomes .Despite this ever – present problem , there are ways to determine if there most likely is a meaningful relationship.

Experimenters use inferential statistics to determine if the results of experiment are meaningful .Inferential statistics is a branch of science that deals with drawing inferences about a population based upon measures taken from a representative sample of that population .
The key to determining if a treatment had an effect is to measure the statistical significance. Statistical significance shows that the relationship between the variables is probably not due to mere chance and that a real relationship most likely exists between the two variables.
Statistical significance is often represented like this :
P <0.05
A p-value of less than 0.05 indicates if the particular results are due merely to chance , the probability of obtaining these results would be less than 5% Occasionally , smaller P-values are seen such as P<0.01 .There are a number of different means of measuring statistical significance .The type of statistical test used depends largely upon the type of research design that was used .

Case 3
In this case , the moderator is a continuous variable and the independent variable is a dichotomy . For instance , the independent variable might be a rational versus fear – arousing attitude change message and the moderator might be intelligence as measured by an IQ test . The fear-arousing message may be more effective for low – IQ subjects , whereas the rational message may be more effective for high IQ subjects . To measure moderator effects in this case , we must a priority how the effect of the independent variable varies as a function of moderator . It is impossible to evaluate the general hypothesis that the effect of the independent variables changes as a function of the moderator because the moderator has many levels .
Figure 2 presents three idealized ways in which the moderator alters the effect of the independent variable on the dependent variable ., First , the effect of independent variable on the dependent variable changes linearly with respect to the moderator the linear hypothesis represents a gradual , steady change in the effect of the independent variable on the dependent variable as a moderator changes . It is this form of moderation that is generally assumed .the second function in the figure is quadratic function . For instance , the fear-arousing message may be more generally effective than the rational message for all low – IQ-subjects , but as IQ increases , the fear-arousing message loses its advantage and the rational message is more effective .
The third function in Figure 2 is a step function .At some critical IQ level , the rational message becomes more effective than the Fear-arousing message .This pattern is tested by dichotomizing the moderator at the point where step is supposed to occur and proceeding as in case 1 .Unfortunately . Theories in social psychology are usually not precise enough to specify the exact point at which the step in the function occurs.
The linear hypothesis is tested by adding the product of moderator and the dichotomous independent variable to the regression equation , as described by Cohen and Cohen (1983) and cleary and Kessler(1982) , so if the independent variable is noted as X , the moderator as Z , and the dependent variables as Y , Y is regressed on X , Y and XZ . Moderator effects are indicated by the significant effect of XZ while X and Z are controlled The simple effects of the independent variablefor different levels of moderator can be measured and tested by procedures described by Aiken and West (1986) . (Measurement error in the moderator requires the same remedies as measurement errir in the independent variable under case 2 .)
The quadratic moderation effect can be tested by dichotomizing the moderator at the point at which function is presented to accelerate .If the function is quadratic , as in Figure 2 , the effect of independent variable should be greatest for those who are high on the moderator .Alternatively quadratic moderation can be tested by hierarchical regression procedures described by Cohen and Cohen (1983) . Using the same notation as in the previous paragraph , Y is regressed on X , Z ,XZ ,Z² , and XZ² ,the test of quadratic moderation is given by the rest of XZ² . The interpretation of this complicated regression equation can be aided by graphing or tabling the oriented values for various values of X and Z .

Case 4 :-
In this case both the moderator variable and the independent variable are continuous . If one believes that the moderator alters the independent – dependent variable relation in a step function (the bottom diagram in figure 2) , One can dichotomize the moderator at the point where the step takes place . After dichotomizing the moderator , the Pattern becomes Case 2 . The measure if the effect of the independent variable is a regression Co-efficient .
If One presumes that the effect of the independent variable (X) on the dependent variable (Y) varies linearly or quadratically with respect to moderator (Z) , The product variable approach described in Case 3 should be used . For Quadratic moderation , The moderator squared must be introduced .One should consult Cohen and Cohen (1983) and cleary and Kessler (1982) for assistance in setting up the interpreting these regressions .
The presence of measurements error in either the moderator or independent variable under Case 4 greatly complicates the analysis . Busemeyer and Jones (1983) assumed the moderation is linear and so can be captured by an XZ product term . they showed that measuring multiplicative interactions when on of the variables has measurement error results in low power in the test of interactive effect .Methods present y Kenny and Judd (1984) can be used to make adjustments fore measurement error in the variables , resulting in proper estimates of interactive effects , However m These methods require that the variables from which the product variable is formed have normal distributions .

The Nature Mediator Variables
Although the systematic search for moderator variables is relatively recent , psychologists have long recognized the importance of mediating variables . Woodworth`s (1928) S-O-R model which recognizes that an active organism intervenes between stimulus and response , is perhaps the most generic formulation of mediation hypothesis . The central idea in this model is that the effects of stimuli on behavior are mediated by various transformation processes internal to the organism theorists as diverse as Hull , Tolman , and Lewin shared belief in the importance of postulating entities or processes the intervene between input and output . ( Skinn